3 a systematic literature review is

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How to write a systematic literature review [9 steps]

What is a systematic literature review?

Where are systematic literature reviews used, what types of systematic literature reviews are there, how to write a systematic literature review, 1. decide on your team, 2. formulate your question, 3. plan your research protocol, 4. search for the literature, 5. screen the literature, 6. assess the quality of the studies, 7. extract the data, 8. analyze the results, 9. interpret and present the results, registering your systematic literature review, frequently asked questions about writing a systematic literature review, related articles.

A systematic literature review is a summary, analysis, and evaluation of all the existing research on a well-formulated and specific question.

Put simply, a systematic review is a study of studies that is popular in medical and healthcare research. In this guide, we will cover:

• the definition of a systematic literature review
• the purpose of a systematic literature review
• the different types of systematic reviews
• how to write a systematic literature review

➡️ Visit our guide to the best research databases for medicine and health to find resources for your systematic review.

Systematic literature reviews can be utilized in various contexts, but they’re often relied on in clinical or healthcare settings.

Medical professionals read systematic literature reviews to stay up-to-date in their field, and granting agencies sometimes need them to make sure there’s justification for further research in an area. They can even be used as the starting point for developing clinical practice guidelines.

A classic systematic literature review can take different approaches:

• Effectiveness reviews assess the extent to which a medical intervention or therapy achieves its intended effect. They’re the most common type of systematic literature review.
• Diagnostic test accuracy reviews produce a summary of diagnostic test performance so that their accuracy can be determined before use by healthcare professionals.
• Experiential (qualitative) reviews analyze human experiences in a cultural or social context. They can be used to assess the effectiveness of an intervention from a person-centric perspective.
• Costs/economics evaluation reviews look at the cost implications of an intervention or procedure, to assess the resources needed to implement it.
• Etiology/risk reviews usually try to determine to what degree a relationship exists between an exposure and a health outcome. This can be used to better inform healthcare planning and resource allocation.
• Psychometric reviews assess the quality of health measurement tools so that the best instrument can be selected for use.
• Prevalence/incidence reviews measure both the proportion of a population who have a disease, and how often the disease occurs.
• Prognostic reviews examine the course of a disease and its potential outcomes.
• Expert opinion/policy reviews are based around expert narrative or policy. They’re often used to complement, or in the absence of, quantitative data.
• Methodology systematic reviews can be carried out to analyze any methodological issues in the design, conduct, or review of research studies.

Writing a systematic literature review can feel like an overwhelming undertaking. After all, they can often take 6 to 18 months to complete. Below we’ve prepared a step-by-step guide on how to write a systematic literature review.

• Decide on your team.
• Formulate your question.
• Plan your research protocol.
• Search for the literature.
• Screen the literature.
• Assess the quality of the studies.
• Extract the data.
• Analyze the results.
• Interpret and present the results.

When carrying out a systematic literature review, you should employ multiple reviewers in order to minimize bias and strengthen analysis. A minimum of two is a good rule of thumb, with a third to serve as a tiebreaker if needed.

You may also need to team up with a librarian to help with the search, literature screeners, a statistician to analyze the data, and the relevant subject experts.

Define your answerable question. Then ask yourself, “has someone written a systematic literature review on my question already?” If so, yours may not be needed. A librarian can help you answer this.

You should formulate a “well-built clinical question.” This is the process of generating a good search question. To do this, run through PICO:

• Patient or Population or Problem/Disease : who or what is the question about? Are there factors about them (e.g. age, race) that could be relevant to the question you’re trying to answer?
• Intervention : which main intervention or treatment are you considering for assessment?
• Comparison(s) or Control : is there an alternative intervention or treatment you’re considering? Your systematic literature review doesn’t have to contain a comparison, but you’ll want to stipulate at this stage, either way.
• Outcome(s) : what are you trying to measure or achieve? What’s the wider goal for the work you’ll be doing?

Now you need a detailed strategy for how you’re going to search for and evaluate the studies relating to your question.

The protocol for your systematic literature review should include:

• the objectives of your project
• the specific methods and processes that you’ll use
• the eligibility criteria of the individual studies
• how you plan to extract data from individual studies
• which analyses you’re going to carry out

For a full guide on how to systematically develop your protocol, take a look at the PRISMA checklist . PRISMA has been designed primarily to improve the reporting of systematic literature reviews and meta-analyses.

When writing a systematic literature review, your goal is to find all of the relevant studies relating to your question, so you need to search thoroughly .

This is where your librarian will come in handy again. They should be able to help you formulate a detailed search strategy, and point you to all of the best databases for your topic.

➡️ Read more on on how to efficiently search research databases .

The places to consider in your search are electronic scientific databases (the most popular are PubMed , MEDLINE , and Embase ), controlled clinical trial registers, non-English literature, raw data from published trials, references listed in primary sources, and unpublished sources known to experts in the field.

➡️ Take a look at our list of the top academic research databases .

Tip: Don’t miss out on “gray literature.” You’ll improve the reliability of your findings by including it.

Don’t miss out on “gray literature” sources: those sources outside of the usual academic publishing environment. They include:

• non-peer-reviewed journals
• pharmaceutical industry files
• conference proceedings
• pharmaceutical company websites
• internal reports

Gray literature sources are more likely to contain negative conclusions, so you’ll improve the reliability of your findings by including it. You should document details such as:

• The databases you search and which years they cover
• The dates you first run the searches, and when they’re updated
• Which strategies you use, including search terms
• The numbers of results obtained

➡️ Read more about gray literature .

This should be performed by your two reviewers, using the criteria documented in your research protocol. The screening is done in two phases:

• Pre-screening of all titles and abstracts, and selecting those appropriate
• Screening of the full-text articles of the selected studies

Make sure reviewers keep a log of which studies they exclude, with reasons why.

➡️ Visit our guide on what is an abstract?

Your reviewers should evaluate the methodological quality of your chosen full-text articles. Make an assessment checklist that closely aligns with your research protocol, including a consistent scoring system, calculations of the quality of each study, and sensitivity analysis.

The kinds of questions you'll come up with are:

• Were the participants really randomly allocated to their groups?
• Were the groups similar in terms of prognostic factors?
• Could the conclusions of the study have been influenced by bias?

Every step of the data extraction must be documented for transparency and replicability. Create a data extraction form and set your reviewers to work extracting data from the qualified studies.

Here’s a free detailed template for recording data extraction, from Dalhousie University. It should be adapted to your specific question.

Establish a standard measure of outcome which can be applied to each study on the basis of its effect size.

Measures of outcome for studies with:

• Binary outcomes (e.g. cured/not cured) are odds ratio and risk ratio
• Continuous outcomes (e.g. blood pressure) are means, difference in means, and standardized difference in means
• Survival or time-to-event data are hazard ratios

Design a table and populate it with your data results. Draw this out into a forest plot , which provides a simple visual representation of variation between the studies.

Then analyze the data for issues. These can include heterogeneity, which is when studies’ lines within the forest plot don’t overlap with any other studies. Again, record any excluded studies here for reference.

Consider different factors when interpreting your results. These include limitations, strength of evidence, biases, applicability, economic effects, and implications for future practice or research.

Apply appropriate grading of your evidence and consider the strength of your recommendations.

It’s best to formulate a detailed plan for how you’ll present your systematic review results. Take a look at these guidelines for interpreting results from the Cochrane Institute.

Before writing your systematic literature review, you can register it with OSF for additional guidance along the way. You could also register your completed work with PROSPERO .

Systematic literature reviews are often found in clinical or healthcare settings. Medical professionals read systematic literature reviews to stay up-to-date in their field and granting agencies sometimes need them to make sure there’s justification for further research in an area.

The first stage in carrying out a systematic literature review is to put together your team. You should employ multiple reviewers in order to minimize bias and strengthen analysis. A minimum of two is a good rule of thumb, with a third to serve as a tiebreaker if needed.

Your systematic review should include the following details:

A literature review simply provides a summary of the literature available on a topic. A systematic review, on the other hand, is more than just a summary. It also includes an analysis and evaluation of existing research. Put simply, it's a study of studies.

The final stage of conducting a systematic literature review is interpreting and presenting the results. It’s best to formulate a detailed plan for how you’ll present your systematic review results, guidelines can be found for example from the Cochrane institute .

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How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-Analyses, and Meta-Syntheses

Affiliations.

• 1 Behavioural Science Centre, Stirling Management School, University of Stirling, Stirling FK9 4LA, United Kingdom; email: [email protected].
• 2 Department of Psychological and Behavioural Science, London School of Economics and Political Science, London WC2A 2AE, United Kingdom.
• 3 Department of Statistics, Northwestern University, Evanston, Illinois 60208, USA; email: [email protected].
• PMID: 30089228
• DOI: 10.1146/annurev-psych-010418-102803

Systematic reviews are characterized by a methodical and replicable methodology and presentation. They involve a comprehensive search to locate all relevant published and unpublished work on a subject; a systematic integration of search results; and a critique of the extent, nature, and quality of evidence in relation to a particular research question. The best reviews synthesize studies to draw broad theoretical conclusions about what a literature means, linking theory to evidence and evidence to theory. This guide describes how to plan, conduct, organize, and present a systematic review of quantitative (meta-analysis) or qualitative (narrative review, meta-synthesis) information. We outline core standards and principles and describe commonly encountered problems. Although this guide targets psychological scientists, its high level of abstraction makes it potentially relevant to any subject area or discipline. We argue that systematic reviews are a key methodology for clarifying whether and how research findings replicate and for explaining possible inconsistencies, and we call for researchers to conduct systematic reviews to help elucidate whether there is a replication crisis.

Keywords: evidence; guide; meta-analysis; meta-synthesis; narrative; systematic review; theory.

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• Systematic Review | Definition, Example, & Guide

Systematic Review | Definition, Example & Guide

Published on June 15, 2022 by Shaun Turney . Revised on November 20, 2023.

A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer.

They answered the question “What is the effectiveness of probiotics in reducing eczema symptoms and improving quality of life in patients with eczema?”

In this context, a probiotic is a health product that contains live microorganisms and is taken by mouth. Eczema is a common skin condition that causes red, itchy skin.

Table of contents

What is a systematic review, systematic review vs. meta-analysis, systematic review vs. literature review, systematic review vs. scoping review, when to conduct a systematic review, pros and cons of systematic reviews, step-by-step example of a systematic review, other interesting articles, frequently asked questions about systematic reviews.

A review is an overview of the research that’s already been completed on a topic.

What makes a systematic review different from other types of reviews is that the research methods are designed to reduce bias . The methods are repeatable, and the approach is formal and systematic:

• Formulate a research question
• Develop a protocol
• Search for all relevant studies
• Apply the selection criteria
• Extract the data
• Synthesize the data
• Write and publish a report

Although multiple sets of guidelines exist, the Cochrane Handbook for Systematic Reviews is among the most widely used. It provides detailed guidelines on how to complete each step of the systematic review process.

Systematic reviews are most commonly used in medical and public health research, but they can also be found in other disciplines.

Systematic reviews typically answer their research question by synthesizing all available evidence and evaluating the quality of the evidence. Synthesizing means bringing together different information to tell a single, cohesive story. The synthesis can be narrative ( qualitative ), quantitative , or both.

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Systematic reviews often quantitatively synthesize the evidence using a meta-analysis . A meta-analysis is a statistical analysis, not a type of review.

A meta-analysis is a technique to synthesize results from multiple studies. It’s a statistical analysis that combines the results of two or more studies, usually to estimate an effect size .

A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarize and evaluate previous work, without using a formal, explicit method.

Although literature reviews are often less time-consuming and can be insightful or helpful, they have a higher risk of bias and are less transparent than systematic reviews.

Similar to a systematic review, a scoping review is a type of review that tries to minimize bias by using transparent and repeatable methods.

However, a scoping review isn’t a type of systematic review. The most important difference is the goal: rather than answering a specific question, a scoping review explores a topic. The researcher tries to identify the main concepts, theories, and evidence, as well as gaps in the current research.

Sometimes scoping reviews are an exploratory preparation step for a systematic review, and sometimes they are a standalone project.

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A systematic review is a good choice of review if you want to answer a question about the effectiveness of an intervention , such as a medical treatment.

To conduct a systematic review, you’ll need the following:

• A precise question , usually about the effectiveness of an intervention. The question needs to be about a topic that’s previously been studied by multiple researchers. If there’s no previous research, there’s nothing to review.
• If you’re doing a systematic review on your own (e.g., for a research paper or thesis ), you should take appropriate measures to ensure the validity and reliability of your research.
• Access to databases and journal archives. Often, your educational institution provides you with access.
• Time. A professional systematic review is a time-consuming process: it will take the lead author about six months of full-time work. If you’re a student, you should narrow the scope of your systematic review and stick to a tight schedule.
• Bibliographic, word-processing, spreadsheet, and statistical software . For example, you could use EndNote, Microsoft Word, Excel, and SPSS.

A systematic review has many pros .

• They minimize research bias by considering all available evidence and evaluating each study for bias.
• Their methods are transparent , so they can be scrutinized by others.
• They’re thorough : they summarize all available evidence.
• They can be replicated and updated by others.

Systematic reviews also have a few cons .

• They’re time-consuming .
• They’re narrow in scope : they only answer the precise research question.

The 7 steps for conducting a systematic review are explained with an example.

Step 1: Formulate a research question

Formulating the research question is probably the most important step of a systematic review. A clear research question will:

• Allow you to more effectively communicate your research to other researchers and practitioners
• Guide your decisions as you plan and conduct your systematic review

A good research question for a systematic review has four components, which you can remember with the acronym PICO :

• Population(s) or problem(s)
• Intervention(s)
• Comparison(s)

You can rearrange these four components to write your research question:

• What is the effectiveness of I versus C for O in P ?

Sometimes, you may want to include a fifth component, the type of study design . In this case, the acronym is PICOT .

• Type of study design(s)
• The population of patients with eczema
• The intervention of probiotics
• In comparison to no treatment, placebo , or non-probiotic treatment
• The outcome of changes in participant-, parent-, and doctor-rated symptoms of eczema and quality of life
• Randomized control trials, a type of study design

Their research question was:

• What is the effectiveness of probiotics versus no treatment, a placebo, or a non-probiotic treatment for reducing eczema symptoms and improving quality of life in patients with eczema?

Step 2: Develop a protocol

A protocol is a document that contains your research plan for the systematic review. This is an important step because having a plan allows you to work more efficiently and reduces bias.

Your protocol should include the following components:

• Background information : Provide the context of the research question, including why it’s important.
• Research objective (s) : Rephrase your research question as an objective.
• Selection criteria: State how you’ll decide which studies to include or exclude from your review.
• Search strategy: Discuss your plan for finding studies.
• Analysis: Explain what information you’ll collect from the studies and how you’ll synthesize the data.

If you’re a professional seeking to publish your review, it’s a good idea to bring together an advisory committee . This is a group of about six people who have experience in the topic you’re researching. They can help you make decisions about your protocol.

It’s highly recommended to register your protocol. Registering your protocol means submitting it to a database such as PROSPERO or ClinicalTrials.gov .

Step 3: Search for all relevant studies

Searching for relevant studies is the most time-consuming step of a systematic review.

To reduce bias, it’s important to search for relevant studies very thoroughly. Your strategy will depend on your field and your research question, but sources generally fall into these four categories:

• Databases: Search multiple databases of peer-reviewed literature, such as PubMed or Scopus . Think carefully about how to phrase your search terms and include multiple synonyms of each word. Use Boolean operators if relevant.
• Handsearching: In addition to searching the primary sources using databases, you’ll also need to search manually. One strategy is to scan relevant journals or conference proceedings. Another strategy is to scan the reference lists of relevant studies.
• Gray literature: Gray literature includes documents produced by governments, universities, and other institutions that aren’t published by traditional publishers. Graduate student theses are an important type of gray literature, which you can search using the Networked Digital Library of Theses and Dissertations (NDLTD) . In medicine, clinical trial registries are another important type of gray literature.
• Experts: Contact experts in the field to ask if they have unpublished studies that should be included in your review.

At this stage of your review, you won’t read the articles yet. Simply save any potentially relevant citations using bibliographic software, such as Scribbr’s APA or MLA Generator .

• Databases: EMBASE, PsycINFO, AMED, LILACS, and ISI Web of Science
• Handsearch: Conference proceedings and reference lists of articles
• Gray literature: The Cochrane Library, the metaRegister of Controlled Trials, and the Ongoing Skin Trials Register
• Experts: Authors of unpublished registered trials, pharmaceutical companies, and manufacturers of probiotics

Step 4: Apply the selection criteria

Applying the selection criteria is a three-person job. Two of you will independently read the studies and decide which to include in your review based on the selection criteria you established in your protocol . The third person’s job is to break any ties.

To increase inter-rater reliability , ensure that everyone thoroughly understands the selection criteria before you begin.

If you’re writing a systematic review as a student for an assignment, you might not have a team. In this case, you’ll have to apply the selection criteria on your own; you can mention this as a limitation in your paper’s discussion.

You should apply the selection criteria in two phases:

• Based on the titles and abstracts : Decide whether each article potentially meets the selection criteria based on the information provided in the abstracts.
• Based on the full texts: Download the articles that weren’t excluded during the first phase. If an article isn’t available online or through your library, you may need to contact the authors to ask for a copy. Read the articles and decide which articles meet the selection criteria.

It’s very important to keep a meticulous record of why you included or excluded each article. When the selection process is complete, you can summarize what you did using a PRISMA flow diagram .

Next, Boyle and colleagues found the full texts for each of the remaining studies. Boyle and Tang read through the articles to decide if any more studies needed to be excluded based on the selection criteria.

When Boyle and Tang disagreed about whether a study should be excluded, they discussed it with Varigos until the three researchers came to an agreement.

Step 5: Extract the data

Extracting the data means collecting information from the selected studies in a systematic way. There are two types of information you need to collect from each study:

• Information about the study’s methods and results . The exact information will depend on your research question, but it might include the year, study design , sample size, context, research findings , and conclusions. If any data are missing, you’ll need to contact the study’s authors.
• Your judgment of the quality of the evidence, including risk of bias .

You should collect this information using forms. You can find sample forms in The Registry of Methods and Tools for Evidence-Informed Decision Making and the Grading of Recommendations, Assessment, Development and Evaluations Working Group .

Extracting the data is also a three-person job. Two people should do this step independently, and the third person will resolve any disagreements.

They also collected data about possible sources of bias, such as how the study participants were randomized into the control and treatment groups.

Step 6: Synthesize the data

Synthesizing the data means bringing together the information you collected into a single, cohesive story. There are two main approaches to synthesizing the data:

• Narrative ( qualitative ): Summarize the information in words. You’ll need to discuss the studies and assess their overall quality.
• Quantitative : Use statistical methods to summarize and compare data from different studies. The most common quantitative approach is a meta-analysis , which allows you to combine results from multiple studies into a summary result.

Generally, you should use both approaches together whenever possible. If you don’t have enough data, or the data from different studies aren’t comparable, then you can take just a narrative approach. However, you should justify why a quantitative approach wasn’t possible.

Boyle and colleagues also divided the studies into subgroups, such as studies about babies, children, and adults, and analyzed the effect sizes within each group.

Step 7: Write and publish a report

The purpose of writing a systematic review article is to share the answer to your research question and explain how you arrived at this answer.

Your article should include the following sections:

• Abstract : A summary of the review
• Introduction : Including the rationale and objectives
• Methods : Including the selection criteria, search method, data extraction method, and synthesis method
• Results : Including results of the search and selection process, study characteristics, risk of bias in the studies, and synthesis results
• Discussion : Including interpretation of the results and limitations of the review
• Conclusion : The answer to your research question and implications for practice, policy, or research

To verify that your report includes everything it needs, you can use the PRISMA checklist .

Once your report is written, you can publish it in a systematic review database, such as the Cochrane Database of Systematic Reviews , and/or in a peer-reviewed journal.

In their report, Boyle and colleagues concluded that probiotics cannot be recommended for reducing eczema symptoms or improving quality of life in patients with eczema. Note Generative AI tools like ChatGPT can be useful at various stages of the writing and research process and can help you to write your systematic review. However, we strongly advise against trying to pass AI-generated text off as your own work.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

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A systematic review is a literature review that gathers all of the available evidence matching pre-specified eligibility criteria to answer a specific research question. It uses explicit, systematic methods, documented in a protocol, to minimize bias , provide reliable findings , and inform decision-making.  ¹  

There are many types of literature reviews.

Before beginning a systematic review, consider whether it is the best type of review for your question, goals, and resources. The table below compares a few different types of reviews to help you decide which is best for you. 

• Scoping Review Guide For more information about scoping reviews, refer to the UNC HSL Scoping Review Guide.

• UNC HSL's Simplified, Step-by-Step Process Map A PDF file of the HSL's Systematic Review Process Map.
• Text-Only: UNC HSL's Systematic Reviews - A Simplified, Step-by-Step Process A text-only PDF file of HSL's Systematic Review Process Map.

The average systematic review takes 1,168 hours to complete. ¹   A librarian can help you speed up the process.

Systematic reviews follow established guidelines and best practices to produce high-quality research. Librarian involvement in systematic reviews is based on two levels. In Tier 1, your research team can consult with the librarian as needed. The librarian will answer questions and give you recommendations for tools to use. In Tier 2, the librarian will be an active member of your research team and co-author on your review. Roles and expectations of librarians vary based on the level of involvement desired. Examples of these differences are outlined in the table below.

• Request a systematic or scoping review consultation

The following are systematic and scoping reviews co-authored by HSL librarians.

Only the most recent 15 results are listed. Click the website link at the bottom of the list to see all reviews co-authored by HSL librarians in PubMed

Researchers conduct systematic reviews in a variety of disciplines.  If your focus is on a topic outside of the health sciences, you may want to also consult the resources below to learn how systematic reviews may vary in your field.  You can also contact a librarian for your discipline with questions.

• EPPI-Centre methods for conducting systematic reviews The EPPI-Centre develops methods and tools for conducting systematic reviews, including reviews for education, public and social policy.

Environmental Topics

• Collaboration for Environmental Evidence (CEE) CEE seeks to promote and deliver evidence syntheses on issues of greatest concern to environmental policy and practice as a public service

Social Sciences

• Siddaway AP, Wood AM, Hedges LV. How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-Analyses, and Meta-Syntheses. Annu Rev Psychol. 2019 Jan 4;70:747-770. doi: 10.1146/annurev-psych-010418-102803. A resource for psychology systematic reviews, which also covers qualitative meta-syntheses or meta-ethnographies
• The Campbell Collaboration

Social Work

Software engineering

• Guidelines for Performing Systematic Literature Reviews in Software Engineering The objective of this report is to propose comprehensive guidelines for systematic literature reviews appropriate for software engineering researchers, including PhD students.

Sport, Exercise, & Nutrition

• Application of systematic review methodology to the field of nutrition by Tufts Evidence-based Practice Center Publication Date: 2009
• Systematic Reviews and Meta-Analysis — Open & Free (Open Learning Initiative) The course follows guidelines and standards developed by the Campbell Collaboration, based on empirical evidence about how to produce the most comprehensive and accurate reviews of research

• Systematic Reviews by David Gough, Sandy Oliver & James Thomas Publication Date: 2020

Updating reviews

• Updating systematic reviews by University of Ottawa Evidence-based Practice Center Publication Date: 2007
• Next: Step 1: Complete Pre-Review Tasks >>
• Last Updated: Jul 15, 2024 4:55 PM
• URL: https://guides.lib.unc.edu/systematic-reviews

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Systematic Reviews

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What Makes a Systematic Review Different from Other Types of Reviews?

• Planning Your Systematic Review
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Reproduced from Grant, M. J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91–108. doi:10.1111/j.1471-1842.2009.00848.x

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Systematic Literature Review or Literature Review?

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Table of Contents

As a researcher, you may be required to conduct a literature review. But what kind of review do you need to complete? Is it a systematic literature review or a standard literature review? In this article, we’ll outline the purpose of a systematic literature review, the difference between literature review and systematic review, and other important aspects of systematic literature reviews.

What is a Systematic Literature Review?

The purpose of systematic literature reviews is simple. Essentially, it is to provide a high-level of a particular research question. This question, in and of itself, is highly focused to match the review of the literature related to the topic at hand. For example, a focused question related to medical or clinical outcomes.

The components of a systematic literature review are quite different from the standard literature review research theses that most of us are used to (more on this below). And because of the specificity of the research question, typically a systematic literature review involves more than one primary author. There’s more work related to a systematic literature review, so it makes sense to divide the work among two or three (or even more) researchers.

Your systematic literature review will follow very clear and defined protocols that are decided on prior to any review. This involves extensive planning, and a deliberately designed search strategy that is in tune with the specific research question. Every aspect of a systematic literature review, including the research protocols, which databases are used, and dates of each search, must be transparent so that other researchers can be assured that the systematic literature review is comprehensive and focused.

Most systematic literature reviews originated in the world of medicine science. Now, they also include any evidence-based research questions. In addition to the focus and transparency of these types of reviews, additional aspects of a quality systematic literature review includes:

• Clear and concise review and summary
• Comprehensive coverage of the topic
• Accessibility and equality of the research reviewed

Systematic Review vs Literature Review

The difference between literature review and systematic review comes back to the initial research question. Whereas the systematic review is very specific and focused, the standard literature review is much more general. The components of a literature review, for example, are similar to any other research paper. That is, it includes an introduction, description of the methods used, a discussion and conclusion, as well as a reference list or bibliography.

A systematic review, however, includes entirely different components that reflect the specificity of its research question, and the requirement for transparency and inclusion. For instance, the systematic review will include:

• Eligibility criteria for included research
• A description of the systematic research search strategy
• An assessment of the validity of reviewed research
• Interpretations of the results of research included in the review

As you can see, contrary to the general overview or summary of a topic, the systematic literature review includes much more detail and work to compile than a standard literature review. Indeed, it can take years to conduct and write a systematic literature review. But the information that practitioners and other researchers can glean from a systematic literature review is, by its very nature, exceptionally valuable.

This is not to diminish the value of the standard literature review. The importance of literature reviews in research writing is discussed in this article . It’s just that the two types of research reviews answer different questions, and, therefore, have different purposes and roles in the world of research and evidence-based writing.

Systematic Literature Review vs Meta Analysis

It would be understandable to think that a systematic literature review is similar to a meta analysis. But, whereas a systematic review can include several research studies to answer a specific question, typically a meta analysis includes a comparison of different studies to suss out any inconsistencies or discrepancies. For more about this topic, check out Systematic Review VS Meta-Analysis article.

Language Editing Plus

With Elsevier’s Language Editing Plus services , you can relax with our complete language review of your systematic literature review or literature review, or any other type of manuscript or scientific presentation. Our editors are PhD or PhD candidates, who are native-English speakers. Language Editing Plus includes checking the logic and flow of your manuscript, reference checks, formatting in accordance to your chosen journal and even a custom cover letter. Our most comprehensive editing package, Language Editing Plus also includes any English-editing needs for up to 180 days.

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Systematic Reviews

Describes what is involved with conducting a systematic review of the literature for evidence-based public health and how the librarian is a partner in the process.

Several CDC librarians have special training in conducting literature searches for systematic reviews.  Literature searches for systematic reviews can take a few weeks to several months from planning to delivery.

Fill out a search request form here  or contact the Stephen B. Thacker CDC Library by email  [email protected] or telephone 404-639-1717.

Campbell Collaboration

Cochrane Collaboration

Eppi Centre

Joanna Briggs Institute

McMaster University

PRISMA Statement

Systematic Reviews – CRD’s Guide

Systematic Reviews of Health Promotion and Public Health Interventions

The Guide to Community Preventive Services

Look for systematic reviews that have already been published. 

• To ensure that the work has not already been done.
• To provides examples of search strategies for your topic

Look in PROSPERO for registered systematic reviews.

Search Cochrane and CRD-York for systematic reviews.

Search filter for finding systematic reviews in PubMed

Other search filters to locate systematic reviews

A systematic review attempts to collect and analyze all evidence that answers a specific question.  The question must be clearly defined and have inclusion and exclusion criteria. A broad and thorough search of the literature is performed and a critical analysis of the search results is reported and ultimately provides a current evidence-based answer  to the specific question.

Time:  According to Cochrane , it takes 18 months on average to complete a Systematic Review.

The average systematic review from beginning to end requires 18 months of work. “…to find out about a healthcare intervention it is worth searching research literature thoroughly to see if the answer is already known. This may require considerable work over many months…” ( Cochrane Collaboration )

Review Team: Team Members at minimum…

• Content expert
• 2 reviewers
• 1 tie breaker
• 1 statistician (meta-analysis)
• 1 economist if conducting an economic analysis
• *1 librarian (expert searcher) trained in systematic reviews

“Expert searchers are an important part of the systematic review team, crucial throughout the review process-from the development of the proposal and research question to publication.” ( McGowan & Sampson, 2005 )

*Ask your librarian to write a methods section regarding the search methods and to give them co-authorship. You may also want to consider providing a copy of one or all of the search strategies used in an appendix.

The Question to Be Answered: A clearly defined and specific question or questions with inclusion and exclusion criteria.

Written Protocol: Outline the study method, rationale, key questions, inclusion and exclusion criteria, literature searches, data abstraction and data management, analysis of quality of the individual studies, synthesis of data, and grading of the evidience for each key question.

Literature Searches:  Search for any systematic reviews that may already answer the key question(s).  Next, choose appropriate databases and conduct very broad, comprehensive searches.  Search strategies must be documented so that they can be duplicated.  The librarian is integral to this step of the process. Before your librarian creates a search strategy and starts searching in earnest you should write a detailed PICO question , determine the inclusion and exclusion criteria for your study, run a preliminary search, and have 2-4 articles that already fit the criteria for your review.

What is searched depends on the topic of the review but should include…

• At least 3 standard medical databases like PubMed/Medline, CINAHL, Embase, etc..
• At least 2 grey literature resources like Clinicaltrials.gov, COS Conference Papers Index, Grey Literature Report,  etc…

Citation Management: EndNote is a bibliographic management tools that assist researchers in managing citations.  The Stephen B. Thacker CDC Library oversees the site license for EndNote.

To request installation:   The library provides EndNote  to CDC staff under a site-wide license. Please use the ITSO Software Request Tool (SRT) and submit a request for the latest version (or upgraded version) of EndNote. Please be sure to include the computer name for the workstation where you would like to have the software installed.

EndNote Training:   CDC Library offers training on EndNote on a regular basis – both a basic and advanced course. To view the course descriptions and upcoming training dates, please visit the CDC Library training page .

For assistance with EndNote software, please contact [email protected]

Vendor Support and Services:   EndNote – Support and Services (Thomson Reuters)  EndNote – Tutorials and Live Online Classes (Thomson Reuters)

Getting Articles:

Articles can be obtained using DocExpress or by searching the electronic journals at the Stephen B. Thacker CDC Library.

IOM Standards for Systematic Reviews: Standard 3.1: Conduct a comprehensive systematic search for evidence

The goal of a systematic review search is to maximize recall and precision while keeping results manageable. Recall (sensitivity) is defined as the number of relevant reports identified divided by the total number of relevant reports in existence. Precision (specificity) is defined as the number of relevant reports identified divided by the total number of reports identified.

Issues to consider when creating a systematic review search:   

• All concepts are included in the strategy
• All appropriate subject headings are used
• Appropriate use of explosion
• Appropriate use of subheadings and floating subheadings
• Use of natural language (text words) in addition to controlled vocabulary terms
• Use of appropriate synonyms, acronyms, etc.
• Truncation and spelling variation as appropriate
• Appropriate use of limits such as language, years, etc.
• Field searching, publication type, author, etc.
• Boolean operators used appropriately
• Line errors: when searches are combined using line numbers, be sure the numbers refer to the searches intended
• Check indexing of relevant articles
• Search strategy adapted as needed for multiple databases
• Cochrane Handbook: Searching for Studies See Part 2, Chapter 6

A step-by-step guide to systematically identify all relevant animal studies

Materials listed in these guides are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings. HHS, PHS, and CDC assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by HHS, PHS, and CDC. Opinion, findings, and conclusions expressed by the original authors of items included in these materials, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of HHS, PHS, or CDC. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by HHS, PHS, or CDC.

A systematic literature review of education for Generation Alpha

• Open access
• Published: 15 August 2024
• Volume 3 , article number  125 , ( 2024 )

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You have full access to this open access article

• Alena Höfrová 1 , 2 ,
• Venera Balidemaj 1 &
• Mark A. Small 1  

Generation Alpha are the first to grow up immersed in digital technology and presumed to be wired differently than previous generations. This systematic review synthesizes the research literature on what has been learned so far and broadly answers the following question: What is happening in the education and training of Generation Alpha? The literature review was conducted based on guidelines outlined by The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Titles of 2,093 studies, abstracts of 603 studies, and 335 full-text studies were evaluated for inclusion criteria. A total of 83 studies were included into the literature review. The studies were sorted into four major categories: (1) the role of teachers, (2) the role of new approaches to education, (3) the role of teaching tools, and (4) the role of blended/online learning. Despite frequent use of the term “Generation Alpha” in the research literature, relatively few studies report generational differences that reveal how children of this generation are characteristically different from previous generations. There is simply a strong assumption that Generation Alpha is different. A major concern is that the use of technology by Generation Alpha has decreased opportunities for social-emotional development and increased mental health problems. There are digital educational tools and online strategies being developed and tested but none have emerged to be dominant.

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1 A systematic literature review of education for generation alpha

Generation Alpha has received a lot of attention by scholars seeking to understand how current advances in technology may impact their learning. The underlying assumptions are that Generation Alpha students differ qualitatively from students from other generations and that there should be corresponding changes to education based on these differences. To date, these assumptions have not been systemically examined, though there have been reviews in related areas (e.g., [ 39 ]). Accordingly, a systematic review of education literature is necessary to discover whether and how Generation Alpha should be considered by educators. The significance of this review is to guide future educational efforts designed to target specific cohorts like Generation Alpha.

The term “generation” can be used to classify people based on year of birth, age, location, similar values, and/or important events and usually spans about 20 years [ 47 ]. The first use of the term “Alpha Generation” is credited to Mark McCrindle who in 2005 coined the term to describe the cohort following Generation Z [ 103 ]. While there is general agreement that the Millennial Generation are classified as those born between 1980–1994, and GenZ/iGen are classified as those born between 1995–2012, there are some differences in the literature identifying the starting date for Generation Alpha. This report follows most of the literature which uses 2010 as the starting date []. Table 1 presents different generations and significant technical milestones.

Generation Alpha and those immediately preceding generations could all be understood to be digital generations with the only difference being the quantity and quality of digital opportunities that were available while growing up. All future generations will be considered true digital natives with Generation Alpha simply being the first to be so immersed in digital technology. For comparison, members of Generation Alpha are unlikely to carry a wallet or take a written exam [ 76 ]. When all members of this generation have been born, they are expected to number almost two billion [ 99 ].

Despite their large numbers, research on characteristics of Generation Alpha is limited. The assumption that Generation Alpha is qualitatively different than Generation Z is largely untested (Nagy & Kolcsey, [ 81 ]) and sometimes disputed [ 59 ]. There are very few direct comparisons between generations measuring the nature and extent of digital fluency or competence. Perhaps the only certainty is that for this generation, the everyday role of digital devices is not perceived as a “tool” or “instrument” to augment life, but as a normative and necessary means to interact with the world. This chief characteristic has important developmental implications, most notably in the construction of identity and social-emotional learning (SEL). Still, there are some research findings that suggest that Generation Alpha can be distinguished from previous generations.

2 Personal characteristics

In a rare study comparing generational differences, Apaydin and Kaya [ 14 ] identified characteristics of Generation Alpha from the perspective of pre-school teachers. Using a qualitative design with a small sample (n = 12) they found Generation Alpha to:

Exhibit behaviors such as being more curious, free from any rules, being more ill-tempered, more mobile and more self-centered than Generation Z; moreover, they also had high self-esteem, and they were more emotional and more conscious. In terms of communication, Generation Alpha was also determined to be more closed and behave more individually than Generation Z. Considering classroom management techniques, preschool teachers were found to use the reconstructive approach for the alpha generation and traditional classroom management techniques for Generation Z. (p. 123)

The above quote from the study by Apaydin and Kaya [ 14 ] is frequently cited in the Generation Alpha literature and the basis for most of the generation’s characteristic assumptions. dos Reis [ 33 ] found similar findings of cognitive flexibility and dynamism and inferred that Generation Alpha will be employed in jobs characterized by decision-making autonomy. This may lead to Generation Alpha being more entrepreneurial (Ziatdinov & Cillers, 2021). Similarly, Selvi et al. [ 100 ] notes Generation Alpha to lack qualities such as “loyalty, thoughtfulness, compassion, open-mindedness, and responsibility” (p. 273).

3 Family dynamics

Few researchers have examined how family dynamics such as family structure and roles of family members interact with Generation Alpha learning. For example, the research looking at family dynamics is almost exclusively concerned with marketing. The marketing industry is especially interested in how Generation Alpha may exert more influence on parental buying decisions because of increased media exposure [ 45 , 63 , 89 , 109 ]). In one study of 206 parents in India, the critical factors in the selection of educational toys for Generation Alpha were found to be brand recognition, brand attributes (e.g., safety) and product appeal [ 92 ].

4 Social media

The use of social media through mobile devices is a chief characteristic of Generation Alpha. The continuous rise in mobile internet use by Generation Alpha is blurring traditional boundaries between news, information, entertainment, socializing and research. Over 80% of parents of Generation Alpha say their children watch videos or play games on a mobile device daily [ 24 ] and on average spend 7–8 h on screen [ 111 ]. As early as kindergarten, children’s individual consumption of digitally streamed movies drives their classroom social interactions (Kaplan-Berkeley, [ 54 ]).

There are ongoing concerns that the rise in interpersonal communication through text will result in a loss of oral communication skills and that a reliance upon social media influencers to learn about current events will result in less critical thinking. Although there is much written about the potential and real harms of social media, there is little research from which to speculate how the impact on Generation Alpha will be different [ 38 ].

5 Social emotional development

The increased use of technology has resulted in a decline in opportunities for social-emotional development. Moreover, the increased use of social media has led to an increase in mental health problems as children who spend more time on screens experience more mental health challenges [ 112 ]. The potential good news is that because Generation Alpha are children born to late Millennials or members of Generation Z, these parents often spend more time and are more engaged with their children’s lives [ 26 ], 32 ). Thus, parents and other adults may be able to mediate harmful effects of social media use. In a rare study of adult–child interaction with 100 parents and children, Mariati et al. [ 73 ] found that “When social media and online games are introduced into a child’s environment, it has been demonstrated that they mediate their conceptualization of learning and cognitive development,... through the interactions between teachers, children, and technology, children conceptualize higher mental functions such as continuous and ongoing problem-solving dispositions, as well as language acquisition and social learning” (p. 95).

More research is needed to understand the optimal conditions to provide social-emotional learning opportunities with parents and teachers. Settings are also important. Schools also might be designed with embedded instruction of non-cognitive skills and opportunities for interpersonal skill development [ 67 ]. Not only schools, but also informal educational settings such as afterschool programs can be reimagined to provide more “edutainment” for Generation Alpha to increase social-emotional development [ 94 ].

6 Worldwide concern

There is widespread concern for the social-emotional and mental health of Generation Alpha and role of teachers and parents. In Slovakia, there is concern for the lack of emotional intelligence in Generation Alpha [ 53 ]. In Romania, based on the results of a previous investigation carried out in the same locations during the period of 2015–2016, exploratory qualitative research concluded that young children in Romania have a low level of digital literacy due to their parents’ and educators’ lack of technology knowledge and skills. Additionally, issues like online privacy and security are rarely of adults’ concern: parents worry more about their children’s eyesight and social isolation (Bako & Tokes, 2018).

In Indonesia, Zulkifli et al. [ 119 ] note “The results of the study [from 25 kindergarten principals] indicate that the role of preschools in the use of gadgets in digital native generation children in Pekanbaru City is included in the low category. Only a few preschools have organized parenting education for parents. There are almost no rules governing children's use of gadgets at home, and few preschools educate children on how to use gadgets properly. It is expected for teachers and preschools to add special programs in the curriculum to provide information about positive gadget use and parenting programs that discuss digital native generation and collaborate with parents to establish rules such as frequency, duration and content of children using gadgets” (p. 1).

In Malaysia, Fadzil et al., [ 35 ] concluded “This study showed that more than half of the respondents (parents and kids) surveyed felt very dependent upon gadgets. Parents need them as kids control, while kids need them for their pleasure and entertainment tools. They feeling the need to have their phones on them 24 h or using their phones every day. This will have caused them to feel anxious, disconnected, or even upset if they did not use and utilize it in their future and daily live” (p. 621). Finally, in Russia, [ 16 ] found that preschool children with prolonged immersion “in virtual leisure and limited social contacts with other people contribute to a decrease in the level of self-esteem and increase in the level of anxiety and social distancing from parents” (p. 11).

This review synthesizes the research literature on what has been learned so far and seeks to accomplish the following goals: (1) Identify the roles of teachers in the education of Generation Alpha; (2) Identify novel educational strategies in the teaching of Generation Alpha; and (3) Identify the roles blended or hybrid learning played in the education of Generation Alpha. There is a growing body of literature focused on answering the question: What is known about learning practices of Generation Alpha?

The systematic review of the Generation Alpha literature was conducted based on guidelines outlined by The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, updated in November 2021 [ 85 ]. Figure  1 illustrates the PRISMA review.

Systematic literature review

In November 2022, searches were conducted using the following databases and search engines: ERIC, APA PsycArticles, APA PsycInfo, ProQuest One Academic, Google Scholar, and Clemson Library (a search engine that includes Scopus and 724 other databases). These databases were selected as they include research focused on education, psychology, and other related areas commonly used in the field of education. The databases covered peer-reviewed articles as well as dissertations. Table 2 illustrates the detailed search strategy for this literature review and provides information about the specific search terms used in the specific databases with numbers of searched returned publications. No filters were used, nor were there language or date restrictions while searching the literature.

The literature search identified a total of 3,067 studies. We retrieved all the records and exported them as a Research Information Systems (RIS) file into Mendeley Reference Manager, where duplicate records were identified and removed from the searches [ 65 ]. Subsequently, the records were reviewed, and 974 duplicates were identified. After deleting the duplicates, we exported 2093 records into an Excel document for further review and coding of titles, abstracts, and full texts.

In the first stage of screening, the titles of 2093 records were evaluated to identify the relevant literature and 1,490 studies were excluded. Studies that were older than 2011 were excluded as not applicable to Generation Alpha (children born from 2010 to 2024). Exclusion criteria were applied to titles that clearly indicated topics unrelated to education of Generation Alpha. Excluded studies: (1) were in a language other than English; (2) focused on disciplines unrelated to education (e.g., finances, marketing, medicine); (3) focused on different generations (e.g., Gen X); (4) focused on school leadership; (5) focused on religious education or Sunday church schools; or (6) focused on homeschool education or family education. While topics such as school leadership and religious education other than formal education may seem relevant, the reviewed titles did not indicate any direct connection to education of Generation Alpha (e.g., they were models of religious education or leadership development and styles).

Titles that were included focused on: (1) education; (2) digital education; (3) digital games in education; (4) virtual reality in education; (5) technology use in education; (6) social media use in education; (7) language development; (8) employment skills; (9) generational differences in the workplace; and (10) labor market trends. Studies with titles that were not descriptive enough to apply the inclusion or exclusion criteria were also included for further review.

In the second stage of screening, the abstracts of 603 records were read to identify the relevant literature and 268 studies were eliminated. Because many titles of papers were not specific, an overabundance of abstracts were read to determine if they applied to Generation Alpha. Publications without abstracts were also included in the full text review to ensure that all essential publications for our review were included. After further review, abstracts were eliminated because they focused on: (1) different generations or different age groups; (2) different disciplines (e.g., medicine, investments, economics, technology outside education); (3) different types of education (e.g., character education, citizenship education, religious education); or (4) soft skills. Abstracts that were included focused on: (1) characteristics of Generation Alpha; (2) curriculum development; and (3) technology and specific teaching tools usage in education.

In the third stage of screening, the full text of 335 records were reviewed to assess them for eligibility. From this stage of review, 84 studies were excluded because: (1) the focus was on different generations or different age groups; (2) the focus was from different disciplines (e.g., investments, architecture, marketing); (3) the language of the publication was other than English; and (4) the publication was not available or was available only after purchase. During this stage, we sorted the studies by the country of origin, type of publication, original research, methods/samples, focal point of the paper (e.g., students, teachers, parents).

In the final stage of screening, the full text of 251 publications were extensively reviewed and 168 studies were excluded from the literature review. The publications were excluded due to the following: (1) lack of empirical research, such as being theoretical or without a data-driven analysis; (2) insufficient sections of the publications, such as studies that lacked clear analysis, had insufficiently detailed data collection description, or had an unclear method section that did not distinguished between empirical research and a literature review; (3) had a focus on different generations; (4) had a focus on non-formal education (e.g., out of school educational settings).

The systematic review concluded with 83 relevant publications that could be analyzed and coded for the literature review. We utilized an indictive coding approach, when the codes, categories, and themes naturally emerged from reading and analyzing the articles. The coding process was performed continuously, starting from the title screening stage and repeated throughout the abstract and the full text review phases. Codes were revisited and refined at each step of the literature review to ensure that the thematic structure accurately represented the data. The result of this coding process, including codes, categories, and themes, are summarized in Table  3 . Four major themes concerning the education of Generation Alpha emerged from the coding process: (1) the role of teachers (18 studies), (2) the role of new approaches to education (12 studies), (3) the role of teaching tools (43 studies), and (4) the role of blended/online learning (10 studies).

Publications came from around the world, with a majority from Indonesia (33 studies), Malaysia (17 studies), followed by the United States (11 studies). Figure  2 . shows the distribution of publications by country, darker color indicating more studies.

Distribution of publications by country

The four themes derive from research that was conducted from around the world and describe common points of emphasis to accomplish the following goals: (1) Identify the roles of teachers in the education of Generation Alpha; (2) Identify novel educational strategies in the teaching of Generation Alpha; and (3) Identify the roles blended or hybrid learning played in the education of Generation Alpha.

9 The role of teachers

If Generation Alpha is special, then the expectation is that teachers would be the first to be impacted by the need for innovative teaching strategies. A synthesis of the 18 studies that focused on teachers confirms the gradual impact of an increased technology expertise needed to teach Generation Alpha. Perhaps unsurprisingly, the research tells a story of how teachers struggle to keep up with technology, recognize the importance of staying updated and innovate to teach Generation Alpha. To tell this story, seven publications were from Asia, five publications were from Europe, three from the United States, two from the Middle East, and one from Brazil.

9.1 Teachers struggle

For teachers (and most others), the speed of technological advances generally outpaces the ability to stay current on the latest educational innovations. The gap between teachers’ and students’ digital competence is dependent upon both teacher training and teacher commitment to staying updated. Regarding teacher training, a study conducted by Galindo-Domínguez and Bezanilla [ 40 ] showed only a medium level of digital competence among 200 future teachers enrolled in educational degrees in universities in Spain. Once in the classroom, whether or not teachers use social media may depend on attitudes toward its usefulness. In a dissertation, Turnbull [ 110 ] explored reasons for the low integration of social media into higher education classroom assignments in the United States. Professors who integrated social media into assignments believed that social media is an important part of students’ present lives and future employability. Professors who did not integrate social media into assignments believed that social media is not relevant to their class and not useful for learning. These professors were also older and unfamiliar with social media. Similarly, Adnan et al. [ 5 ] investigated teachers’ content development utilizing innovative teaching and learning technologies among tertiary teachers in Malaysia. The results showed that after training, very few teachers created interactive learning materials (e.g., virtual reality) on their own. The results affirm the necessity of offering opportunities for teachers to master new digital technologies throughout their careers.

9.2 The need for training for teachers

In educational institutions around the world, there is a growing acknowledgment that teacher training needs to be responsive to the assumed growing digital divide between teachers and students. In Brazil, future teachers were able to identify Generation Alpha’s use and ease with digital technologies but also recognized that their courses did not sufficiently prepare them to teach this new generation [ 25 ]. Similarly, future teachers in the Czech Republic believed that information and communication technology could support classes such as mathematics and elementary science but reported that for their own learning, they prefer textbooks and notes from lectures rather than the internet [ 113 ]. Finally, Aditya et al. [ 4 ] found that although early childhood education teachers in Indonesia had positive attitudes toward the use of information and communication technology, the lack of technical support and training led to difficulties with integrating technology in their online activities.

Even if proper teacher training is possible, how to train teachers is an important challenge. In a longitudinal case study dissertation, Mullen [ 78 ] investigated teachers’ jobs, administrative technology, education technology, and self-reported educator self-efficacy from the beginning of their employment through orientation and the first 60 days of an onboarding process in the United States. Unfortunately, the results showed that the onboarding intervention resulted only in minor changes in teachers’ self-efficacy.

Can the presence of older more experienced teachers from other generations make a difference? In a dissertation, Teske [ 108 ] exanimated generational differences of Baby Boomers, Generation Xers, and Millennials regarding educational and workplace values among American public-school teachers. Differences were found among the generations in work ethic, ability to establish positive relationships, utilization of technology, willingness to change, patience, and respect for hierarchy. Similarities between generations were found in motivation and types of leadership. In general:

The Baby Boomer generation perceived themselves resisting and experiencing difficulties when making changes . . . Millennials were identified by the other generations and perceived themselves as being flexible and open-minded to change, which was classified as a positive value. Generation Xers felt they aligned with the Baby Boomers’ difficulty to accept change, while the other two generations believed Generation Xers adapted well to change. (p. 174-175)

Overall, the findings suggest that it may be difficult to teach digital skills (see also, [ 60 ]).

As a model for understanding teachers’ preparedness, in Indonesia, there is significant national effort directed at measuring teachers’ capacity and competence with scientific literacy and digital technology. A national initiative creates teacher profiles through measures of a teacher’s ability to plan and integrate technology, pedagogy, and content knowledge for effective teaching to support student learning. Known at TPACK (Technological Pedagogy Content Knowledge), the framework has proved useful [ 60 ]. For example, Fakhriyah et al. [ 36 ] found that TPACK ability was good among future teachers and the factors that contributed the most to the abilities were the pedagogic component and the content knowledge component. Recommendations are for schools to improve teachers TPACK scores through individualized teacher training because group trainings fail to consider teacher characteristics. Following this recommendation, Churiyah et al. [ 28 ] evaluated a program that aimed to train and assist Indonesian vocational high school teachers in developing learning media and models that can accommodate the creativity skills of students. The results showed that teachers who took part in the program had skills in developing media and implementing learning models that support the students’ creative skills.

In addition to preparing teachers based on pre-existing competencies, specialized training can increase the chance of digital competence. For example, Karacan and Polat [ 55 ] examined the factors that predict Turkish pre-service English teachers’ intentions to use augmented reality in their classes. The pre-service teachers attended a training on augmented reality in language classes and a workshop to create augmented reality experiences. The results indicated that the pre-service teachers who perceived the augmented reality useful were more likely to adopt the augmented reality in their future classes. In addition, pre-service teachers’ self-efficacy beliefs also positively affected their adoption of augmented reality.

Training with the use of flipped classrooms had mixed results. Hashim and Shaari [ 44 ] examined Malaysian primary and secondary school teachers’ perception of flipped classrooms. Teachers perceived the flipped classroom as useful and believed it can improve their knowledge and skills. However, the teachers faced some challenges during the implementation, most of them believed that their students do not like watching short, flipped videos and they are not interested in the educational material in flipped classrooms.

Competencies other than mastering technologies are still important. Fauyan [ 37 ] conducted a study that investigated the roles and competencies of millennial teachers in Indonesia. The results showed that teachers had roles of agents of transferring the knowledge, managers, learning agents that created active and creative learning environment, motivators who encourage students’ involvement by using multimethod, multimedia, and multisource. Additionally, the following competences were found crucial: planning, implementing, and evaluating. Those roles and competencies showed teachers’ readiness in implementation of the latest technology during remote teaching in the COVID-19 period. In summary, there appears to be increasing efforts to understand how best to improve teacher competencies.

9.3 Teachers are Innovating

Teachers have been innovating and experimenting with new teaching methods for Generation Alpha with mixed success. In Ukraine, Morze et al. [ 77 ] examined competences required for critical evaluation of internet resources among future primary school teachers. The results showed that most future teachers have faced different types of fraud online and all teachers were aware of cybersecurity measures carried out at the national level. Most of the future teachers believed that critical evaluation of Internet resources should be developed in the computer science classes. The future teachers believed that the following techniques should be used most often in the development of future teachers’ internet critical thinking: project activity, effective use of digital tools, and collaboration in groups. Based on the findings, the authors designed a model of the system of formation of internet resource critical evaluation skills of future primary school teachers.

In another effort to improve Generation Alpha’s reading skills, Aberšek and Kerneža [ 2 ] examined Slovenian primary teachers’ attitudes towards an Internet Reciprocal Teaching (IRT) method that aims to improve students’ functional literacy competence when using the internet and screens. Research with previous generations suggest that paper-based reading produced better learning outcomes than screen-based reading [ 31 ]. Teachers believed that the IRT method is suitable for developing functional literacy in digital learning environment among students 9–11 years old and should be modified for younger students. There was an acknowledgement that there was no going back to paper-based reading.

Games and robots are also making inroads into Generation Alpha curriculums. Masril et al. [ 74 ] found that the use of robotic technology (Lego Mindstorms Ev3) as a learning resource by Indonesian elementary school teachers had a positive effect on behavior and was perceived as a learning tool that should be used in the elementary school curriculum. In Turkey, Akkaya et al. [ 9 ] found that most teachers considered themselves competent in using technology and they used digital games mostly in mathematics classes. Teachers believed that although there are many educational benefits, the usage of games could lead to physical health problems, communication problems, focusing problems, mental disorders, and excessive time loss.

In summary, the experimentation with novel approaches has some promising results, but there was no single innovation that has been replicated or scaled to an extent to be seen as universally effective.

10 The role of new approaches to education

New approaches to education are occurring at every level. Twelve studies focused on rethinking curriculums and programs for Generation Alpha. Five studies were from Indonesia, two studies were from Europe, two from the United States, one from Turkey, one from Algeria, and one from Kuwait.

10.1 Rethinking national approaches

In some countries, researchers are discovering how best to train teachers at the national level. In Croatia, Jukić and Škojo [ 51 ] conducted interviews with 10 information and communication technology (ICT) experts and 10 university professors to assess the future and integration of technology. Reflecting their orientation, the ICT experts perceived that teachers have insufficient training, their computer literacy is lower than students, and schools do not have adequate equipment. According to the ICT experts, teaching must be more dynamic and should be gamified as future occupations will be related to highly developed technology and artificial intelligence. The professors raised concerns about challenges associated with insufficient social interactions, problems of socialization, and insufficient development of social competencies and communication skills. In other words, social-emotional learning was important. The professors also agreed that the teaching process must be updated to be more interesting, teaching methods need to be multimodal with visualization of the teaching content, the teaching process must be more dynamic with active learning and interactive teaching, and the curriculum must be attractive with elective subjects.

When countries do innovate to meet the needs of Generation Alpha, it is important to evaluate the effectiveness of the approach. In Algeria, Sarnou [ 97 ] investigated the reasons of unsuccessful technologization of schools and universities and found the major reasons for the failure of an effective integration of ICT into the classrooms were social, cultural, economic, and educational factors. Specifically, there were deeply rooted regional differences in culture, politics, and financing within the country that made integration of ICT difficult. The ineffective integration of ICT was also found to negatively influence the relationship between teachers and students.

In summary, although a few articles depict efforts to understand how best to educate Generation Alpha, the results have not yet translated into national policies.

10.2 Rethinking language programs

An important goal in many educational systems is to improve language proficiency of non-native speakers and there has been some success with new programs aimed at increasing language proficiency for Generation Alpha. For example, Kadir et al. [ 52 ] examined the effectiveness of a 3 year foreign language program of Arabic-English-Japanese in three Indonesian schools. The language program implemented smart and creative learning methodologies with audio and visual gadgets. The findings reveled that the program created engaging and enjoyable learning environment for students. Also in Indonesia, Rombot et al. [ 91 ] developed a blended learning model for foreign speakers that gave the students the opportunity to repeatedly read the text and ultimately improved Indonesian reading skills. Finally, Shamir et al. [ 101 ] explored the effectiveness of the Waterford Early Learning curriculum, a game-based curriculum designed to promote English as a foreign language through reading, writing, and typing among students in kindergarten through second grade. The results showed that students that used the curriculum had significantly higher literacy scores than students who did not use the curriculum.

In summary, as more language curriculums innovate to take advantage of technology, there will likely be an increase of research that capitalizes on Generation Alpha’s presumed digital competence.

10.3 Rethinking STEM and ICT programs

For a generation that is digitally fluent, there is a natural increased emphasis on science, technology, engineering, and math (STEM) and information and communications technology (ICT) programs in education. This increased emphasis has yielded research attempting to take advantage of Generation Alpha’s ability to learn. A quasi-experimental dissertation by LiCalsi [ 68 ] examined the effects of robotics curriculum on American elementary students’ attitude, interest, persistence, self-efficacy, and career interest in STEM. The results indicated that younger students in the treatment group had an increase in the measured variables compared to older students. Girls in the treatment group had an increase in self-efficacy and career interest in STEM compared to girls in the control group.

In another dissertation, Malallah [ 71 ] developed a computational thinking pedagogy framework with a virtual world environment for early childhood education. Using a developed STEM model designed to meet the needs of Arabic/Persian Gulf region students, the STEM program improved students’ computational thinking abilities. The study compared the implementation of the STEM program in the U.S. and in Kuwait and examined factors that influence female and male preference and performance in STEM education in Kuwait.

In a study aimed at second graders, Lucenko et al. [ 70 ] examined the effectiveness of an innovative curriculum design in a Ukrainian primary school. The design used project-research activities in the lessons and the teacher’s role was an organizer of the student project activity. The results showed that the innovative curriculum design was more effective than a traditional methodological approach.

Turkish gifted students perceived that a flipped learning model was fun, different, instructive, useful, increased learning, saved time, provided opportunities for practice, advantageous, and flexible in terms of in-class practices. The study also showed that there was not a significant difference in the emotional semantic orientations in the in-class practices between female and male students. However, there were significant differences in the out-of-class effectiveness and entertainment. Male students perceived the flipped learning model more effective than female students and female students perceived that the flipped learning model as more fun than male students did [ 80 ].

10.4 Rethinking other programs

Two studies fell outside the category of language and science programs. Akmal et al. [ 10 ] evaluated the application of a social-emotional learning model that involves collaboration with parents in early childhood education institutions in Indonesia. The results indicated that a program that aims to teach social-emotional skills in early childhood can be successfully implemented by teachers and parents. In another program aimed at teachers, Defit et al. [ 30 ] developed a Literacy and Technology-based Elementary School Teacher Development model that integrates coaching and mentoring. The aim of the program was to optimize teachers’ leadership abilities to improve the quality of Indonesian teachers in the current digital era. The feasibility of the program design was assessed by lecturers and school principals and deemed suitable for teachers.

Overall, the role of new approaches to the education of Generation Alpha has been to reimagine traditional STEM and languages areas and experiment with some non-traditional areas such as social-emotional learning. Although the cited research is included because of the link to Generation Alpha, there are likely many more relevant efforts naturally occurring with this population that do not operationally define their populations by generation.

11 The role of teaching tools

Next to the role of teachers, the availability of teaching tools (broadly construed) to teach Generation Alpha is the most important factor in understanding how and whether education differs for Generation Alpha. Tools were categorized according to whether there was some evaluation or whether they were in development. Forty-three studies examined a development, or a usage, of a specific technological teaching tool designed for use with Generation Alpha. Out of the forty-four studies, thirty-one studies were conducted in Asia (mostly in Indonesia and Malaysia), four in Europe, two in the United States, two in Middle East, one in Australia, one in Ecuador, one in collaboration between Saudi Arabia, Pakistan, Malaysia, Canada, United Kingdom, and Sweden, one in collaboration between Indonesia and Portugal, and one in a collaboration between Indonesia and Germany.

11.1 Evaluated teaching tools

Eleven studies employed either a pre-test and post-test or experimental design with a control group to evaluate the effect of the educational tool. The teaching tools that were used were the following: virtual reality glasses [ 93 ], Project Based Learning assisted by Electronic Media [ 95 ], Loose Parts learning media [ 86 ], learning media based on modules and GeoGebr [ 15 ], augmented reality pictorial storybook [ 69 ], QR codes as an Augmented reality [ 11 ], Six Facets of Serious Game Design and Ernest Adams’ Game Design [ 27 ], jazz chants approach [ 102 ], collaborative planning and teaching with virtual reality [ 72 ], multimedia learning environment Augmented Reality English Vocabulary Acquisition [ 114 ], and AsKINstagram [ 50 ].

Overall, the teaching tools were effective. Positive outcomes included improved drawing performance [ 93 ], increased motivation [ 95 ], improved science process skills [ 95 ], improved mathematics learning outcomes and performance [ 15 , 27 ], improved naturalist intelligence [ 86 ], increased anxiety in mathematics learning [ 69 ], enhanced student performance [ 11 ], improved academic performance in English as a second Language [ 102 , 114 ], improved vocabulary learning in English as a second Language [ 72 ], and improved students’ writing English as a second language [ 50 ].

11.2 Unevaluated teaching tools

Nineteen studies focused on a usage of a specific teaching tool without an evaluation of the tool’s effect. The teaching tools that were used were the following: Science Technology Engineering and Math-Project Based Learning [ 82 ], Virtual Reality technology [ 3 , 49 , 61 ], serious games [ 1 ], expected game-based learning for protracted waste problem [ 64 ], code.org [ 20 ], Minecraft [ 107 ], digital board game Master Malaysia 123 v2 [ 57 ], Instagram [ 18 ], Instagram interactive face filters [ 90 ], social media [ 116 ], YouTube [ 87 ], WhatsApp [ 105 ], interactive digital phonics show [ 42 ], voca-lens [ 117 ], virtual game using the Sphero haptic device [ 23 ], Chromebook [ 115 ], educational mobile applications (Nevřelová, 2020), and use of technology [ 98 ].

The goals of these projects were to increase cognitive engagement [ 115 ], increase phonological awareness [ 42 ], increase communication skills [ 82 ], increase engagement and entertainment [ 61 ], increase reading skills [ 107 ], increase computational thinking [ 20 ], enhance knowledge [ 57 ], increase language skills for English as a second language [ 116 , 117 ], increase motivation to speak English as a second language [ 18 , 90 ], engaged students [ 98 ], build children’s awareness of waste problems [ 3 ], increase early mathematic skills [ 87 ], increase early literacy skills [ 87 ], increase socio-emotional development [ 87 ], increase executive function [ 87 ], improve narrative writing [ 105 ], improve quality of life [ 1 ], and improve the visual-motor coordination [ 23 ].

11.3 Tools in development

Thirteen studies focused on a development of a teaching tool specifically to improve or capitalize on the digital competency of Generation Alpha. The following teaching tools were developed: argumentation-based educational digital game (Bağ &Çalık, [ 19 ]), game design activity [ 48 ], Android based educational games [ 79 , 96 ], escape room-based mobile game [ 13 ], motion comic storyboard [ 56 ], digital map application with hand gesture recognition [ 84 ], mobile-based learning application [ 17 ],Omar & Abd Muin, [ 83 ]), story digital book [ 43 ], lift the flap book digital media [ 21 ], lift-a-flap picture book with audio [ 62 ], and Edmodo-Based Science Module [ 6 ].

Those teaching tools led to improved mathematical skills [ 56 ], improved literacy [ 43 ], improved science process skills [ 6 ], improved language skills (Omar and Abd Muin, [ 83 ]), improved vocabulary learning [ 62 ], increased motivation [ 19 ], increased creativity [ 48 ], improved motivation to learn English [ 13 ], improved English language skills [ 13 ], increased interests in learning mathematics [ 96 ], improved historical learning [ 84 ], improved early reading [ 79 ], and increased interests in learning science among alpha generation [ 21 ].

In a rare study that focused on a disabled sub-population within Generation Alpha, Aziz et al. [ 17 ] developed an application to improve math skills for those with poor vision. Unfortunately, the effectiveness of the mobile application was not evaluated. Given the benefit of new educational technologies for the disabled, it is surprising that more educational technologies are not developed [ 46 ].

Only one study acknowledged disadvantages associated with the use of technology in classrooms. A study conducted by Kurniawati et al. [ 61 ] focused on the integration of virtual reality into English vocabulary teaching in Indonesia. Teachers were able to incorporate the virtual reality into classes despite some challenges with device availability, workloads, teaching media, and classroom managerial skills. The students perceived that learning English vocabulary using virtual reality was engaging and entertaining. However, the students reported headaches from prolonged exposure to the virtual reality lens. Overall, the studies reveal technology being developed to appeal to students. The state-of-the-art of research has not yet focused on potential negative effects and how to overcome them.

In summary, the development of tools represents the greatest portion of the literature reviewed and reflects the ongoing interest in discovering how best to teach Generation Alpha.

12 The role of blended/online learning

The review of blended/online learning for Generation Alpha is a subset of a far greater research literature on teaching modalities. The literature review directly related to Generation Alpha produced ten studies that examined online or blended learning: five studies were conducted in Asia, two studies in Europe, one study in the United States, and two studies in the Middle East. The findings of the articles are best synthesized and sorted into categories describing the importance of experienced teachers and parents in improving experiences with Generation Alpha and describing some successes and some challenges with adopting distance and blended learning.

12.1 Experienced adults are important

During distance learning, parent–child interaction was an important factor that influenced the success of early childhood education in Indonesia [ 88 ]. To ensure success for distance learning, teachers in Turkey recommended parental support, active participation of students, use of Web 2.0 tools, gamification, and sharing information about training for parents [ 29 ]. Another study showed that experience with technology matters. Masry-Herzallah and Stavissky [ 75 ] found that older elementary and middle school teachers and younger elementary students had more difficulties than younger teachers and older students to transition to online learning during the pandemic in Israel.

12.2 Success with online and blended learning

Although the entire world adopted online and blended learning models during the pandemic, only a few studies used the term “Generation Alpha” in defining their success. Because the overall findings in the literature review are not driven by common research paradigms or common outcome measures, the results are specific to the researchers’ interests and relevant primarily within the context of the country’s education system. Thus, there is a limitation in generalizing the findings from the following countries:

In Ukraine, when teachers incorporated videos and online learning games, students perceived the online learning more beneficial [ 106 ].

In Indonesia, secondary school teachers reported that the best way to provide learning materials was through WhatsApp, Google Classroom and for some students, directly through the school [ 7 ]. In another study, Indonesian high school teachers reported that they used synchronous video conference platforms, asynchronous learning management systems (LMSs), and various learning media to help them conduct laboratory work [ 12 ]. The implementation of the blended learning when done in collaboration between Indonesians schools, teachers, parents, and students leads to more effective and meaningful learning [ 41 ]. Duggal et al. [ 34 ] found that Indian students were accepting the online education but there was a need to keep them engaged and enticed. The implementation of new learning methodologies such as gamified online education can help to overcome the online education challenges.

In Turkey, teachers reported the use of Google Classroom, Edmodo, Classdojo, Microsoft Teams, and Twinspace during distance teaching. They used these Learning Management Systems (LMS) for course and project management, flipped education practices, personal and professional development activities, and management of extracurricular and guidance activities. They described that the LMSs allowed to quickly follow the learning process of the students, allowed students individual progress, and the lessons were more efficient [ 29 ].

In the United States, Kingsbury [ 58 ] compared U. S. students’ online learning experience between schools that were already virtual and traditional in-person schools during the pandemic. He found that the virtual schools outperformed the in-person schools and parents were more likely to report that their child learned a lot during online learning.

12.3 Teacher and school challenges

The challenges to implementing online and blended learning as related to Generation Alpha fell into two categories: teacher challenges and school challenges.

During kindergarten distance teaching in Israel, pre-service teachers came across some challenges with communications, attitudes, tools, and technological skills [ 8 ]. Legvart et al. [ 66 ] found that Slovenian elementary school teachers experienced issues with students limited digital literacy competences which impacted the communication between teachers and students and among the students. In Indonesia, secondary school teachers experienced obstacles with applications, limited internet data, learning management, assessment, and supervision [ 7 ]. Similarly for high school, Indonesian teachers experienced technical issues and reduced interaction during learning process [ 12 ]. Finally, Turkish teachers experienced the most issues with the deficiencies related to technological equipment [ 29 ].

Schools also faced challenges and some research addresses how improvements might be made. In a study of middle-schoolers, Bruggeman [ 22 ] confirmed that “the micro-school environment, with an intentional overlay of a student-centered philosophy, personalized learning, and small mixed-age classroom settings, has a positive impact on the development of three elements of student agency: motivation, choice, and competency” (p. 220). In addition to redesigning classroom settings, a study of 500 elementary school students in Indonesia show that the increased use of smartphones and social media (YouTube, Google) may necessitate the reimagining of how libraries remain relevant [ 104 ]. Given the slow rate of adaption to change, even universities should start thinking of how to meet the needs of Generation Alpha [ 118 ].

13 Conclusions

Despite frequent use of the term “Generation Alpha” in the research literature, relatively few studies report generational differences that reveal how children of this generation are characteristically different from previous generations. There is simply a strong assumption that Generation Alpha is different. A major worldwide concern is that the use of technology by Generation Alpha has decreased opportunities for social-emotional development and increased mental health problems. Where research has been conducted, the underlying goal is to discover how educational practices may benefit with parents and adults mediating technology use.

Within the reviewed literature, no reference was found to any field of studies or organization of scholars focused exclusively on Generation Alpha. Rather, the examined studies reveal research to be the work of independent researchers focused on mostly practical educational strategies. Most of the research literature assumes differences between Generation Alpha and previous generations without systematic observation. An open question is how Generation Alpha is qualitatively different from previous generations. Therefore, future research should utilize theoretical frameworks to identify and understand the unique characteristics, behaviors, and traits of the digital native Generation Alpha.

The term “Generation Alpha” frequently appears in international research literature and is not commonly used in studies the United States. Future researchers might conduct comparative studies across diverse cultures and educational settings while using international collaboration to develop a more comprehensive understanding of Generation Alpha’s characteristics and needs. Longitudinal studies could help understand how Generation Alpha’s experiences and exposure to technology shape their identities, needs, career choices, social-emotional development.

To improve education for members of Generation Alpha and all subsequent digital natives, research might also best be focused less on children’s use of technology and more on the roles and competencies of adults and teachers to create environments that facilitate both digital and social-emotional learning.

On a final note, during the pandemic, out of necessity, schools use of novel digital approaches to education accelerated and there may be many studies published in the future which are relevant to Generation Alpha. As noted, this literature review captures only a small portion of the education research directed at this population as there are many studies that target this age group but do not use the term “Generation Alpha.” A primary takeaway from the review should be that experienced adults matter in the success of Generation Alpha. To be sure, there are also many tools and digital educational tools and online strategies being developed and tested but none have emerged to be dominant. Indeed, there is not even consensus on the best approach. There is still much research needed to produce evidence-based tools that can be recommended. What is happening in the education research on Generation Alpha is an increased recognition of their presumed digital capacity without a corresponding consensus on best educational practices. Future policies and practices would benefit from more specific research on how generational cohorts differ from one another in their exposure and experience with technology.

Data availability

No datasets were generated or analysed during the current study.

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Höfrová, A., Balidemaj, V. & Small, M.A. A systematic literature review of education for Generation Alpha. Discov Educ 3 , 125 (2024). https://doi.org/10.1007/s44217-024-00218-3

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REVIEW article

Crafting personalized learning paths with ai for lifelong learning: a systematic literature review.

• 1 Business School, Tecnologico de Monterrey, Faculty of Informatics, Autonomous University of Querétaro, Monterrey, Mexico
• 2 EGADE Business School, Institute for the Future of Education, Tecnologico de Monterrey, Monterrey, Mexico
• 3 Faculty of Informatics, Autonomous University of Querétaro, Juriquilla, Mexico

The rapid evolution of knowledge requires constantly acquiring and updating skills, making lifelong learning crucial. Despite decades of artificial intelligence, recent advances promote new solutions to personalize learning in this context. The purpose of this article is to explore the current state of research on the development of artificial intelligence-mediated solutions for the design of personalized learning paths. To achieve this, a systematic literature review (SRL) of 78 articles published between 2019 and 2024 from the Scopus and Web or Science databases was conducted, answering seven questions grouped into three themes: characteristics of the published research, context of the research, and type of solution analyzed. This study identified that: (a) the greatest production of scientific research on the topic is developed in China, India and the United States, (b) the focus is mainly directed towards the educational context at the higher education level with areas of opportunity for application in the work context, and (c) the development of adaptive learning technologies predominates; however, there is a growing interest in the application of generative language models. This article contributes to the growing interest and literature related to personalized learning under artificial intelligence mediated solutions that will serve as a basis for academic institutions and organizations to design programs under this model.

Introduction

Today, lifelong learning has become an imperative to thrive in an ever-changing and evolving world. The quest for knowledge is no longer limited to traditional classrooms; instead, it has transformed into a personalized and adaptive journey that spans a lifetime. The individual’s life cycle can no longer be divided into a period of preparation followed by a period of action, but rather learning extends throughout all stages of life ( UNESCO, 2022 ). Promoting lifelong learning means creating systems that realize the right to education for people of all ages and provide opportunities to unleash their potential. Within the framework of the Sustainable Development Goals (SDGs) as guiding axes of the 2030 Agenda of the United Nations Educational Scientific and Cultural Organization (UNESCO), SDG 4 promotes ensuring inclusive quality education and promoting lifelong learning opportunities for people ( UNESCO, 2017 ). The Organization for Economic Cooperation and Development (OECD) proposes four fundamental characteristics for lifelong learning: a systematic vision that includes both formal and informal educational contexts, the centrality of the learner by placing him/her as the focus of learning, the motivation to learn by developing the ability to “learn to learn” through self-regulated and self-directed learning, and the focus on a multi-objective education where learning priorities and goals can be transformed throughout the individual’s life ( OECD, 2009 ), in addition to the above, the recognition of the evolution of the concept of a single job for life makes it imperative for workers’ organizations to direct their efforts towards the development of new skills and lifelong learning ( Organización Internacional del Trabajo, 2020 ). With a view to increasing the life expectancy and quality of life of individuals, the study and development of lifelong learning options is becoming increasingly relevant.

Despite the growing recognition of the importance of lifelong learning, there is a significant gap in understanding how to effectively personalize learning experiences to meet the diverse and evolving needs of individuals throughout their lives. Traditional educational methods often fall short in providing the flexibility and adaptability required for lifelong learning. This gap necessitates the exploration of innovative solutions to enhance the personalization of learning experiences.

In this context, artificial intelligence (AI) emerges as a powerful and transformative ally, enabling the creation of personalized and effective learning experiences capable of adapting to the priorities and needs of the individual at a specific point in his or her life. The integration of AI-assisted learning solutions are key to the modernization of education as they have the ability to create motivating and quality learning environments ( Zhang et al., 2023 ). Artificial intelligence chatbots, for example, can provide individualized instruction and feedback based on the needs and progress of each learner, revolutionizing the concept of personalized learning ( Osamor et al., 2023 ). With the advent of generative language models, AI has reached new heights of capability and versatility, sparking unprecedented interest in its application for the design of learning trajectories ( Ifelebuegu, 2023 ). Because of its ability to transform education by offering motivating and personalized experiences that are tailored to individual needs, AI is positioned as an ally for lifelong learning.

The application of AI in educational contexts is not only based on the development of learning objects or tools for their creation but encompasses a wide range of applications. For the personalization of AI-mediated learning, researchers can opt for solutions such as mobile learning, educational games, collaborative learning in social networks, MOOCs or the application of augmented reality, among many others ( Hamal et al., 2022 ; Del Campo et al., 2023 ), without leaving aside the conversion towards an assessment of learning focused on complex thinking skills, avoiding the implicit risk of bad practices in the use of AI by students that constitute a threat to the legitimacy of online assessment and academic integrity ( Ifelebuegu, 2023 ). It is necessary, then, to consider that the use of IA in the educational context involves risks and limitations, among which are the problems of privacy, misinformation, plagiarism, biases and cultural differences and the lack of human connection, as well as other ethical implications, especially the gaps that may arise between students with access to these new technologies and those students who will be left behind due to the impossibility of access to these new educational models ( Wang et al., 2023 ; Bulathwela et al., 2024 ). Finally, it is important to note that the adoption of AI is not in itself the answer to meet the educational challenges in higher education and its use must be accompanied by a correct pedagogical design ( O’dea and O’Dea, 2023 ; Osamor et al., 2023 ). The adoption of AI in the educational context implies enormous advantages, however it is necessary to adopt a careful approach that allows minimizing its risks and maximizing its benefits in the development of solutions for the personalization of learning under a sound pedagogical design as an indispensable complement to this process. This research uses systematic literature review (SLR) to identify design strategies for artificial intelligence solutions in learning personalization. It aims to provide a broad overview of the current state of AI in learning personalization, highlight key areas of research, and advance the use of AI to enhance personalized learning in lifelong learning by proposing recommendations based on the findings to advance the integration of AI in personalized lifelong learning environments. The results contribute to the advancement of AI in learning personalization, key to educational innovation.

Literature review

Lifelong learning.

The growing need for adults to acquire new skills and knowledge to advance their careers, address personal problems, or start and manage ventures has generated a growing interest in designing online education programs that are tailored to their specific needs and characteristics. In today’s evolving work environment, students entering the job market must demonstrate not only performance, but also collaboration, negotiation, planning, and organizational skills ( Partnership for 21st Century Learning, 2019 ). The labor market is experiencing a shift toward complex “hybrid” jobs with emerging roles. This transition reflects a movement from manufacturing to service-oriented industries, reducing the demand for mechanical-routine workers and emphasizing the need for autonomous, flexible, and creative individuals capable of problem solving ( Chiappe et al., 2020 ). The European Area of Lifelong Learning (2021) defines lifelong learning as any learning activity undertaken throughout life to improve knowledge, skills and competences with a personal, civic, social or occupational perspective. Adult education, especially in the context of lifelong learning, is a field that has become increasingly important in an ever-changing world. The idea that learning is a continuous process throughout life has become a fundamental principle.

Lifelong learning is crucial for the continued development and progress of individuals throughout their lives. Lifelong learning is important for several reasons. Firstly, it allows individuals to continuously acquire new knowledge and skills, enabling personal and professional growth throughout their lives ( Merriam et al., 2007 ). Secondly lifelong learning also promotes community well-being and contributes to social cohesion ( Merriam and Kee, 2014 ). Thirdly, it can enhance political stability and non-violence by fostering a culture of learning and knowledge acquisition ( Asongu and Nwachukwu, 2016 ). Fourthly lifelong learning is crucial for adapting to the changing demands of the labor market, as it helps individuals stay relevant and employable ( Schultheiss and Backes-Gellner, 2023 ), and lastly, it can also promote equality by providing opportunities for disadvantaged individuals to access education and improve their socio-economic status ( Hällsten, 2011 ). In summary, lifelong learning is crucial to adapt to the changing demands of the labor market, promote equality and improve individual and community well-being, a tool that can help enhance the acquisition of new skills and knowledge for adults in a flexible environment and that advances at great speed thanks to AI for its ability to generate flexible and adaptable environments for the learner.

Artificial intelligence

In the current era, characterized by rapid technological evolution and the growing need for continuous adaptation, artificial intelligence emerges as an invaluable resource that transforms and enriches the educational landscape, offering personalized and motivating solutions that enhance lifelong learning. AI plays a crucial role in adult and lifelong learning. Lifelong learning aims to emulate the capability of humans to continuously acquire knowledge and skills throughout their lives ( Chen and Liu, 2018 ). AI systems can support lifelong learning by providing personalized and adaptive learning experiences, allowing individuals to learn at their own pace and according to their specific needs ( Ally and Perris, 2022 ). AI can enhance learning efficiency and cognitive abilities, improving teaching and learning outcomes ( Huang et al., 2021 ). Additionally, AI can automate the construction of visual–linguistic knowledge, enabling continuous knowledge construction for lifelong learning ( Ha et al., 2015 ). The use of AI in education can transform learning into an ongoing, lifelong process ( Tang and Deng, 2022 ). AI supports ongoing initiatives to promote lifelong learning by providing tailored and adaptable experiences. Moreover, AI aids lifelong learning through automated assessment and progress tracking ( Sanabria-Z et al., 2023 ). Overall, AI has the potential to revolutionize adult and lifelong learning by providing personalized, adaptive, and continuous learning experiences.

Despite the growing importance of online adult education and the existence of effective design principles, relatively little attention has been paid to how to adapt these principles to adult learning, specifically those seeking to acquire new skills related to adapting to changing work environments. Massive open online courses (MOOCs) have triggered a sudden change in the educational scene. Its characteristics of being free, heterogeneous, multi-thematic, and fostering lifelong learning have completely changed the instructional design scene, allowing these innovations and new architectures of teaching and learning to be included ( Valenzuela et al., 2019 ). In this sense, Conget et al. (2021) point out that lifelong learning benefits both from feedback that elicits reflection on learning as well as the promotion of curiosity, motivation, perseverance and regulation of learning. Lifelong learning empowers individuals to have control over their own learning agenda and shape their own lives ( Eynon and Malmberg, 2021 ). As adult learners may differ significantly from younger learners, learning design based on andragogical principles and focused on the pragmatic and context-dependent needs of learners calls for consideration of incorporating frameworks on lifelong learning for learning design. Overall, lifelong learning is essential for personal development, societal progress, and economic prosperity.

Personalized learning

Personalized learning has become a key approach in contemporary education. In a world where universal approaches are no longer appropriate, personalized learning requires education to provide customized solutions to students according to their particular needs. This represents a significant shift from traditional teacher-centered models. However, the reality of education in schools, especially in developing countries, is far from being flexible, personalized, oriented to the development of soft skills, and based on the use of information and communication technologies ( Chiappe et al., 2020 ). The U.S. Office of Educational Technology defined personalized learning as “instruction tailored to the specific learning needs, preferences, and interests of diverse learners” ( Peters and Araya, 2011 ). In a personalized educational environment, learning objectives and content, as well as method and pace, may vary, so personalization encompasses differentiation and individualization. Personalization of learning can be based either on the cognitive characteristics or behavioral traits of learners but also on their learning style, level of knowledge or learning preferences ( Nguyen and Nguyen, 2023 ). Personalization of learning enables the improvement of both academic performance and the enhancement of student’s digital skills ( Yang and Wen, 2023 ). However, it is not without its challenges, mainly in terms of social skills development due to the lack of community learning environments, which can isolate students and prevent them from acquiring the skills needed in a work environment ( Pence, 2020 ). The personalization of learning represents an enormous advantage, not only in terms of improving the student’s academic performance and the development of digital skills, but also in terms of the needs of adult learning in work contexts with a focus on lifelong learning.

Notwithstanding the efforts made for the incorporation of personalization of learning in the educational system, it still presents challenges that need to be faced. It is necessary to expand the existing knowledge on the design of learning itineraries in terms of actors involved, personalization strategies, pattern language, structure and ways to evaluate the results of the implementation ( Buitrago et al., 2021 ). Personalized learning challenges include a lack of unified agreement on the components needed for a dynamic, personalized learning approach, which can provide unique and effective learning experiences. It should consider learner profiles, prior knowledge, adaptive learning paths, and flexible self-paced environments, with learning analytics generating dynamic environments ( Shemshack et al., 2021 ). Personalization is most successful when relevant learner characteristics are measured repeatedly during the learning process and used to adapt instruction in a systematic way ( Tetzlaff et al., 2021 ). A successful personalized learning system balances making the best recommendations based on current knowledge and exploring new learning trajectories that may pay off ( Tang et al., 2019 ). In summary, to delve into the design of learning pathways and overcome the challenges that the implementation of personalized learning represents, it is crucial to foster effective and adaptive educational experiences that cater to the diverse needs and preferences of learners.

In order to establish the basis for the exploration of artificial intelligence-mediated personalization of learning as a response to the growing need to establish a continuum of lifelong learning and professional development, a systematic literature review (SRL) was conducted. The SRL method allows identifying, analyzing and interpreting evidence related to the research objective in an unbiased and repeatable way ( García-Peñalvo, 2017 ) being a process that synthesizes research in a systematic, transparent and reproducible way in order to enrich knowledge and inform policy making and practice ( Tranfield et al., 2003 ). The process consisted of (1) the formulation of research questions, (2) the selection of databases and search terms or keywords for the preliminary mapping of articles published on the topic in question as part of the search process, (3) the selection of quality criteria for the inclusion and exclusion of articles to be reviewed, (4) the selection and extraction of data and, finally, (5) the synthesis of information that would allow answers to the research questions defined ( Kitchenham and Charters, 2007 ; Landa et al., 2011 ). The control of this process was carried out following the guidelines established by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement ( Page et al., 2021 ).

Research questions

The research questions were established in order to analyze published articles on artificial intelligence-mediated solutions for personalization of learning, published in the last five years. These seven research questions were designed to address three topics of interest:

1. Characteristics of the studies analyzed in the field of artificial intelligence and personalized learning.

2. Educational level and environment in which the research is carried out.

3. Solutions and technologies for AI-mediated personalization of learning.

The choice of research questions was based on the following criteria:

1. Relevance: the questions had to be relevant to the research topic, which is artificial intelligence applied to learning personalization.

2. Accuracy: The questions had to be precise and concise, to facilitate their understanding and response.

3. Feasibility: The questions had to be feasible to answer, taking into account the limitations of the research.

The research questions formulated are described in Table 1 .

Table 1 . Topics and research questions.

Search process

The search for articles was performed on February 26, 2024, in the Scopus and Web of Science (WoS) databases. The keywords chosen were: artificial intelligence and personalized learning, the search period: 2019 to 2024 and the document type: article, with the publication languages: English or Spanish, were the delimiters. The search strings are shown in Table 2 .

Table 2 . Search strings.

Inclusion and exclusion criteria

The Scopus and Web of Science (WoS) databases were used to search for articles. The search terms used were: Artificial intelligence, Personal learning, Personal training, and Learning path. For the elaboration of this review, the following categorization of criteria for the inclusion of information was carried out:

1. Thematic: Artificial intelligence and Personalized learning.

2. Spatial: It was decided not to establish a geographical restriction. Worldwide research is included only in English language.

3. Temporal: Papers published during the last five years are included, starting in 2019.

4. Type of document: Research articles.

5. As criteria for exclusion of information, it was established that documents lacking a theoretical or conceptual framework would be eliminated, as well as those that did not meet the previously established inclusion criteria.

Selection process and data extraction

The number of articles found in both databases (Scopus and WoS) was 139. Following quality criteria, four records corresponding to articles withdrawn from the publications were eliminated to proceed to a review of titles and abstracts in order to identify those articles that corresponded to the focus of interest of this publication in terms of type of document and relationship with the topic, leaving a total of 78 articles selected for the SRL, from which the following information was extracted: Title, Author, DOI, Abstract, Name of the publication, Number of citations and Country of the first author. This information was compiled in an Excel file available for consultation. Figure 1 shows the selection process of articles included in the SLR.

Figure 1 . PRISMA diagram.

Data extraction and synthesis

Before writing the narrative synthesis, the 78 selected publications were carefully read and summarized in the context of the research questions. Given the newness of the research in the field there was, to the authors’ knowledge, no definitive classification proposed for the type of artificial intelligence solutions used for learning personalization, so, to answer question RQ7 a classification of our own was created based on the classifications proposed by Hashim et al. (2022) and Bozkurt et al. (2021) which includes the categories: (1) Adaptive learning environments, (2) AI-aided LMS, (3) AI-Enabled Mobile Apps, (4) Content recommendation systems, (5) Generative language model, (6) Holistic integration, (7) Intelligent conversational agents and tutorials, (8) Learning analytics and personalized assessment, (9) Machine learning, (10) Smart classrooms/IoT, and (11) Virtual and augmented reality.

This section presents the results obtained from the meticulous and systematic analysis of the data collected. The articles under analysis are integrated in Supplementary Material , with an identification number, in order to identify them in some of the results.

RQ1. What is the geographical distribution of the authors of the articles analyzed?

In order to identify the regions of the world that are currently leading research on personalization of learning through artificial intelligence-assisted solutions, the geographical dispersion of the first authors of the articles reviewed was analyzed. Figure 2 shows that the highest concentration of authors is located in China outnumbering in a ratio of about 5:1 the next concentration located in the United States, followed by India and Germany. The areas with low scientific production on the subject are in Africa and Latin America.

Figure 2 . Geographical dispersion of authors.

The study of the geographic distribution of authors with interest in the subject allows us to identify the regions of the world where there is an area of opportunity for the development of research, especially if we consider that the personalization of learning requires adapting it to the characteristics of the student, which may be influenced by both culture and place of origin.

RQ2. In which journals have artificial intelligence and personalized learning items been published more, and what are the Q levels of the journals?

To understand which academic journals are leaders in the publication of articles related to artificial intelligence and personalized learning, we analyzed the total number of published articles as well as the year of publication and the quartile of classification of the journal. It was found that 37% of the published articles are grouped in 8 journals at the Q1 and Q2 levels: International Journal of Emerging Technologies in Learning (6), Applied Mathematics and Nonlinear Sciences (5), Sustainability (Switzerland)(4), IEEE Access (4). Education Sciences (4), Frontiers in Psychology (3), Frontiers in Education (3). The analysis by quality levels (Q levels) of the journals indicates that 73% of the articles reviewed were published in journals with Q1 and Q2 level ( Figure 3 ).

Figure 3 . Frequency of publication by Q level.

The identification of publications and their quality level allows authors to identify sources for the dissemination of their research results through clarity on the perceived quality of research on personalization of learning and artificial intelligence as well as informed decision making for the development of strategies to raise the overall standard of research on the topic.

RQ3. What keywords are most commonly used in the analyzed articles?

The identification and analysis of keyword co-occurrence in the 78 articles reviewed made it possible to detect and group the concepts that are related to the key terms of this research: personalized learning and artificial intelligence. Figure 4 allows identifying the relationship between the term “personalized learning” and the four groupings of words, indicated by color, among which can be grouped those referring to learning analytics as a way to evaluate learning effectiveness, deep learning algorithms, the different personalization solutions mediated by artificial intelligence among which “e-learning,” “machine learning” and “learning systems” stand out. It is worth noting the grouping corresponding to generative language models where ChatGPT occupies an important place despite the novelty of its appearance in practice.

Figure 4 . Keyword co-occurrence map.

The keyword co-occurrence analysis allows identifying predominant themes and areas of interest in the academic literature, as well as emerging trends and possible gaps in knowledge. The groupings in terms of artificial intelligence-mediated solutions for learning personalization reflect the main trends in educational practice and confirm the classification proposed for the development of this research.

RQ4. What are the trends in research methods observed in the articles?

The analysis of trends in research methods and design allows inferring the preferred methodological approaches for research on learning personalization and artificial intelligence. The classification of articles was done in terms of quantitative methods (experimental design, quasi-experimental, applied experimental), qualitative methods (case study, conceptual design, exploratory, descriptive, grounded theory, theoretical research) and mixed methods (bibliometric analysis, case study, experimental, and systematic literature review) ( Bauer, 2000 ; Valenzuela and Flores, 2013 ). The review of research methods and designs results in a concentration of more than 50% of the articles reviewed whose research was conducted under a quantitative research method in an experimental research design. It should be noted that among the articles with the highest number of citations are studies conducted under a qualitative approach: A-131 with 64 citations on business models for the application of learning analytics and artificial intelligence for the development of learning solutions ( Renz and Hilbig, 2020 ), A-138 with 47 citations that explores whether artificial intelligence-mediated solutions can be applied in an enterprise learning environment for talent development ( Maity, 2019 ) A-132 with a state-of-the-art review on personalization of learning and exposing gaps in creating and maintaining motivation for learning, a focus on diversity, and eliminating data-and algorithm-induced biases ( Maghsudi et al., 2021 ), and A-056 on the advantages of using ChatGPT in the educational context, with 29 citations each ( Sallam et al., 2023 ). The results of this review are detailed in Figure 5 .

Figure 5 . Research methods and design.

The study of research methods and designs applied to the topics of interest is useful to determine their state of maturity since a tendency towards experimental and exploratory approaches denotes novelty in the topic and the existence of important gaps and areas of opportunity and convergence for future research.

RQ5. At what educational levels is the personalization of learning mediated by artificial intelligence being developed?

RQ6. In what environments is the research and development of initiatives on personalization of learning mediated by artificial intelligence taking place?

RQ7. What kind of solutions or technologies are being prioritized in the development of AI-mediated personalization of learning?

In order to identify the educational contexts and the kind of solutions towards which the research on personalized learning and artificial intelligence is directed, the classification of articles was made under three headings: Educational (encompasses primary education, secondary education and higher education), Industry (refers to the application in the workplace and/or in organizations) and Informal Education (that available outside the scope of formal education), finding that more than 80% of the research is conducted in the educational context with a prevalence of 86% in higher education, while only 11.5% of the articles reviewed have a focus on learning within the work context and 6.5% in the context of informal education. Within this context, the study of artificial intelligence under a holistic approach stands out (44%) and only two of the articles reviewed, whose first authors are located on the Asian continent, focus on a specific solution. In A-050, the authors point out the imperative need to establish large-scale rapid retraining systems for the workforce, anticipating that smart and virtual workplaces will replace traditional offices, and thus an artificial intelligence-based framework for rapid retraining of job skills is presented ( Ashrafi et al., 2023 ). In A-113, the authors propose a Blended Learning model for the assignment of learning paths based on the evaluation of knowledge acquisition with a focus on higher education applied in work contexts for the development of lifelong learning ( Bekmanova et al., 2021 ). The results of this analysis are shown in Figure 6 .

Figure 6 . Trends and context of AI-measured solutions for learning personalization.

In the educational context, the study of solutions focused on Adaptive Learning (11) predominates, followed closely by Generative Language Models (9): A-002, A-005, A-010, A-015, A-023, A-045, A-056, A-057, A-093 and A-040.

The analysis of the educational levels in which the development of solutions for the personalization of learning is studied allows identifying areas of opportunity in the expansion towards contexts where the solutions for the personalization of learning are applicable in the format of continuing education or lifelong learning for the development of work competencies as well as the development of Adaptive Learning solutions taking into account the characteristics of the adult population as well as the use of Generative Language Models for the integration of training programs for adults.

After analyzing 78 articles published in the Scopus and Web of Science databases between 2019 and 2024, the main findings and recommendations for future research are presented.

The study of personalization of learning through mediated solutions is not yet widespread as research is concentrated in a small number of countries. Figure 2 shows the geographical concentration of authors on the topic finding that the largest production comes from China, followed by far by the United States and India. The above coincides with the findings of other research ( Nguyen and Nguyen, 2023 ) and allows highlighting the need not only for further research on the subject but also for its geographical dispersion, since the risks exposed by Bulathwela et al. (2024) and Wang et al. (2023) point out the lack of democratization in the personalization of learning due to the existence of gaps in access, which will disadvantage students in countries lagging behind in the application and implementation of these solutions. The dissemination of the relevance of the topic is important to motivate research throughout the world.

Regarding the relevance of research on the topic, it is clear that top-tier academic publications are aware of the importance of the application of AI in personalizing learning. Figure 3 allows us to observe a significant concentration of publications in Q1 and Q2level publications. The finding coincides with that reported by Del Campo et al. (2023) who report a remarkable increase in the scientific literature on the subject in the last decade, focusing on the use of disruptive technologies in education especially in high impact publications. It is expected that this importance will peer in the educational environment for a greater scientific production on the research topic.

Although the novelty regarding the incorporation of generative language models has put the focus on the advantages of their adaptation for the personalization of learning, their adoption is not the only way to achieve the acquisition of learning and the development of digital skills. Figure 4 , through the correlation of terms around personalized learning allows identifying the importance of the development of these in a learning platform environment. The result is consistent with that pointed out by Hamal et al. (2022) and Valenzuela et al. (2019) . The new technologies, in terms of personalization of learning mediated by AI through solutions such as mobile learning, educational games, collaborative learning in social networks, MOOCs or the application of augmented reality, as well as the evaluation and feedback on them ( Ifelebuegu, 2023 ). The variety of options in terms of learning personalization demands a holistic approach to evaluate the effectiveness of different solutions.

Much of the research around AI and its different applications is still novel. Figure 5 shows a significant concentration of research still in its experimental and theoretical design stage. In their research Chiappe et al. (2020) point out that, although these concepts are not new, personal learning paths, research-based teaching, open, flexible and digitally supported curricula and lifelong learning, awaken a renewed interest by educational researchers and new developments around this area of study. It is inferred that, as this line of research acquires greater maturity, there will be more research on the evaluation of the effectiveness of its applications.

Finally, the existence of a wide window of opportunity for the development of personalized learning applications in informal education environments and work contexts where the need for lifelong learning dynamics is imperative is highlighted. Figure 6 shows the development trends and context in which AI-mediated solutions for the personalization of learning are developed where an incipient development is observed in informal education and work contexts. The design of these solutions must obey a correct pedagogical design ( Pence, 2020 ) and be oriented to the needs of users at different educational levels as a means of preparation for accessing job opportunities ( Sanabria-Z et al., 2023 ). In a world of constant change and evolution, individuals require learning solutions tailored to their interests, goals and preferences that allow them to remain relevant in the workplace.

Research on personalization of learning and the use of AI in lifelong learning emerges as a vital field in the current era of constant change in response to the need to adapt by developing the practice of continuous and adaptive lifelong learning. In this sense, the integration of AI solutions in education offers transformative potential to create personalized and effective learning experiences that adapt to individual and contextual needs. From the application of AI chatbots to generative language models, AI offers a range of innovative tools that revolutionize the concept of personalized learning.

However, it is crucial to address the challenges and risks associated with the use of AI in education. The concentration of research development in a few countries can contribute to algorithmic bias and the digital divide, highlighting the need for a careful and ethical approach to the implementation of these technologies. Furthermore, it is critical to recognize that IA is not a one-size-fits-all solution to all educational challenges but must be complemented by sound pedagogical design and attention to the specific needs of learners in different educational contexts and levels.

This study highlights the importance of continuing to research and develop IL solutions for the personalization of learning, especially in areas such as informal education and the workplace. The identification of trends, areas of opportunity and ethical challenges provides a solid foundation for future research and educational practices. Ultimately, the goal is to promote equitable and democratized access to personalized learning opportunities, thus driving innovation and progress in education towards lifelong learning.

This study is not exhaustive as it only analyzed publications found in two databases, which have the widest coverage but are not the only ones where relevant research on the topic can be found. The research method used (SLR) and its findings provide a comprehensive overview of the existing literature and highlight key areas in the use of IL to enhance personalized learning in the context of lifelong learning. The results of this research contribute to the advancement of the use of IA to enhance personalization of learning as one of the pillars of lifelong learning for innovation in education. Future lines of research are proposed among which are the determination of the factors that influence the effectiveness of personalization of learning as well as the ethical implications of this development to ensure equity and non-discrimination in access to these solutions in order to promote the democratization of learning.

Author contributions

KB-C: Writing – original draft, Writing – review & editing. M-SR-M: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing. AM-A: Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors would like to thank the financial support from Tecnologico de Monterrey through the “Challenge-Based Research Funding Program 2022.” Project ID # I003 – IFE001 – C2-T3 – T. Also, academic support from Writing Lab, Institute for the Future of Education, Tecnologico de Monterrey, México.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2024.1424386/full#supplementary-material

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Keywords: personalized learning paths, lifelong learning, artificial intelligence, educational innovation, higher education

Citation: Bayly-Castaneda K, Ramirez-Montoya M-S and Morita-Alexander A (2024) Crafting personalized learning paths with AI for lifelong learning: a systematic literature review. Front. Educ . 9:1424386. doi: 10.3389/feduc.2024.1424386

Received: 28 April 2024; Accepted: 30 July 2024; Published: 08 August 2024.

Reviewed by:

Copyright © 2024 Bayly-Castaneda, Ramirez-Montoya and Morita-Alexander. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: K. Bayly-Castaneda, [email protected] ; M-S. Ramirez-Montoya, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Introduction to systematic review and meta-analysis

1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.

Introduction

A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].

Levels of evidence.

In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].

Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.

Study Planning

It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.

Flowchart illustrating a systematic review.

Formulating research questions

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].

Protocols and registration

In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.

Defining inclusion and exclusion criteria

Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.

Literature search and study selection

In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].

Quality of evidence

However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.

If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].

The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]

Data extraction

Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.

Data Analysis

The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.

The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and ​ and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.

Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.

Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).

Summary of Meta-analysis Methods Available in RevMan [ 28 ]

The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.

When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .

A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].

Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].

Heterogeneity

Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].

I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.

Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.

Publication bias

Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).

Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.

Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.

Result Presentation

When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.

The GRADE Evidence Quality for Each Outcome

N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.

When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.

A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.

When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.

1) http://www.ohri.ca .

2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .

3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.

4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.

5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.

6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.

7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.

8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].

9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].

10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.