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Case studies.

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Case studies are stories that are used as a teaching tool to show the application of a theory or concept to real situations. Dependent on the goal they are meant to fulfill, cases can be fact-driven and deductive where there is a correct answer, or they can be context driven where multiple solutions are possible. Various disciplines have employed case studies, including humanities, social sciences, sciences, engineering, law, business, and medicine. Good cases generally have the following features: they tell a good story, are recent, include dialogue, create empathy with the main characters, are relevant to the reader, serve a teaching function, require a dilemma to be solved, and have generality.

Instructors can create their own cases or can find cases that already exist. The following are some things to keep in mind when creating a case:

• What do you want students to learn from the discussion of the case?
• What do they already know that applies to the case?
• What are the issues that may be raised in discussion?
• How will the case and discussion be introduced?
• What preparation is expected of students? (Do they need to read the case ahead of time? Do research? Write anything?)
• What directions do you need to provide students regarding what they are supposed to do and accomplish?
• Do you need to divide students into groups or will they discuss as the whole class?
• Are you going to use role-playing or facilitators or record keepers? If so, how?
• What are the opening questions?
• How much time is needed for students to discuss the case?
• What concepts are to be applied/extracted during the discussion?
• How will you evaluate students?

To find other cases that already exist, try the following websites:

• The National Center for Case Study Teaching in Science , University of Buffalo. SUNY-Buffalo maintains this set of links to other case studies on the web in disciplines ranging from engineering and ethics to sociology and business
• A Journal of Teaching Cases in Public Administration and Public Policy , University of Washington

For more information:

• World Association for Case Method Research and Application

Book Review :  Teaching and the Case Method , 3rd ed., vols. 1 and 2, by Louis Barnes, C. Roland (Chris) Christensen, and Abby Hansen. Harvard Business School Press, 1994; 333 pp. (vol 1), 412 pp. (vol 2).

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Case-based learning.

Case-based learning (CBL) is an established approach used across disciplines where students apply their knowledge to real-world scenarios, promoting higher levels of cognition (see Bloom’s Taxonomy ). In CBL classrooms, students typically work in groups on case studies, stories involving one or more characters and/or scenarios.  The cases present a disciplinary problem or problems for which students devise solutions under the guidance of the instructor. CBL has a strong history of successful implementation in medical, law, and business schools, and is increasingly used within undergraduate education, particularly within pre-professional majors and the sciences (Herreid, 1994). This method involves guided inquiry and is grounded in constructivism whereby students form new meanings by interacting with their knowledge and the environment (Lee, 2012).

There are a number of benefits to using CBL in the classroom. In a review of the literature, Williams (2005) describes how CBL: utilizes collaborative learning, facilitates the integration of learning, develops students’ intrinsic and extrinsic motivation to learn, encourages learner self-reflection and critical reflection, allows for scientific inquiry, integrates knowledge and practice, and supports the development of a variety of learning skills.

CBL has several defining characteristics, including versatility, storytelling power, and efficient self-guided learning.  In a systematic analysis of 104 articles in health professions education, CBL was found to be utilized in courses with less than 50 to over 1000 students (Thistlethwaite et al., 2012). In these classrooms, group sizes ranged from 1 to 30, with most consisting of 2 to 15 students.  Instructors varied in the proportion of time they implemented CBL in the classroom, ranging from one case spanning two hours of classroom time, to year-long case-based courses. These findings demonstrate that instructors use CBL in a variety of ways in their classrooms.

The stories that comprise the framework of case studies are also a key component to CBL’s effectiveness. Jonassen and Hernandez-Serrano (2002, p.66) describe how storytelling:

Is a method of negotiating and renegotiating meanings that allows us to enter into other’s realms of meaning through messages they utter in their stories,

Helps us find our place in a culture,

Allows us to explicate and to interpret, and

Facilitates the attainment of vicarious experience by helping us to distinguish the positive models to emulate from the negative model.

Neurochemically, listening to stories can activate oxytocin, a hormone that increases one’s sensitivity to social cues, resulting in more empathy, generosity, compassion and trustworthiness (Zak, 2013; Kosfeld et al., 2005). The stories within case studies serve as a means by which learners form new understandings through characters and/or scenarios.

CBL is often described in conjunction or in comparison with problem-based learning (PBL). While the lines are often confusingly blurred within the literature, in the most conservative of definitions, the features distinguishing the two approaches include that PBL involves open rather than guided inquiry, is less structured, and the instructor plays a more passive role. In PBL multiple solutions to the problem may exit, but the problem is often initially not well-defined. PBL also has a stronger emphasis on developing self-directed learning. The choice between implementing CBL versus PBL is highly dependent on the goals and context of the instruction.  For example, in a comparison of PBL and CBL approaches during a curricular shift at two medical schools, students and faculty preferred CBL to PBL (Srinivasan et al., 2007). Students perceived CBL to be a more efficient process and more clinically applicable. However, in another context, PBL might be the favored approach.

In a review of the effectiveness of CBL in health profession education, Thistlethwaite et al. (2012), found several benefits:

Students enjoyed the method and thought it enhanced their learning,

Instructors liked how CBL engaged students in learning,

CBL seemed to facilitate small group learning, but the authors could not distinguish between whether it was the case itself or the small group learning that occurred as facilitated by the case.

Other studies have also reported on the effectiveness of CBL in achieving learning outcomes (Bonney, 2015; Breslin, 2008; Herreid, 2013; Krain, 2016). These findings suggest that CBL is a vehicle of engagement for instruction, and facilitates an environment whereby students can construct knowledge.

Science – Students are given a scenario to which they apply their basic science knowledge and problem-solving skills to help them solve the case. One example within the biological sciences is two brothers who have a family history of a genetic illness. They each have mutations within a particular sequence in their DNA. Students work through the case and draw conclusions about the biological impacts of these mutations using basic science. Sample cases: You are Not the Mother of Your Children ; Organic Chemisty and Your Cellphone: Organic Light-Emitting Diodes ;   A Light on Physics: F-Number and Exposure Time

Medicine – Medical or pre-health students read about a patient presenting with specific symptoms. Students decide which questions are important to ask the patient in their medical history, how long they have experienced such symptoms, etc. The case unfolds and students use clinical reasoning, propose relevant tests, develop a differential diagnoses and a plan of treatment. Sample cases: The Case of the Crying Baby: Surgical vs. Medical Management ; The Plan: Ethics and Physician Assisted Suicide ; The Haemophilus Vaccine: A Victory for Immunologic Engineering

Public Health – A case study describes a pandemic of a deadly infectious disease. Students work through the case to identify Patient Zero, the person who was the first to spread the disease, and how that individual became infected.  Sample cases: The Protective Parent ; The Elusive Tuberculosis Case: The CDC and Andrew Speaker ; Credible Voice: WHO-Beijing and the SARS Crisis

Law – A case study presents a legal dilemma for which students use problem solving to decide the best way to advise and defend a client. Students are presented information that changes during the case.  Sample cases: Mortgage Crisis Call (abstract) ; The Case of the Unpaid Interns (abstract) ; Police-Community Dialogue (abstract)

Business – Students work on a case study that presents the history of a business success or failure. They apply business principles learned in the classroom and assess why the venture was successful or not. Sample cases: SELCO-Determining a path forward ; Project Masiluleke: Texting and Testing to Fight HIV/AIDS in South Africa ; Mayo Clinic: Design Thinking in Healthcare

Humanities - Students consider a case that presents a theater facing financial and management difficulties. They apply business and theater principles learned in the classroom to the case, working together to create solutions for the theater. Sample cases: David Geffen School of Drama

Recommendations

Finding and Writing Cases

Consider utilizing or adapting open access cases - The availability of open resources and databases containing cases that instructors can download makes this approach even more accessible in the classroom. Two examples of open databases are the Case Center on Public Leadership and Harvard Kennedy School (HKS) Case Program , which focus on government, leadership and public policy case studies.

• Consider writing original cases - In the event that an instructor is unable to find open access cases relevant to their course learning objectives, they may choose to write their own. See the following resources on case writing: Cooking with Betty Crocker: A Recipe for Case Writing ; The Way of Flesch: The Art of Writing Readable Cases ;   Twixt Fact and Fiction: A Case Writer’s Dilemma ; And All That Jazz: An Essay Extolling the Virtues of Writing Case Teaching Notes .

Implementing Cases

Take baby steps if new to CBL - While entire courses and curricula may involve case-based learning, instructors who desire to implement on a smaller-scale can integrate a single case into their class, and increase the number of cases utilized over time as desired.

Use cases in classes that are small, medium or large - Cases can be scaled to any course size. In large classes with stadium seating, students can work with peers nearby, while in small classes with more flexible seating arrangements, teams can move their chairs closer together. CBL can introduce more noise (and energy) in the classroom to which an instructor often quickly becomes accustomed. Further, students can be asked to work on cases outside of class, and wrap up discussion during the next class meeting.

Encourage collaborative work - Cases present an opportunity for students to work together to solve cases which the historical literature supports as beneficial to student learning (Bruffee, 1993). Allow students to work in groups to answer case questions.

Form diverse teams as feasible - When students work within diverse teams they can be exposed to a variety of perspectives that can help them solve the case. Depending on the context of the course, priorities, and the background information gathered about the students enrolled in the class, instructors may choose to organize student groups to allow for diversity in factors such as current course grades, gender, race/ethnicity, personality, among other items.  

Use stable teams as appropriate - If CBL is a large component of the course, a research-supported practice is to keep teams together long enough to go through the stages of group development: forming, storming, norming, performing and adjourning (Tuckman, 1965).

Walk around to guide groups - In CBL instructors serve as facilitators of student learning. Walking around allows the instructor to monitor student progress as well as identify and support any groups that may be struggling. Teaching assistants can also play a valuable role in supporting groups.

Interrupt strategically - Only every so often, for conversation in large group discussion of the case, especially when students appear confused on key concepts. An effective practice to help students meet case learning goals is to guide them as a whole group when the class is ready. This may include selecting a few student groups to present answers to discussion questions to the entire class, asking the class a question relevant to the case using polling software, and/or performing a mini-lesson on an area that appears to be confusing among students.  

Assess student learning in multiple ways - Students can be assessed informally by asking groups to report back answers to various case questions. This practice also helps students stay on task, and keeps them accountable. Cases can also be included on exams using related scenarios where students are asked to apply their knowledge.

Barrows HS. (1996). Problem-based learning in medicine and beyond: a brief overview. New Directions for Teaching and Learning, 68, 3-12.  

Bonney KM. (2015). Case Study Teaching Method Improves Student Performance and Perceptions of Learning Gains. Journal of Microbiology and Biology Education, 16(1): 21-28.

Breslin M, Buchanan, R. (2008) On the Case Study Method of Research and Teaching in Design.  Design Issues, 24(1), 36-40.

Bruffee KS. (1993). Collaborative learning: Higher education, interdependence, and authority of knowledge. Johns Hopkins University Press, Baltimore, MD.

Herreid CF. (2013). Start with a Story: The Case Study Method of Teaching College Science, edited by Clyde Freeman Herreid. Originally published in 2006 by the National Science Teachers Association (NSTA); reprinted by the National Center for Case Study Teaching in Science (NCCSTS) in 2013.

Herreid CH. (1994). Case studies in science: A novel method of science education. Journal of Research in Science Teaching, 23(4), 221–229.

Jonassen DH and Hernandez-Serrano J. (2002). Case-based reasoning and instructional design: Using stories to support problem solving. Educational Technology, Research and Development, 50(2), 65-77.  

Kosfeld M, Heinrichs M, Zak PJ, Fischbacher U, Fehr E. (2005). Oxytocin increases trust in humans. Nature, 435, 673-676.

Krain M. (2016) Putting the learning in case learning? The effects of case-based approaches on student knowledge, attitudes, and engagement. Journal on Excellence in College Teaching, 27(2), 131-153.

Lee V. (2012). What is Inquiry-Guided Learning?  New Directions for Learning, 129:5-14.

Nkhoma M, Sriratanaviriyakul N. (2017). Using case method to enrich students’ learning outcomes. Active Learning in Higher Education, 18(1):37-50.

Srinivasan et al. (2007). Comparing problem-based learning with case-based learning: Effects of a major curricular shift at two institutions. Academic Medicine, 82(1): 74-82.

Thistlethwaite JE et al. (2012). The effectiveness of case-based learning in health professional education. A BEME systematic review: BEME Guide No. 23.  Medical Teacher, 34, e421-e444.

Tuckman B. (1965). Development sequence in small groups. Psychological Bulletin, 63(6), 384-99.

Williams B. (2005). Case-based learning - a review of the literature: is there scope for this educational paradigm in prehospital education? Emerg Med, 22, 577-581.

Zak, PJ (2013). How Stories Change the Brain. Retrieved from: https://greatergood.berkeley.edu/article/item/how_stories_change_brain

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In This Article Expand or collapse the "in this article" section Case Study in Education Research

Introduction, general overview and foundational texts of the late 20th century.

• Conceptualisations and Definitions of Case Study
• Case Study and Theoretical Grounding
• Choosing Cases
• Methodology, Method, Genre, or Approach
• Case Study: Quality and Generalizability
• Multiple Case Studies
• Exemplary Case Studies and Example Case Studies
• Criticism, Defense, and Debate around Case Study

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Case Study in Education Research by Lorna Hamilton LAST REVIEWED: 27 June 2018 LAST MODIFIED: 27 June 2018 DOI: 10.1093/obo/9780199756810-0201

It is important to distinguish between case study as a teaching methodology and case study as an approach, genre, or method in educational research. The use of case study as teaching method highlights the ways in which the essential qualities of the case—richness of real-world data and lived experiences—can help learners gain insights into a different world and can bring learning to life. The use of case study in this way has been around for about a hundred years or more. Case study use in educational research, meanwhile, emerged particularly strongly in the 1970s and 1980s in the United Kingdom and the United States as a means of harnessing the richness and depth of understanding of individuals, groups, and institutions; their beliefs and perceptions; their interactions; and their challenges and issues. Writers, such as Lawrence Stenhouse, advocated the use of case study as a form that teacher-researchers could use as they focused on the richness and intensity of their own practices. In addition, academic writers and postgraduate students embraced case study as a means of providing structure and depth to educational projects. However, as educational research has developed, so has debate on the quality and usefulness of case study as well as the problems surrounding the lack of generalizability when dealing with single or even multiple cases. The question of how to define and support case study work has formed the basis for innumerable books and discursive articles, starting with Robert Yin’s original book on case study ( Yin 1984 , cited under General Overview and Foundational Texts of the Late 20th Century ) to the myriad authors who attempt to bring something new to the realm of case study in educational research in the 21st century.

This section briefly considers the ways in which case study research has developed over the last forty to fifty years in educational research usage and reflects on whether the field has finally come of age, respected by creators and consumers of research. Case study has its roots in anthropological studies in which a strong ethnographic approach to the study of peoples and culture encouraged researchers to identify and investigate key individuals and groups by trying to understand the lived world of such people from their points of view. Although ethnography has emphasized the role of researcher as immersive and engaged with the lived world of participants via participant observation, evolving approaches to case study in education has been about the richness and depth of understanding that can be gained through involvement in the case by drawing on diverse perspectives and diverse forms of data collection. Embracing case study as a means of entering these lived worlds in educational research projects, was encouraged in the 1970s and 1980s by researchers, such as Lawrence Stenhouse, who provided a helpful impetus for case study work in education ( Stenhouse 1980 ). Stenhouse wrestled with the use of case study as ethnography because ethnographers traditionally had been unfamiliar with the peoples they were investigating, whereas educational researchers often worked in situations that were inherently familiar. Stenhouse also emphasized the need for evidence of rigorous processes and decisions in order to encourage robust practice and accountability to the wider field by allowing others to judge the quality of work through transparency of processes. Yin 1984 , the first book focused wholly on case study in research, gave a brief and basic outline of case study and associated practices. Various authors followed this approach, striving to engage more deeply in the significance of case study in the social sciences. Key among these are Merriam 1988 and Stake 1995 , along with Yin 1984 , who established powerful groundings for case study work. Additionally, evidence of the increasing popularity of case study can be found in a broad range of generic research methods texts, but these often do not have much scope for the extensive discussion of case study found in case study–specific books. Yin’s books and numerous editions provide a developing or evolving notion of case study with more detailed accounts of the possible purposes of case study, followed by Merriam 1988 and Stake 1995 who wrestled with alternative ways of looking at purposes and the positioning of case study within potential disciplinary modes. The authors referenced in this section are often characterized as the foundational authors on this subject and may have published various editions of their work, cited elsewhere in this article, based on their shifting ideas or emphases.

Merriam, S. B. 1988. Case study research in education: A qualitative approach . San Francisco: Jossey-Bass.

This is Merriam’s initial text on case study and is eminently accessible. The author establishes and reinforces various key features of case study; demonstrates support for positioning the case within a subject domain, e.g., psychology, sociology, etc.; and further shapes the case according to its purpose or intent.

Stake, R. E. 1995. The art of case study research . Thousand Oaks, CA: SAGE.

Stake is a very readable author, accessible and yet engaging with complex topics. The author establishes his key forms of case study: intrinsic, instrumental, and collective. Stake brings the reader through the process of conceptualizing the case, carrying it out, and analyzing the data. The author uses authentic examples to help readers understand and appreciate the nuances of an interpretive approach to case study.

Stenhouse, L. 1980. The study of samples and the study of cases. British Educational Research Journal 6:1–6.

DOI: 10.1080/0141192800060101

A key article in which Stenhouse sets out his stand on case study work. Those interested in the evolution of case study use in educational research should consider this article and the insights given.

Yin, R. K. 1984. Case Study Research: Design and Methods . Beverley Hills, CA: SAGE.

This preliminary text from Yin was very basic. However, it may be of interest in comparison with later books because Yin shows the ways in which case study as an approach or method in research has evolved in relation to detailed discussions of purpose, as well as the practicalities of working through the research process.

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Teacher Professional Development Case Studies: K-12, TVET, and Tertiary Education

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Quality teaching is vital to meet the increasingly complex needs of students as they prepare for further education and work in the 21st century. The publication showcases 14 case studies from around the world as examples of teacher professional development programs that support, improve, and harness teaching capabilities and expertise. It also discusses government initiatives and other factors that can contribute to quality teaching.

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Tertiary education is essential for opportunity, competitiveness, and growth

Mamta murthi, nina arnhold, roberta malee bassett.

Most of us remember being asked as a child, what do you want to be when you grow up? Many of us would eagerly answer: a teacher, an engineer, a scientist. As we grew older, we realized that pursuing any of these essential jobs in the 21st century requires higher levels of education. Tertiary education has become the aspiration of more and more young people around the globe while at the same time a fundamental requirement for employment in the sectors and industries that drive development in every country.

At the World Bank, we encourage countries to strengthen their tertiary education systems to build the professional expertise necessary to drive public and private sector development; to produce the doctors, nurses, teachers, scientists, managers and so on needed to support growing economies.

We have been engaged in tertiary education reforms since 1963; currently, the Bank has an active portfolio of over US$9 billion in projects supporting post-secondary education efforts across all regions. This makes the World Bank the largest external source of funding for tertiary education in the world.

As we build upon decades of learning and experience in tertiary education reform, we continue to ask ourselves key questions to ensure we are grounding our work in the current conditions and with a focus on outcomes that can best serve our clients.  

Four of these key questions are:

1.    Should low-income countries invest public resources in tertiary education, including research, even when resources are needed to strengthen primary and secondary education as well?

Yes.  Economic research unequivocally demonstrates high rates of private and social return on investments in tertiary education, including research. The benefits include higher employment and earnings, increased productivity and innovation, greater social stability, more effective public administrations, increased civic engagement, and better health outcomes.  And these outcomes are critical for low-income countries’ development today, in the same way they were for today’s rich countries when they were much poorer 200 or even 1,000 years ago when they started investing in tertiary education. 

The consequences of underinvesting in tertiary education include loss of talent, limited access to applied research capacity needed for local problem solving, hindered economic growth due to low levels of skills in the workforce, low-quality teaching and learning at every level of education, and, perhaps most glaringly, expanded wealth inequality both within countries and among nations, with those investing more experiencing higher levels of innovation and attraction of investment. 

There is ample evidence of the role of education, including tertiary education, has played in boosting economic growth. One such example is the Republic of Korea which in 1948, was one of the poorest countries in the world. It grew to be the world’s 15th richest economy, however, by investing in and strengthening education at all levels, including providing universal access to tertiary education. Interestingly, already in the early 1980s, Korea started placing higher education in a lifelong learning context and has reaped the benefits of this decision ever since.

2.    Should digital skills and digital technologies be part of the investments made in tertiary education in low-income countries?

Yes. Digital technology and capabilities are essential to more resilient tertiary education systems. The COVID-19 pandemic has clearly revealed that digital technologies are the primary instrument for resilience in tertiary education, and that all types of tertiary education institutions will need to embrace and adapt to remote delivery and online settings. The expansion of ‘Open Universities’ around the world—including in Turkey, Tanzania, and Zimbabwe, for example—reflects the access and delivery opportunities afforded by embracing digital delivery in tertiary education. Building individual- and organizational-level digital skills can support: high-quality, adaptive teaching for students; new opportunities in digital research tools and methods; and digital competences. Without investments in digitalization and digital skills, individuals and systems will fall further behind, as has been exposed by the shifts in education delivery forced by the COVID-19 pandemic: in Sub-Saharan Africa, where over 80% of tertiary education students do not have access to reliable internet, the shift to on-line education during lockdown was not sustainable. 

3.    Will the sustained expansion of accessibility ensure the closing of equity gaps in tertiary education?

No, not without further measures including a combination of merit- and need-based approaches to student support and a diversity of other options. To date, tertiary education expansion has generally not meant equitable access—that is, more students accessing tertiary education globally has not resulted in proportionally more students enrolling from low socioeconomic status or underrepresented groups. 

To address these issues, countries need to have deliberate and sound policies to concomitantly enable access to disadvantaged groups such as means-based scholarships, grants and student loan programs, and remedial intervention to ensure readiness for postsecondary studies. Moreover, countries should foster the development of a high-quality ecosystem of tertiary education with a variety of options and flexible pathways, including high-quality, short-cycle tertiary education programs, as has been developed with long-term benefits in California, for instance, via the “Master Plan for Higher Education” and in many countries in Central and Eastern Europe, including Hungary, Slovenia, and the Czech Republic.  

4.    Should countries invest in tertiary education only after they can ensure jobs for graduates?

No. The march of technology, the emergence of big data, AI and other elements of the ‘fourth’ industrial revolution are changing the nature of production and work all over the world.  Countries with a workforce lacking the required skills are less likely to keep up with technological advancement - as well as R&D-intensive domestic or foreign investments. Moreover, there is a lag in the response in the supply of tertiary graduates to the labor market demand from firms. Investments and policy reforms to improve other important aspects of the investment climate such as infrastructure, taxes, and regulations, can take a shorter time than to produce an adequately skilled supply of graduates. While this lag may seem troubling, perceptions of social unrest increasing when there are greater numbers of un- or under-employed educated people can make some policy makers look to delaying investments in tertiary education. However, there is little rigorous evidence that bears out this anecdotal perception that improved education in environments lacking opportunities leads to unrest.  Moreover, the alternative is simply unacceptable—no country should forgo developing the skills of its people as highly as possible in a globe dominated by knowledge economies.  Limiting education results in limited development—it’s as simple as that.

Every government must be purposeful in supporting and steering its tertiary education systems , including universities, polytechnics, community colleges, and other institutions, to build the human capital vital to the 21st century knowledge economy. As hubs for advanced education and skill development in every country, regardless of GDP and income status, these institutions have the potential to produce the skilled workers and leaders needed to create stable foundations for sound governance and dynamic economic growth, and address pressing challenges like global pandemics and climate change to promote opportunities today and into the future. The World Bank will continue to advance its support for the global tertiary education sector to prepare systems and institutions for the challenges of the 21st century.

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Blended Learning in Tertiary Education: A Case Study

• V. Aleksić , M. Ivanović
• Published in Balkan Conference in… 2013
• Computer Science, Education

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Everything you need to know about teaching cases

Teaching cases can help students apply their knowledge to a real-world situation and make learning interactive. Here’s how to use them

Kerryn Krige

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Kovin was struggling. He had lost his friend and partner the year before, and after their decades of partnership, taking over as CEO had made sense. But he could feel the tension in the senior management meeting. The team were enjoying the benefits of their commercial success –   they could attract talent, offer competitive salaries, develop more meaningful patents. But at what cost? Could they really still consider themselves a non-profit social enterprise? 

In the age of AI and Co-Pilot, the teaching case is gaining attention as a useful teaching tool. It not only consolidates lessons around central analytical themes, but also tests students’ analytical and interpretation skills. 

• Flip students’ presentations for more in-depth learning
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A teaching case like this one brings real-world difficulties into the classroom, allowing students to apply what they’ve learned and solve problems. This case tells the story of anti-apartheid activist-turned-optometrist Kovin Naidoo, who finds himself leading a multinational social enterprise after the death of his partner. He struggles to balance his deeply held commitments to social change with the benefits of a fast-commercialising organisation. Studying this, students experience CEO-level tensions balancing profit and purpose, and work through how to balance competing value systems.

How do teaching cases work?

In the classroom, the lecturer introduces the case, describing the main protagonist, the context that they are operating in, and the dilemma that they are faced with. Teaching cases are not short of drama. They typically end on a cliffhanger, that nail-biting “now what?” moment, which students must then solve. Like a detective in a novel, students piece together the main facts, identify the red herrings, and through this process apply theoretical frameworks. Teaching cases are wonderful tools for critical thinking , synthesis and analysis.

For many years, the teaching case was seen as the domain of law and business schools. But steady work by publishers such as Emerald, IVEY, Harvard, Yale and the Case Centre has seen the scope of cases widening. 

What they have in common is a commitment to developing cases that transport students to different contexts, where they can immerse themselves in the detail of the problem and apply their learnings, across a range of disciplines. It is this DNA that makes them a useful tool in the AI learning world.

Cases contextualise and tailor our curriculum. In doing so, they are an anchor in our efforts to build nuanced teaching materials, providing alternatives to standard scenarios which, when repeated, risk one-dimensional, monocausal teaching.

Because of their focus on different situations, they contribute to inclusivity . Not only do they provide contextual variation but they engage with core concepts in a multitude of ways to facilitate learning . They allow us to use storytelling to address difficult topics that have the potential to marginalise or exclude. They encourage simulation and group work and create a dialogue between theory and practice , both teaching processes that contribute to professional relevance . As a result, students are immersed in problem-based, action-oriented learning techniques, which results in them being “ engaged in, and structuring their own learning ” – one of the pillars of inclusive teaching practice .

But it is the teaching note that is the lecturer’s friend. It introduces new pedagogy and approaches. Anchored in Bloom’s Taxonomy , it builds the teaching narrative, specifying the learning outcomes and how theoretical frameworks can be taught.

We are seeing innovations in case writing as the format gains popularity, such as:

• Short cases: Harvard is pioneering cases of 800 words (the length of this article) with a snappy dilemma. These can be fictional or drawn from secondary literature, building an informed narrative around a figure they can relate to. Taylor Swift is the focus of this case, for example, which requires little preparation time and sparks lively discussion. 
• Visual cases: We’ve been seeing comic book-style teaching notes that encourage storyboarding and description of both theory and process. Visual cases encourage broad discussion as students read between the lines, stimulated by both the style of graphics and minimised narrative. IMD’s Netflix: streaming wars and IBS’ Turbulence on the Tarmac are stylistically different, setting a different tone for discussion and debate.   
• Multimedia cases: Animation or short-form interviews that students can interact with are becoming common in teaching cases. Role-playing can range from the classic Oxford-style debate, where students develop opposing arguments, to materials such as this animation by IE Business School, which allows chronological learning across a historical timeline. 
• Raw teaching cases encourage students to research the dilemma that they are presented with. These cases are crisp in their presentation of the dilemma, and require students to then research and investigate the problem and solution. Pioneered by Yale, Raw Cases teach students how to synthesise information. Examples include this case on SELCO , and this debate-style approach to analysing the  Systems of Exchange framework.
• Students writing teaching cases as assignments: The NACRA community and our colleague Maria Ballesteros-Solas have developed the idea of putting students in the driving seat .  Here, students write a teaching case and note as an assignment. It requires students to research a dilemma and establish theoretical links   –   a creative process that encourages deep analysis and application of theory to practice.

Teaching cases are not new but the questions educators are grappling with on the use of AI have refocused our attention on the value that they bring to the classroom. They bring real-world dilemmas for students to think through, where the applied nature of the work means that there is no one-size-fits-all response. By encouraging group work and interactive and inclusive teaching practice, they help us as teachers contextualise our content and create opportunities for rich, structured debate and embedded learning.

Kerryn Krige is senior lecturer of teaching practice at the Marshall Institute at the London School of Economics. 

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Case studies in educational research

31 Mar 2011

Dr Lorna Hamilton

To cite this reference: Hamilton, L. (2011) Case studies in educational research, British Educational Research Association on-line resource. Available on-line at [INSERT WEB PAGE ADDRESS HERE] Last accessed [insert date here]

Case study is often seen as a means of gathering together data and giving coherence and limit to what is being sought. But how can we define case study effectively and ensure that it is thoughtfully and rigorously constructed?  This resource shares some key definitions of case study and identifies important choices and decisions around the creation of studies. It is for those with little or no experience of case study in education research and provides an introduction to some of the key aspects of this approach: from the all important question of what exactly is case study, to the key decisions around case study work and possible approaches to dealing with the data collected. At the end of the resource, key references and resources are identified which provide the reader with further guidance.

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• Published: 24 April 2023

Artificial intelligence in higher education: the state of the field

• Helen Crompton   ORCID: orcid.org/0000-0002-1775-8219 1 , 3 &
• Diane Burke 2  

International Journal of Educational Technology in Higher Education volume  20 , Article number:  22 ( 2023 ) Cite this article

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This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72% of the studies focused on students, 17% instructors, and 11% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT.

A systematic review examining AIEd in higher education (HE) up to the end of 2022.

Unique findings in the switch from US to China in the most studies published.

A two to threefold increase in studies published in 2021 and 2022 to prior years.

AIEd was used for: Assessment/Evaluation, Predicting, AI Assistant, Intelligent Tutoring System, and Managing Student Learning.

Introduction

The use of artificial intelligence (AI) in higher education (HE) has risen quickly in the last 5 years (Chu et al., 2022 ), with a concomitant proliferation of new AI tools available. Scholars (viz., Chen et al., 2020 ; Crompton et al., 2020 , 2021 ) report on the affordances of AI to both instructors and students in HE. These benefits include the use of AI in HE to adapt instruction to the needs of different types of learners (Verdú et al., 2017 ), in providing customized prompt feedback (Dever et al., 2020 ), in developing assessments (Baykasoğlu et al., 2018 ), and predict academic success (Çağataylı & Çelebi, 2022 ). These studies help to inform educators about how artificial intelligence in education (AIEd) can be used in higher education.

Nonetheless, a gap has been highlighted by scholars (viz., Hrastinski et al., 2019 ; Zawacki-Richter et al., 2019 ) regarding an understanding of the collective affordances provided through the use of AI in HE. Therefore, the purpose of this study is to examine extant research from 2016 to 2022 to provide an up-to-date systematic review of how AI is being used in the HE context.

Artificial intelligence has become pervasive in the lives of twenty-first century citizens and is being proclaimed as a tool that can be used to enhance and advance all sectors of our lives (Górriz et al., 2020 ). The application of AI has attracted great interest in HE which is highly influenced by the development of information and communication technologies (Alajmi et al., 2020 ). AI is a tool used across subject disciplines, including language education (Liang et al., 2021 ), engineering education (Shukla et al., 2019 ), mathematics education (Hwang & Tu, 2021 ) and medical education (Winkler-Schwartz et al., 2019 ),

Artificial intelligence

The term artificial intelligence is not new. It was coined in 1956 by McCarthy (Cristianini, 2016 ) who followed up on the work of Turing (e.g., Turing, 1937 , 1950 ). Turing described the existence of intelligent reasoning and thinking that could go into intelligent machines. The definition of AI has grown and changed since 1956, as there has been significant advancements in AI capabilities. A current definition of AI is “computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and the use of data for complex processing tasks” (Popenici et al., 2017 , p. 2). The interdisciplinary interest from scholars from linguistics, psychology, education, and neuroscience who connect AI to nomenclature, perceptions and knowledge in their own disciplines could create a challenge when defining AI. This has created the need to create categories of AI within specific disciplinary areas. This paper focuses on the category of AI in Education (AIEd) and how AI is specifically used in higher educational contexts.

As the field of AIEd is growing and changing rapidly, there is a need to increase the academic understanding of AIEd. Scholars (viz., Hrastinski et al., 2019 ; Zawacki-Richter et al., 2019 ) have drawn attention to the need to increase the understanding of the power of AIEd in educational contexts. The following section provides a summary of the previous research regarding AIEd.

Extant systematic reviews

This growing interest in AIEd has led scholars to investigate the research on the use of artificial intelligence in education. Some scholars have conducted systematic reviews to focus on a specific subject domain. For example, Liang et. al. ( 2021 ) conducted a systematic review and bibliographic analysis the roles and research foci of AI in language education. Shukla et. al. ( 2019 ) focused their longitudinal bibliometric analysis on 30 years of using AI in Engineering. Hwang and Tu ( 2021 ) conducted a bibliometric mapping analysis on the roles and trends in the use of AI in mathematics education, and Winkler-Schwartz et. al. ( 2019 ) specifically examined the use of AI in medical education in looking for best practices in the use of machine learning to assess surgical expertise. These studies provide a specific focus on the use of AIEd in HE but do not provide an understanding of AI across HE.

On a broader view of AIEd in HE, Ouyang et. al. ( 2022 ) conducted a systematic review of AIEd in online higher education and investigated the literature regarding the use of AI from 2011 to 2020. The findings show that performance prediction, resource recommendation, automatic assessment, and improvement of learning experiences are the four main functions of AI applications in online higher education. Salas-Pilco and Yang ( 2022 ) focused on AI applications in Latin American higher education. The results revealed that the main AI applications in higher education in Latin America are: (1) predictive modeling, (2) intelligent analytics, (3) assistive technology, (4) automatic content analysis, and (5) image analytics. These studies provide valuable information for the online and Latin American context but not an overarching examination of AIEd in HE.

Studies have been conducted to examine HE. Hinojo-Lucena et. al. ( 2019 ) conducted a bibliometric study on the impact of AIEd in HE. They analyzed the scientific production of AIEd HE publications indexed in Web of Science and Scopus databases from 2007 to 2017. This study revealed that most of the published document types were proceedings papers. The United States had the highest number of publications, and the most cited articles were about implementing virtual tutoring to improve learning. Chu et. al. ( 2022 ) reviewed the top 50 most cited articles on AI in HE from 1996 to 2020, revealing that predictions of students’ learning status were most frequently discussed. AI technology was most frequently applied in engineering courses, and AI technologies most often had a role in profiling and prediction. Finally, Zawacki-Richter et. al. ( 2019 ) analyzed AIEd in HE from 2007 to 2018 to reveal four primary uses of AIEd: (1) profiling and prediction, (2) assessment and evaluation, (3) adaptive systems and personalization, and (4) intelligent tutoring systems. There do not appear to be any studies examining the last 2 years of AIEd in HE, and these authors describe the rapid speed of both AI development and the use of AIEd in HE and call for further research in this area.

Purpose of the study

The purpose of this study is in response to the appeal from scholars (viz., Chu et al., 2022 ; Hinojo-Lucena et al., 2019 ; Zawacki-Richter et al., 2019 ) to research to investigate the benefits and challenges of AIEd within HE settings. As the academic knowledge of AIEd HE finished with studies examining up to 2020, this study provides the most up-to-date analysis examining research through to the end of 2022.

The overarching question for this study is: what are the trends in HE research regarding the use of AIEd? The first two questions provide contextual information, such as where the studies occurred and the disciplines AI was used in. These contextual details are important for presenting the main findings of the third question of how AI is being used in HE.

In what geographical location was the AIEd research conducted, and how has the trend in the number of publications evolved across the years?

What departments were the first authors affiliated with, and what were the academic levels and subject domains in which AIEd research was being conducted?

Who are the intended users of the AI technologies and what are the applications of AI in higher education?

A PRISMA systematic review methodology was used to answer three questions guiding this study. PRISMA principles (Page et al., 2021 ) were used throughout the study. The PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols (PRISMA-P; Moher et al., 2015 ) were utilized in this study to provide an a priori roadmap to conduct a rigorous systematic review. Furthermore, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA principles; Page et al., 2021 ) were used to search, identify, and select articles to be included in the research were used for searching, identifying, and selecting articles, then in how to read, extract, and manage the secondary data gathered from those studies (Moher et al., 2015 , PRISMA Statement, 2021 ). This systematic review approach supports an unbiased synthesis of the data in an impartial way (Hemingway & Brereton, 2009 ). Within the systematic review methodology, extracted data were aggregated and presented as whole numbers and percentages. A qualitative deductive and inductive coding methodology was also used to analyze extant data and generate new theories on the use of AI in HE (Gough et al., 2017 ).

The research begins with the search for the research articles to be included in the study. Based on the research question, the study parameters are defined including the search years, quality and types of publications to be included. Next, databases and journals are selected. A Boolean search is created and used for the search of those databases and journals. Once a set of publications are located from those searches, they are then examined against an inclusion and exclusion criteria to determine which studies will be included in the final study. The relevant data to match the research questions is then extracted from the final set of studies and coded. This method section is organized to describe each of these methods with full details to ensure transparency.

Search strategy

Only peer-reviewed journal articles were selected for examination in this systematic review. This ensured a level of confidence in the quality of the studies selected (Gough et al., 2017 ). The search parameters narrowed the search focus to include studies published in 2016 to 2022. This timeframe was selected to ensure the research was up to date, which is especially important with the rapid change in technology and AIEd.

The data retrieval protocol employed an electronic and a hand search. The electronic search included educational databases within EBSCOhost. Then an additional electronic search was conducted of Wiley Online Library, JSTOR, Science Direct, and Web of Science. Within each of these databases a full text search was conducted. Aligned to the research topic and questions, the Boolean search included terms related to AI, higher education, and learning. The Boolean search is listed in Table 1 . In the initial test search, the terms “machine learning” OR “intelligent support” OR “intelligent virtual reality” OR “chatbot” OR “automated tutor” OR “intelligent agent” OR “expert system” OR “neural network” OR “natural language processing” were used. These were removed as they were subcategories of terms found in Part 1 of the search. Furthermore, inclusion of these specific AI terms resulted in a large number of computer science courses that were focused on learning about AI and not the use of AI in learning.

Part 2 of the search ensured that articles involved formal university education. The terms higher education and tertiary were both used to recognize the different terms used in different countries. The final Boolean search was “Artificial intelligence” OR AI OR “smart technologies” OR “intelligent technologies” AND “higher education” OR tertiary OR graduate OR undergraduate. Scholars (viz., Ouyang et al., 2022 ) who conducted a systematic review on AIEd in HE up to 2020 noted that they missed relevant articles from their study, and other relevant journals should intentionally be examined. Therefore, a hand search was also conducted to include an examination of other journals relevant to AIEd that may not be included in the databases. This is important as the field of AIEd is still relatively new, and journals focused on this field may not yet be indexed in databases. The hand search included: The International Journal of Learning Analytics and Artificial Intelligence in Education, the International Journal of Artificial Intelligence in Education, and Computers & Education: Artificial Intelligence.

Electronic and hand searches resulted in 371 articles for possible inclusion. The search parameters within the electronic database search narrowed the search to articles published from 2016 to 2022, per-reviewed journal articles, and duplicates. Further screening was conducted manually, as each of the 138 articles were reviewed in full by two researchers to examine a match against the inclusion and exclusion criteria found in Table 2 .

The inter-rater reliability was calculated by percentage agreement (Belur et al., 2018 ). The researchers reached a 95% agreement for the coding. Further discussion of misaligned articles resulted in a 100% agreement. This screening process against inclusion and exclusion criteria resulted in the exclusion of 237 articles. This included the duplicates and those removed as part of the inclusion and exclusion criteria, see Fig.  1 . Leaving 138 articles for inclusion in this systematic review.

(From: Page et al., 2021 )

PRISMA flow chart of article identification and screening

The 138 articles were then coded to answer each of the research questions using deductive and inductive coding methods. Deductive coding involves examining data using a priori codes. A priori are pre-determined criteria and this process was used to code the countries, years, author affiliations, academic levels, and domains in the respective groups. Author affiliations were coded using the academic department of the first author of the study. First authors were chosen as that person is the primary researcher of the study and this follows past research practice (e.g., Zawacki-Richter et al., 2019 ). Who the AI was intended for was also coded using the a priori codes of Student, Instructor, Manager or Others. The Manager code was used for those who are involved in organizational tasks, e.g., tracking enrollment. Others was used for those not fitting the other three categories.

Inductive coding was used for the overarching question of this study in examining how the AI was being used in HE. Researchers of extant systematic reviews on AIEd in HE (viz., Chu et al., 2022 ; Zawacki-Richter et al., 2019 ) often used an a priori framework as researchers matched the use of AI to pre-existing frameworks. A grounded coding methodology (Strauss & Corbin, 1995 ) was selected for this study to allow findings of the trends on AIEd in HE to emerge from the data. This is important as it allows a direct understanding of how AI is being used rather than how researchers may think it is being used and fitting the data to pre-existing ideas.

Grounded coding process involved extracting how the AI was being used in HE from the articles. “In vivo” (Saldana, 2015 ) coding was also used alongside grounded coding. In vivo codes are when codes use language directly from the article to capture the primary authors’ language and ensure consistency with their findings. The grounded coding design used a constant comparative method. Researchers identified important text from articles related to the use of AI, and through an iterative process, initial codes led to axial codes with a constant comparison of uses of AI with uses of AI, then of uses of AI with codes, and codes with codes. Codes were deemed theoretically saturated when the majority of the data fit with one of the codes. For both the a priori and the grounded coding, two researchers coded and reached an inter-rater percentage agreement of 96%. After discussing misaligned articles, a 100% agreement was achieved.

Findings and discussion

The findings and discussion section are organized by the three questions guiding this study. The first two questions provide contextual information on the AIEd research, and the final question provides a rigorous investigation into how AI is being used in HE.

RQ1. In what geographical location was the AIEd research conducted, and how has the trend in the number of publications evolved across the years?

The 138 studies took place across 31 countries in six of seven continents of the world. Nonetheless, that distribution was not equal across continents. Asia had the largest number of AIEd studies in HE at 41%. Of the seven countries represented in Asia, 42 of the 58 studies were conducted in Taiwan and China. Europe, at 30%, was the second largest continent and had 15 countries ranging from one to eight studies a piece. North America, at 21% of the studies was the continent with the third largest number of studies, with the USA producing 21 of the 29 studies in that continent. The 21 studies from the USA places it second behind China. Only 1% of studies were conducted in South America and 2% in Africa. See Fig.  2 for a visual representation of study distribution across countries. Those continents with high numbers of studies are from high income countries and those with low numbers have a paucity of publications in low-income countries.

Geographical distribution of the AIEd HE studies

Data from Zawacki-Richter et. al.’s ( 2019 ) 2007–2018 systematic review examining countries found that the USA conducted the most studies across the globe at 43 out of 146, and China had the second largest at eleven of the 146 papers. Researchers have noted a rapid trend in Chinese researchers publishing more papers on AI and securing more patents than their US counterparts in a field that was originally led by the US (viz., Li et al., 2021 ). The data from this study corroborate this trend in China leading in the number of AIEd publications.

With the accelerated use of AI in society, gathering data to examine the use of AIEd in HE is useful in providing the scholarly community with specific information on that growth and if it is as prolific as anticipated by scholars (e.g., Chu et al., 2022 ). The analysis of data of the 138 studies shows that the trend towards the use of AIEd in HE has greatly increased. There is a drop in 2019, but then a great rise in 2021 and 2022; see Fig.  3 .

Chronological trend in AIEd in HE

Data on the rise in AIEd in HE is similar to the findings of Chu et. al. ( 2022 ) who noted an increase from 1996 to 2010 and 2011–2020. Nonetheless Chu’s parameters are across decades, and the rise is to be anticipated with a relatively new technology across a longitudinal review. Data from this study show a dramatic rise since 2020 with a 150% increase from the prior 2 years 2020–2019. The rise in 2021 and 2022 in HE could have been caused by the vast increase in HE faculty having to teach with technology during the pandemic lockdown. Faculty worldwide were using technologies, including AI, to explore how they could continue teaching and learning that was often face-to-face prior to lockdown. The disadvantage of this rapid adoption of technology is that there was little time to explore the possibilities of AI to transform learning, and AI may have been used to replicate past teaching practices, without considering new strategies previously inconceivable with the affordances of AI.

However, in a further examination of the research from 2021 to 2022, it appears that there are new strategies being considered. For example, Liu et. al.’s, 2022 study used AIEd to provide information on students’ interactions in an online environment and examine their cognitive effort. In Yao’s study in 2022, he examined the use of AI to determine student emotions while learning.

RQ2. What departments were the first authors affiliated with, and what were the academic levels and subject domains in which AIEd research was being conducted?

Department affiliations

Data from the AIEd HE studies show that of the first authors were most frequently from colleges of education (28%), followed by computer science (20%). Figure  4 presents the 15 academic affiliations of the authors found in the studies. The wide variety of affiliations demonstrate the variety of ways AI can be used in various educational disciplines, and how faculty in diverse areas, including tourism, music, and public affairs were interested in how AI can be used for educational purposes.

Research affiliations

In an extant AIED HE systematic review, Zawacki-Richter et. al.’s ( 2019 ) named their study Systematic review of research on artificial intelligence applications in higher education—where are the educators? In this study, the authors were keen to highlight that of the AIEd studies in HE, only six percent were written by researchers directly connected to the field of education, (i.e., from a college of education). The researchers found a great lack in pedagogical and ethical implications of implementing AI in HE and that there was a need for more educational perspectives on AI developments from educators conducting this work. It appears from our data that educators are now showing greater interest in leading these research endeavors, with the highest affiliated group belonging to education. This may again be due to the pandemic and those in the field of education needing to support faculty in other disciplines, and/or that they themselves needed to explore technologies for their own teaching during the lockdown. This may also be due to uptake in professors in education becoming familiar with AI tools also driven by a societal increased attention. As the focus of much research by education faculty is on teaching and learning, they are in an important position to be able to share their research with faculty in other disciplines regarding the potential affordances of AIEd.

Academic levels

The a priori coding of academic levels show that the majority of studies involved undergraduate students with 99 of the 138 (72%) focused on these students. This was in comparison to the 12 of 138 (9%) for graduate students. Some of the studies used AI for both academic levels: see Fig.  5

Academic level distribution by number of articles

This high percentage of studies focused on the undergraduate population was congruent with an earlier AIED HE systematic review (viz., Zawacki-Richter et al., 2019 ) who also reported student academic levels. This focus on undergraduate students may be due to the variety of affordances offered by AIEd, such as predictive analytics on dropouts and academic performance. These uses of AI may be less required for graduate students who already have a record of performance from their undergraduate years. Another reason for this demographic focus can also be convenience sampling, as researchers in HE typically has a much larger and accessible undergraduate population than graduates. This disparity between undergraduates and graduate populations is a concern, as AIEd has the potential to be valuable in both settings.

Subject domains

The studies were coded into 14 areas in HE; with 13 in a subject domain and one category of AIEd used in HE management of students; See Fig.  6 . There is not a wide difference in the percentages of top subject domains, with language learning at 17%, computer science at 16%, and engineering at 12%. The management of students category appeared third on the list at 14%. Prior studies have also found AIEd often used for language learning (viz., Crompton et al., 2021 ; Zawacki-Richter et al., 2019 ). These results are different, however, from Chu et. al.’s ( 2022 ) findings that show engineering dramatically leading with 20 of the 50 studies, with other subjects, such as language learning, appearing once or twice. This study appears to be an outlier that while the searches were conducted in similar databases, the studies only included 50 studies from 1996 to 2020.

Subject domains of AIEd in HE

Previous scholars primarily focusing on language learning using AI for writing, reading, and vocabulary acquisition used the affordances of natural language processing and intelligent tutoring systems (e.g., Liang et al., 2021 ). This is similar to the findings in studies with AI used for automated feedback of writing in a foreign language (Ayse et al., 2022 ), and AI translation support (Al-Tuwayrish, 2016 ). The large use of AI for managerial activities in this systematic review focused on making predictions (12 studies) and then admissions (three studies). This is positive to see this use of AI to look across multiple databases to see trends emerging from data that may not have been anticipated and cross referenced before (Crompton et al., 2022 ). For example, to examine dropouts, researchers may consider examining class attendance, and may not examine other factors that appear unrelated. AI analysis can examine all factors and may find that dropping out is due to factors beyond class attendance.

RQ3. Who are the intended users of the AI technologies and what are the applications of AI in higher education?

Intended user of AI

Of the 138 articles, the a priori coding shows that 72% of the studies focused on Students, followed by a focus on Instructors at 17%, and Managers at 11%, see Fig.  7 . The studies provided examples of AI being used to provide support to students, such as access to learning materials for inclusive learning (Gupta & Chen, 2022 ), provide immediate answers to student questions, self-testing opportunities (Yao, 2022 ), and instant personalized feedback (Mousavi et al., 2020 ).

Intended user

The data revealed a large emphasis on students in the use of AIEd in HE. This user focus is different from a recent systematic review on AIEd in K-12 that found that AIEd studies in K-12 settings prioritized teachers (Crompton et al., 2022 ). This may appear that HE uses AI to focus more on students than in K-12. However, this large number of student studies in HE may be due to the student population being more easily accessibility to HE researchers who may study their own students. The ethical review process is also typically much shorter in HE than in K-12. Therefore, the data on the intended focus should be reviewed while keeping in mind these other explanations. It was interesting that Managers were the lowest focus in K-12 and also in this study in HE. AI has great potential to collect, cross reference and examine data across large datasets that can allow data to be used for actionable insight. More focus on the use of AI by managers would tap into this potential.

How is AI used in HE

Using grounded coding, the use of AIEd from each of the 138 articles was examined and six major codes emerged from the data. These codes provide insight into how AI was used in HE. The five codes are: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. For each of these codes there are also axial codes, which are secondary codes as subcategories from the main category. Each code is delineated below with a figure of the codes with further descriptive information and examples.

Assessment/evaluation

Assessment and Evaluation was the most common use of AIEd in HE. Within this code there were six axial codes broken down into further codes; see Fig.  8 . Automatic assessment was most common, seen in 26 of the studies. It was interesting to see that this involved assessment of academic achievement, but also other factors, such as affect.

Codes and axial codes for assessment and evaluation

Automatic assessment was used to support a variety of learners in HE. As well as reducing the time it takes for instructors to grade (Rutner & Scott, 2022 ), automatic grading showed positive use for a variety of students with diverse needs. For example, Zhang and Xu ( 2022 ) used automatic assessment to improve academic writing skills of Uyghur ethnic minority students living in China. Writing has a variety of cultural nuances and in this study the students were shown to engage with the automatic assessment system behaviorally, cognitively, and affectively. This allowed the students to engage in self-regulated learning while improving their writing.

Feedback was a description often used in the studies, as students were given text and/or images as feedback as a formative evaluation. Mousavi et. al. ( 2020 ) developed a system to provide first year biology students with an automated personalized feedback system tailored to the students’ specific demographics, attributes, and academic status. With the unique feature of AIEd being able to analyze multiple data sets involving a variety of different students, AI was used to assess and provide feedback on students’ group work (viz., Ouatik et al., 2021 ).

AI also supports instructors in generating questions and creating multiple question tests (Yang et al., 2021 ). For example, (Lu et al., 2021 ) used natural language processing to create a system that automatically created tests. Following a Turing type test, researchers found that AI technologies can generate highly realistic short-answer questions. The ability for AI to develop multiple questions is a highly valuable affordance as tests can take a great deal of time to make. However, it would be important for instructors to always confirm questions provided by the AI to ensure they are correct and that they match the learning objectives for the class, especially in high value summative assessments.

The axial code within assessment and evaluation revealed that AI was used to review activities in the online space. This included evaluating student’s reflections, achievement goals, community identity, and higher order thinking (viz., Huang et al., 2021 ). Three studies used AIEd to evaluate educational materials. This included general resources and textbooks (viz., Koć‑Januchta et al., 2022 ). It is interesting to see the use of AI for the assessment of educational products, rather than educational artifacts developed by students. While this process may be very similar in nature, this shows researchers thinking beyond the traditional use of AI for assessment to provide other affordances.

Predicting was a common use of AIEd in HE with 21 studies focused specifically on the use of AI for forecasting trends in data. Ten axial codes emerged on the way AI was used to predict different topics, with nine focused on predictions regarding students and the other on predicting the future of higher education. See Fig.  9 .

Predicting axial codes

Extant systematic reviews on HE highlighted the use of AIEd for prediction (viz., Chu et al., 2022 ; Hinojo-Lucena et al., 2019 ; Ouyang et al., 2022 ; Zawacki-Richter et al., 2019 ). Ten of the articles in this study used AI for predicting academic performance. Many of the axial codes were often overlapping, such as predicting at risk students, and predicting dropouts; however, each provided distinct affordances. An example of this is the study by Qian et. al. ( 2021 ). These researchers examined students taking a MOOC course. MOOCs can be challenging environments to determine information on individual students with the vast number of students taking the course (Krause & Lowe, 2014 ). However, Qian et al., used AIEd to predict students’ future grades by inputting 17 different learning features, including past grades, into an artificial neural network. The findings were able to predict students’ grades and highlight students at risk of dropping out of the course.

In a systematic review on AIEd within the K-12 context (viz., Crompton et al., 2022 ), prediction was less pronounced in the findings. In the K-12 setting, there was a brief mention of the use of AI in predicting student academic performance. One of the studies mentioned students at risk of dropping out, but this was immediately followed by questions about privacy concerns and describing this as “sensitive”. The use of prediction from the data in this HE systematic review cover a wide range of AI predictive affordances. students Sensitivity is still important in a HE setting, but it is positive to see the valuable insight it provides that can be used to avoid students failing in their goals.

AI assistant

The studies evaluated in this review indicated that the AI Assistant used to support learners had a variety of different names. This code included nomenclature such as, virtual assistant, virtual agent, intelligent agent, intelligent tutor, and intelligent helper. Crompton et. al. ( 2022 ), described the difference in the terms to delineate the way that the AI appeared to the user. For example, if there was an anthropomorphic presence to the AI, such as an avatar, or if the AI appeared to support via other means, such as text prompt. The findings of this systematic review align to Crompton et. al.’s ( 2022 ) descriptive differences of the AI Assistant. Furthermore, this code included studies that provide assistance to students, but may not have specifically used the word assistance. These include the use of chatbots for student outreach, answering questions, and providing other assistance. See Fig.  10 for the axial codes for AI Assistant.

AI assistant axial codes

Many of these assistants offered multiple supports to students, such as Alex , the AI described as a virtual change agent in Kim and Bennekin’s ( 2016 ) study. Alex interacted with students in a college mathematics course by asking diagnostic questions and gave support depending on student needs. Alex’s support was organized into four stages: (1) goal initiation (“Want it”), (2) goal formation (“Plan for it”), (3) action control (“Do it”), and (4) emotion control (“Finish it”). Alex provided responses depending on which of these four areas students needed help. These messages supported students with the aim of encouraging persistence in pursuing their studies and degree programs and improving performance.

The role of AI in providing assistance connects back to the seminal work of Vygotsky ( 1978 ) and the Zone of Proximal Development (ZPD). ZPD highlights the degree to which students can rapidly develop when assisted. Vygotsky described this assistance often in the form of a person. However, with technological advancements, the use of AI assistants in these studies are providing that support for students. The affordances of AI can also ensure that the support is timely without waiting for a person to be available. Also, assistance can consider aspects on students’ academic ability, preferences, and best strategies for supporting. These features were evident in Kim and Bennekin’s ( 2016 ) study using Alex.

Intelligent tutoring system

The use of Intelligent Tutoring Systems (ITS) was revealed in the grounded coding. ITS systems are adaptive instructional systems that involve the use of AI techniques and educational methods. An ITS system customizes educational activities and strategies based on student’s characteristics and needs (Mousavinasab et al., 2021 ). While ITS may be an anticipated finding in AIED HE systematic reviews, it was interesting that extant reviews similar to this study did not always describe their use in HE. For example, Ouyang et. al. ( 2022 ), included “intelligent tutoring system” in search terms describing it as a common technique, yet ITS was not mentioned again in the paper. Zawacki-Richter et. al. ( 2019 ) on the other hand noted that ITS was in the four overarching findings of the use of AIEd in HE. Chu et. al. ( 2022 ) then used Zawacki-Richter’s four uses of AIEd for their recent systematic review.

In this systematic review, 18 studies specifically mentioned that they were using an ITS. The ITS code did not necessitate axial codes as they were performing the same type of function in HE, namely, in providing adaptive instruction to the students. For example, de Chiusole et. al. ( 2020 ) developed Stat-Knowlab, an ITS that provides the level of competence and best learning path for each student. Thus Stat-Knowlab personalizes students’ learning and provides only educational activities that the student is ready to learn. This ITS is able to monitor the evolution of the learning process as the student interacts with the system. In another study, Khalfallah and Slama ( 2018 ) built an ITS called LabTutor for engineering students. LabTutor served as an experienced instructor in enabling students to access and perform experiments on laboratory equipment while adapting to the profile of each student.

The student population in university classes can go into the hundreds and with the advent of MOOCS, class sizes can even go into the thousands. Even in small classes of 20 students, the instructor cannot physically provide immediate unique personalize questions to each student. Instructors need time to read and check answers and then take further time to provide feedback before determining what the next question should be. Working with the instructor, AIEd can provide that immediate instruction, guidance, feedback, and following questioning without delay or becoming tired. This appears to be an effective use of AIEd, especially within the HE context.

Managing student learning

Another code that emerged in the grounded coding was focused on the use of AI for managing student learning. AI is accessed to manage student learning by the administrator or instructor to provide information, organization, and data analysis. The axial codes reveal the trends in the use of AI in managing student learning; see Fig.  11 .

Learning analytics was an a priori term often found in studies which describes “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (Long & Siemens, 2011 , p. 34). The studies investigated in this systematic review were across grades and subject areas and provided administrators and instructors different types of information to guide their work. One of those studies was conducted by Mavrikis et. al. ( 2019 ) who described learning analytics as teacher assistance tools. In their study, learning analytics were used in an exploratory learning environment with targeted visualizations supporting classroom orchestration. These visualizations, displayed as screenshots in the study, provided information such as the interactions between the students, goals achievements etc. These appear similar to infographics that are brightly colored and draw the eye quickly to pertinent information. AI is also used for other tasks, such as organizing the sequence of curriculum in pacing guides for future groups of students and also designing instruction. Zhang ( 2022 ) described how designing an AI teaching system of talent cultivation and using the digital affordances to establish a quality assurance system for practical teaching, provides new mechanisms for the design of university education systems. In developing such a system, Zhang found that the stability of the instructional design, overcame the drawbacks of traditional manual subjectivity in the instructional design.

Another trend that emerged from the studies was the use of AI to manage student big data to support learning. Ullah and Hafiz ( 2022 ) lament that using traditional methods, including non-AI digital techniques, asking the instructor to pay attention to every student’s learning progress is very difficult and that big data analysis techniques are needed. The ability to look across and within large data sets to inform instruction is a valuable affordance of AIEd in HE. While the use of AIEd to manage student learning emerged from the data, this study uncovered only 19 studies in 7 years (2016–2022) that focused on the use of AIEd to manage student data. This lack of the use was also noted in a recent study in the K-12 space (Crompton et al., 2022 ). In Chu et. al.’s ( 2022 ) study examining the top 50 most cited AIEd articles, they did not report the use of AIEd for managing student data in the top uses of AIEd HE. It would appear that more research should be conducted in this area to fully explore the possibilities of AI.

Gaps and future research

From this systematic review, six gaps emerged in the data providing opportunities for future studies to investigate and provide a fuller understanding of how AIEd can used in HE. (1) The majority of the research was conducted in high income countries revealing a paucity of research in developing countries. More research should be conducted in these developing countries to expand the level of understanding about how AI can enhance learning in under-resourced communities. (2) Almost 50% of the studies were conducted in the areas of language learning, computer science and engineering. Research conducted by members from multiple, different academic departments would help to advance the knowledge of the use of AI in more disciplines. (3) This study revealed that faculty affiliated with schools of education are taking an increasing role in researching the use of AIEd in HE. As this body of knowledge grows, faculty in Schools of Education should share their research regarding the pedagogical affordances of AI so that this knowledge can be applied by faculty across disciplines. (4) The vast majority of the research was conducted at the undergraduate level. More research needs to be done at the graduate student level, as AI provides many opportunities in this environment. (5) Little study was done regarding how AIEd can assist both instructors and managers in their roles in HE. The power of AI to assist both groups further research. (6) Finally, much of the research investigated in this systematic review revealed the use of AIEd in traditional ways that enhance or make more efficient current practices. More research needs to focus on the unexplored affordances of AIEd. As AI becomes more advanced and sophisticated, new opportunities will arise for AIEd. Researchers need to be on the forefront of these possible innovations.

In addition, empirical exploration is needed for new tools, such as ChatGPT that was available for public use at the end of 2022. With the time it takes for a peer review journal article to be published, ChatGPT did not appear in the articles for this study. What is interesting is that it could fit with a variety of the use codes found in this study, with students getting support in writing papers and instructors using Chat GPT to assess students work and with help writing emails or descriptions for students. It would be pertinent for researchers to explore Chat GPT.

Limitations

The findings of this study show a rapid increase in the number of AIEd studies published in HE. However, to ensure a level of credibility, this study only included peer review journal articles. These articles take months to publish. Therefore, conference proceedings and gray literature such as blogs and summaries may reveal further findings not explored in this study. In addition, the articles in this study were all published in English which excluded findings from research published in other languages.

In response to the call by Hinojo-Lucena et. al. ( 2019 ), Chu et. al. ( 2022 ), and Zawacki-Richter et. al. ( 2019 ), this study provides unique findings with an up-to-date examination of the use of AIEd in HE from 2016 to 2022. Past systematic reviews examined the research up to 2020. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged.

The findings show that of the 138 studies examined, research was conducted in six of the seven continents of the world. In extant systematic reviews showed that the US led by a large margin in the number of studies published. This trend has now shifted to China. Another shift in AIEd HE is that while extant studies lamented the lack of focus on professors of education leading these studies, this systematic review found education to be the most common department affiliation with 28% and computer science coming in second at 20%. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for, 72% of the studies focused on students, 17% instructors, and 11% managers.

Grounded coding was used to answer the overarching question of how AIEd was used in HE. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. Assessment and evaluation had a wide variety of purposes, including assessing academic progress and student emotions towards learning, individual and group evaluations, and class based online community assessments. Predicting emerged as a code with ten axial codes, as AIEd predicted dropouts and at-risk students, innovative ability, and career decisions. AI Assistants were specific to supporting students in HE. These assistants included those with an anthropomorphic presence, such as virtual agents and persuasive intervention through digital programs. ITS systems were not always noted in extant systematic reviews but were specifically mentioned in 18 of the studies in this review. ITS systems in this study provided customized strategies and approaches to student’s characteristics and needs. The final code in this study highlighted the use of AI in managing student learning, including learning analytics, curriculum sequencing, instructional design, and clustering of students.

The findings of this study provide a springboard for future academics, practitioners, computer scientists, policymakers, and funders in understanding the state of the field in AIEd HE, how AI is used. It also provides actionable items to ameliorate gaps in the current understanding. As the use AIEd will only continue to grow this study can serve as a baseline for further research studies in the use of AIEd in HE.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Reality versus perception: Restructuring tertiary education and institutional organisational change – a case study

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The purpose of this study was toinvestigate the outcome of restructuring thetertiary system in New South Wales, Australiafive years after its announcement in the late1980s. It was hoped that lessons could belearned to assist policy makers in Nova Scotia,Canada in their attempt to restructure highereducation. Twenty-four senior administratorswere interviewed to collect data on `why' and`how' decisions were made in response to avoluntary restructuring policy. Qualitativedata analysis revealed that (1) voluntaryamalgamations and federations take place whentertiary institutions fear governments willmandate restructuring; (2) restructuring oldestablished institutions is more difficult; (3)personal ambitions of leaders negotiatingmergers play an important role; (4) loosefederations are likely to become morebureaucratic and less efficient; (5)organisational change and development arepoorly understood by senior administrators.

To achieve organisational change, more than onefactor must be present. Congruence betweenthese factors is critical to achieve desiredoutcomes. The data inferred that there is arelationship between leadership, restructuring,managing staff relations, organisationaldevelopment, external pressure for change, andorganisational change. To illustrate thisrelationship, the `primary triad model' wascreated suggesting a holistic approach toachieving desired outcomes. Otherwise,organisational change may be perception ratherthan reality.

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Legitimizing Change in Higher Education: Exploring the Rationales Behind Major Organizational Restructuring

A qualitative exploration of the microfoundations of institutional theory: reflections from higher education context.

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Curri, G. Reality versus perception: Restructuring tertiary education and institutional organisational change – a case study. Higher Education 44 , 133–151 (2002). https://doi.org/10.1023/A:1015521313117

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Redefining steam to steam ∀h (steam for all humanity) in higher education.

1. Introduction

1.1. towards humanity-centred design, 1.2. the exploria project: previous works.

• Great Books
• Introduction to Christianity
• Keys to Contemporary History
• The Church’s social doctrine
• World and modern man
• Anthropology
• History and Society
• Person and Modern World
• History of Culture (Western)
• The Church’s Social Doctrine

2. Purpose of This Article

3. steam ∀h (for all humanity) definition, 3.1. definition of the original steam model.

• Science (S) : It deals with everything that exists naturally and how it is studied. In this way, physics, biology, chemistry, biochemistry, Earth and space sciences and others close to technology, such as biotechnology or biomedicine, are considered areas of scientific education [ 30 ].
• Technology (T) : This is responsible for studying everything that has been created and manufactured by humans [ 30 ]. As a school discipline, technology was the last to reach the educational plans and since it did the connections with mathematics and sciences became evident as they were existing disciplines that supported their appearance, technology being the most transversal subject of all the established disciplines [ 31 ].
• Engineering (E) : According to [ 30 ] Engineering is understood as the “use of creativity and logic, based on mathematics and science which uses technology as an agent to create contributions to the world”. That is, engineering is the use of science and maths for designing new technology [ 32 ].
• Mathematics (M) : Mathematics is the discipline that studies numbers and their operations, the handling of algebraic expressions, analytical geometry, the handling of measurements, data analysis, probability, problem solving, logical reasoning and their communication [ 30 ]. The essence of mathematics is problem solving and this subject is necessary to define, analyse and solve all kinds of real life problems. From an interdisciplinary perspective, mathematics in the STEM field is revealed as the common language to the rest of the fields, the language through which all communications are regulated, defined and understood [ 30 ].
• Art (A) : According to cite Yakman08, this discipline can be divided into several types: language arts, fine arts or plastic arts, physical arts, manual arts, and liberal arts that encompass the social sciences. Several of these arts have been considered independent disciplines in educational systems, such as language arts, social sciences, plastic or physical arts through physical education [ 30 ]. From this point of view, the presence of “art” in the educational world is broad and would not be limited to the plastic and manual arts.

3.2. Redefining the Original STEAM Model in STEAM ∀H, (∀H, for All Humanity)

• Fine arts : Everything regarding topics traditionally covered in “art” classes, such as painting, sculpture, colour theory, and tangible creative expressions.
• Physical arts : Those topics that include personal or collective movement, sports, dance and performance.
• Manual arts : Topics related to particular physical or technical skills needed to manipulate objects.
• Liberal arts : This is the broadest category since it included the social sciences such as sociology, philosophy, psychology, theology, history, civics, politics and where the field of education itself is also included. Ref. [ 5 ] literally defines Liberal arts as Liberal arts (Social): Including Education, History, Phylosophy, Politics, Psychology, Sociology, Theology, Science Technology Society (STS) and more .

3.3. Definition of the STEAM ∀H Model

• Liberal arts : This category includes the social sciences such as sociology, philosophy, psychology, theology, history, civics, politics and where the field of education itself is also included.

4. STEAM ∀H in EXPLORIA

4.1. exploria project in the degree of engineering in industrial design and product development.

• Act I: Shape
• Act II: Volume
• Act III: Colour
• Act IV: Space
• Act V: Structure
• Act VI: Project

4.2. Catholic Social Teaching (CST)

4.3. activities and sessions carried out during module ii.

• Session (mathematics). Maths session in which symmetry and proportions are discussed. In this session, students study the concept of symmetry and proportion in a theoretical way and subsequently by using the application exercises, they motivate the formal study on some artistic representations. These are related to the iconological content of it. The works of study are from the Muslim, Christian, Egyptian and contemporary periods. The study of rotational symmetries from the iconographic sense is very important, using the concept of vanishing point and repetition of motifs, in order to build the group of isometries, as well as to investigate in the iconological understanding of the work.
• Session (shape representation). Dedicated to discovering the proportions behind the human body, golden number, to draw it beautifully proportioned.
• Session (basic design): Here we will understand, analyse, apply and create the shape in two dimensions according to its size, proportion, visual weight, and how to compose on basic reticles and relate to others through various interrelations—intersection, overlapping, penetration, etc.—aiming to obtain a balanced and aesthetic result; for which the isometric transformations linked to mathematical calculations will be used in subsequent sessions.
• Session (physics). Physics session in which measurement units and errors are discussed. An experiment is performed to measure the golden number or divine proportion by measuring the proportions of the phalanges of the fingers and arm. The results obtained are pooled and the measurement and error of the whole class experiment is obtained.
• Session (shape representation). The human face, beautiful and well structured, is governed by a series of parameters, proportions and relationships, including the golden number, among the various elements that compose it. In this kind of drawing task they are discovered and applied.
• Session (basic design + shape representation). The link between identity and shape is transmitted. Whenever a two-dimensional form must convey a message, contain a meaning/s or represent a brand or person, its utility and aesthetic result -beauty- will lie in the simplicity and ease in which it integrates different concepts, characteristics or qualities. In this session the students analyse themselves to create a personal brand, a logo that identifies them, a shape justified with their personality and essence. In subsequent sessions and other subjects, the student will discover the style and characteristics of the person and work of Antonio Gaudí, and identity with shape will be linked again. The students will have to recognize the organic and geometric shapes that identify this author and they must also try to understand the balance, proportion and beauty resulting from the inspiration of the author in nature.
• Session (CST and basic design). After the initial learning about the elements that define a space, their organization and relationship, as well as the application of them in various practical exercises; in this two-day joint session (8 h in total), the student is shown the concept of universal accessibility (which includes physical and cognitive accessibility), as well as being shown the reality of diversity in society and vulnerabilities in youth to, from all this information, analyse the space of our school (The ESET Technical School of Engineering) and propose, through the design, improvements that allow the Universal Accessibility of our school for all users and visitors.
• Session (mathematics). Mathematics session in which we talk about Gaudí and the Holy Family where we can observe different geometries introduced by him.
• Session (history of art): The divine proportion in Art. In this session, one of the main questions of aesthetics is worked through case studies: what is beauty and what are its qualities and essence? The observation and imitation of nature has been essential in most of history of art. In addition, since classical Greece, an idea has been maintained regarding beauty, showing that it is intimately linked to proportion. If this is so, and that proportion has been observed in nature, then a suggestive question may arise in the classroom: Do you think that God, through the use of a relationship of proportions in nature itself, is giving an answer as a mathematical formula to the crucial question on beauty that philosophers and artists have been raising for centuries? [ 34 ]. Through the proposed cases, works by artists such as M.C. Escher, Salvador Dalí or photographs by Henri Cartier-Bresson, students reflect on the concept of beauty and its observation in nature and its rules.
• Session (shape representation). Drawing as a tool for understanding Nature. In the first session of Act II: Volume, we analyse the conical perspective as the most realistic and natural technique for drawing what the human eye can see; this technique, far from the artifices of isometric perspectives and, above all, knightly.
• Session (shape representation). The leitmotiv of this session is the understanding of the behaviour of light in Nature, to then apply it in our drawings until achieving the feeling of volume in the elements represented.
• Session (CST). CST session in which we talk about Gaudí and the Holy Family.
• Session 4. Physics session in which we talk about Gaudí and the Holy Family. We talk about the importance of the shape of the arches, their height versus the lateral reinforcements, the friction columns and the double helix columns that Gaudí introduced. We will also study fractal structures and their physical properties. Much emphasis is placed on what was Gaudí’s main source of inspiration, nature. Famous is his phrase, “originality consists in the return to the origin; so, original is all that returns to the simplicity of the first solutions”.
• Session (descriptive geometry). In the descriptive geometry session we explain the warped ruled surfaces, including the hyperbolic paraboloid, its mathematical and physical properties. This session connects to The Pringles equilibrium challenge activity from our previous work [ 27 ].
• Session (physics). A master class in physics explaining Newton’s 3 Laws. Upon completion of the master lesson, students go out to the streets and select 4 applications of Newton’s laws in architecture, take a photo, and explain how laws work in that particular situation.
• Session (basic design). The concept of structure as an integral part of the project. Structure and design. In this session the student is given the definition and basic behaviour of the structure, as well as the possibilities in terms of its typology. According to its origin, we make a difference between technical structure (the one built by man) and natural structure (the one that comes from nature itself); being aware that for techniques, man is completely inspired by the natural world, both in the physical and aesthetic sphere. Science and technology arise from the exploration of nature. In it there are perfect structures like those of the radiolar ones, which are marine protozoa endowed with an internal skeleton with a very elaborate structure and of great beauty. We observe and analyse works by various designers who have been inspired by this type of natural structures.
• Session (physics). A master class in physics that explains the concepts of a free-body diagram and the equilibrium equations for a rigid body. After the conclusion, the students go out to the street and select 4 applications of the equilibrium equations in design products. They take a photo, draw the free-body diagram, and calculate the equilibrium equations in that particular application. This session is connected to The Pringles equilibrium challenge activity from our previous work [ 27 ].
• Session (CST). Master class on the existence of God from the Catholic point of view. We will talk about St. Thomas Aquinas’ 5 ways to defend the existence of God. Of the five ways, we will use the one that connects with the physics part. The First Way is deduced from the movement of objects. Thomas explains through the distinction of act and power, that the same entity cannot move and be moved at the moment, then everything that moves is done by virtue of another element. A series of movers is therefore initiated, and this series cannot be taken to infinity, because there would be no first mover, nor second (i.e., there would be no transmission of movement) therefore there must be a First Unmoved Mover that is identified with God, the beginning of everything. In addition, the thoughts of several scientists who have expressed the relationship between science and religion such as G. Mendel, Einstein, Newton, Faraday, Pasteur, Copernicus, Ada Lovelace or Florence Nightingale, among others, are studied.
• Session (physics). Theoretical session in which a review is made of the relationship between physicists and God throughout history and how physicists have tried to decipher “Who is God” . The session takes a historical journey from Aristotle to the most modern theories of quantum physics, the theory of everything, string theory, etc. Throughout the journey, there are several key moments that connect with learnings from other sessions, these are: - Aristotle: The first thing we talk about is the First Unmoved Mover . The first person to speak of the First Unmoved Mover was Aristotle, not Thomas Aquinas. The unmoved mover is a metaphysical concept described by Aristotle as the first cause of all motion in the universe, and which is therefore not moved by anything else. Aristotle speaks in his eighth book of Physics of an immaterial entity that is the physical principle of the world, and in Metaphysics, he referred to it as God. - Newton: Newton and his 3 fundamental laws are then discussed. Newton’s first law is directly related to the First Unmoved Mover since the first law dictates that: “every object will remain at rest or in uniform motion in a straight line unless compelled to change its state by the action of an external force” . This first law is strictly necessary for Newton’s second and third Law to be fulfilled. In other words, Newton’s laws can be applied except at the origin of the universe as there had to be a first mover on which these rules were not applied. Newton spent much of his life seeking the answer to this question in religion, as he was an Arianist. - Einstein : In other part we talk about Einstein and who was God for him . Einstein was asked many times if he believed in God but he never used to answer that question. The few times he did answer by saying “I believe in Spinoza’s God that reveals himself in the regulated harmony of all that exists, but not in a God that is concerned with the destiny and acts of humanity” . Spinoza was a Dutch philosopher considered one of the three great rationalists of seventeenth-century philosophy. He is considered an absolute rationalist because of his conception of the principle of sufficient reason: “for everything there is a cause, both for its existence, if it exists, and for its non-existence, if it does not exist” . As far as Einstein was concerned, God’s “ruled harmony” is established through the cosmos by strict adherence to the physical principles of cause and effect. Therefore, there is no room in Einstein’s philosophy for free will: “Everything is determined, the beginning as much as the end, by forces over which we have no control… we all dance a mysterious tune, intoned in a distance by an invisible musician” . Everything is governed by the rules of physics and there can be no higher entity that can bypass them. - Born and Einstein : Next we talk about Max Born. He was a German physicist and mathematician who played a decisive role in the development of quantum mechanics. On 4 December 1926, Einstein wrote in a letter to Born: “Quantum mechanics is certainly imposing. But an inner voice tells me that this is not yet the real thing. ’The theory produces a good deal but hardly brings us closer to the secret of The Old One’, I, in any case, am convinced that “God” does not play dice.” Einstein was not a believer, but he used the metaphor of God to refer to the functioning of nature and its rules. - The Old One : At this point in the class two concepts are presented: * “The Old One” : As it was for Einstein, nature and its rules, and as these, the rules of nature have inspired works of art, architects as for example Gaudí, or product design. Geometric shapes of nature such as spirals, fractal growth patterns such as those of trees, symmetries, proportions (golden number), the concept of beauty (symmetry + proportion), and finally, the very laws of physics applied to the design of products, such as forces, gravity, etc. * “Playing the Old One” : This concept refers to the work of a designer who must propose creative ideas, “playing with the Old One’s rules” , that is, playing with the laws of physics and nature. Figure 3 shows a classic example in which the table on the left is a typical table with 4 legs while the other two tables are tables designed in which the designer has “played with the Old Man’s rules” to get an original table that complies with the rules of physics to ensure functionality. At the end of the class, students are presented with the work they must do on “Who is God?” . The work should contain the following points: - An introduction that includes a description explaining the Unmoved Mover theory, the five ways of St. Aquinas, Newton’s Laws, and the theory of everything. - A historical/bibliographic review of different physicists/philosophers who throughout history have studied the origin, both from the physical and religious point of view. - Who’s the Old One? It must include who “the Old One” is and the implications it has on product design based on examples of designers. - Who is God? Personal reflection on “Who is God?” from all points of view based on everything read/learned.
• Session (physics + basic design extension): In this combined session that had 3 sessions of 2 h between physics and basic design extension, the students were asked to select a famous designer and one of his/her products. After having begun to understand the concept of structure, as well as the basic elements of its behaviour, they investigated about different designers, studies and products—in particular furniture, to, among all of them, select a piece of furniture and analyse it to identify which elements integrate it and how its shape and volume are structured. In this way the objective is to understand that each product conforms to the human scale and has an adequate and proportional size to the function for which it is designed; integrating a structure that supports and conforms it. Regarding the basic design part, they are asked, after the research, first to select a piece of furniture whose structure has been used as a functional, aesthetic or differentiating element. Later, the analysis of the piece will consist of inquiring about his/her designer, his/her product line, the brand that produces it, the city where and year when it was launched, as well as the description of the furniture itself: concept, use, shape, finish and materials that integrate it, size—technical drawings-, components/parts that integrate it; and a final essay on the role of the structure in that design and why it is relevant. Regarding physics, they are asked to make the diagram of free bodies of the design and analyse how the “rules of the Old One” are applied and how the designer has “played to be the Old One” , that is, how he/she has played with the rules of physics and nature to transform the initial concept of design into a final product and what things he/she has had to assume to achieve it. For example, in Figure 4 you can see a design of the Italian design studio based in Milan Studiopepe called Verre Particulier where from the original design formed by simple figures, cylinders, the designer has made the decision to cut the base of the cylinder in order to improve the stability of the table and avoid vibrations when the table is used; as well as integrate, with that brief gesture, aesthetics and proportion in the whole of the furniture.
• Phase 1—Definition of the challenge. The teams, based on the proposed theme, must define the challenge they intend to solve. The purpose at this stage is to state what they want to solve, without having yet formalized a specific element or object.
• Phase 2—Research. The teams investigate the challenge given looking for specific solutions, transferring the idea to a shaped solution, and sketching the solution to be proposed while working on its possibilities of volume, colour or structure.
• Phase 3—Performance This phase begins with an Elevator Pitch in which the team members explain their challenge and proposed idea in 2 min. The team of teachers advise/guide them in the project. Then the teams continue to work on the challenge.
• Phase 4—Documentation. In this phase the students document the project, prepare the model and presentation/defence for the next day.
• Phase 5—Presentation. The teams present their projects to the rest of the teams and teachers and are evaluated by both the teachers and the other teams.
• Milestone V. Community of Alfara The CEU Cardenal Herrera University and in particular the Technical School of Engineering (ESET) are located in a Valencian town called Alfara del Patriarca. It is a small town, the town size is 2 km 2 and is located in the area Huerta de Valencia, it has 3301 inhabitants (2021). The local economy has traditionally depended on agriculture with 100% of irrigated crops. The majority of its inhabitants are elderly people who live with students of the University. During Phase 1 of the Landmark, the students undertook a guided tour of the town to see first-hand how it is and the possible needs the community of Alfara may have. After that visit, the teams started developing their proposals. Figure 5 shows one of the projects presented in which a space was proposed where both elderly and young people, basically from the university, shared a space and conversations.
• Milestone VI. SDG Project (Sustainable Development Goal) The second value-related milestone was number VI. In this case, the students had to develop solutions that would improve some of the sustainable development goals. In this case, the project was carried out individually and had a duration of two weeks. At the end of the two weeks, a public presentation was held in which the students presented a panel and the model of their product, see Figure 6 . The presentation was open to the general public and they could vote which of the products presented seemed best to them. In Figure 7 you can see the project that gathered the most votes, Depoocar , a system to transport water in the third world where, by using the physics present in the cars pulled by mules, large radius wheels full of water that are very easy to transport are designed.

5. Materials and Methods

5.1. participants, 5.2. scope of application.

• 85% of students agree or strongly agree that the EXPLORIA methodology has allowed them to connect mathematics knowledge with product design and development.
• Regarding physics, 86% of the students agreed or strongly agreed that the EXPLORIA methodology had allowed them to connect with product design.
• As for the Church Social Doctrine, 67% of the students agreed or strongly agreed that EXPLORIA had helped them connect with product design while 28% remained in a neutral position. Only 16% of the students disagreed and thought that EXPLORIA had not been able to connect CST with product design.
• For drawing, 80% of them agreed or strongly agreed on the usefulness of EXPLORIA to connect with the design.
• Regarding the Milestones, 93% of the students showed that they agreed or strongly agreed on their usefulness in connecting the subjects with the design of products.
• For the metaphors of “the rules of the Old One” or “playing at being the Old One” , 76% of students agree or strongly agree that it has helped them to value the technical part in the design and development of products. 18% are neutral while 6% disagree and cannot find it useful.
• As for whether the metaphors have been useful to assess the importance of nature as a source of inspiration, 77% agree or strongly agree, 21% are indifferent while only 2% cannot find it useful.
• Regarding question 9, if the lessons of the different subjects that have followed the EXPLORIA methodology have allowed me to understand the basic concepts that the design and development of products must contain, (aesthetics, technique, values); practically all the students agree or strongly agree, only one student has shown indifference and nobody has shown disagreement.

7. Discussion

8. conclusions and further developments, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, abbreviations.

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Montés, N.; Barquero, S.; Martínez-Carbonell, A.; Aloy, P.; Ferrer, T.; Romero, P.D.; Millan, M.; Salazar, A.d.S. Redefining STEAM to STEAM ∀H (STEAM for All Humanity) in Higher Education. Educ. Sci. 2024 , 14 , 888. https://doi.org/10.3390/educsci14080888

Montés N, Barquero S, Martínez-Carbonell A, Aloy P, Ferrer T, Romero PD, Millan M, Salazar AdS. Redefining STEAM to STEAM ∀H (STEAM for All Humanity) in Higher Education. Education Sciences . 2024; 14(8):888. https://doi.org/10.3390/educsci14080888

Montés, Nicolás, Sara Barquero, Alfonso Martínez-Carbonell, Paula Aloy, Teresa Ferrer, Pantaleón David Romero, Manuel Millan, and Arturo del Saz Salazar. 2024. "Redefining STEAM to STEAM ∀H (STEAM for All Humanity) in Higher Education" Education Sciences 14, no. 8: 888. https://doi.org/10.3390/educsci14080888

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