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Case Study – Methods, Examples and Guide

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A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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Case Study Research and Applications Design and Methods

• Robert K. Yin - COSMOS Corporation
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Password-protected Instructor Resources include the following:

• An expanded glossary provided by the author in the form of downloadable Briefs.
• Additional tutorials written by the author which correspond to Chapters 1, 2, 3, 5, and 6.
• A selection of author Robert Yin's SAGE journal articles.
• Tables and figures from the book available for download.

“The book is filled with tips to the researcher on how to master the craft of doing research overall and specifically how to account for multi-layered cases.”

“Yin covers all of the basic and advanced knowledge for conducting case study and why they are useful for specific research studies without getting lost in the weeds.”

“The applications enhance the original material because it gives the reader concrete examples.”

“Yin is much more in-depth on case study methods both within a general qualitative text and any other case study text I have seen.”

On demand used as recommendation for basic literature for case study research

An essential reading for people doing case studies.

very thoruogh introduction

Very good introduction to Case Study design. I have used case study approach for my PhD study. I would recommend this book for an indepth understanding of case study design for research projects.

Dr Siew Lee School of Nursing, Midwifery and Paramedic Practice Robert Gordon University, Aberdeen.

The book is a really good introduction to case study research and is full of useful examples. I will recommend as the definitive source for students interested in pursuing this further in their projects.

In our Doctor of Ministerial Leadership (DML), Case Study is the Methodology that is required in this program. Robert Yin's book provides the foundational knowledge needed to conduct research using his Case Study design.

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Preface: Spotlighting "Case Study Research"

Chapter 1: Getting Started

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Case study research and applications : design and methods

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• Foreword / Donald T. Campbell
• Getting started : how to know whether and when to use the case study as a research method
• Designing case studies : identifying your case(s) and establishing the logic of your case study
• Preparing to collect case study evidence : what you need to do before starting to collect case study data
• Collecting case study evidence : the principles you should follow in working with six sources of evidence
• Analyzing case study evidence : how to start your analysis, your analytic choices, and how they work
• Reporting case studies : how and what to compose.

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Applications of Case Study Research

This book by Robert K. Yin aims to help researchers and graduate level students to improve their knowledge and skills in conducting case study research. 21 individual applications of case study research are included that allow readers to see how the theory can be applied in practice. The book also includes sections on case study evaluations, as well as classroom activities. 

Chapter One of this book is available for free download from SAGE Publications. 

“ A. CASE STUDIES AS A RESEARCH (NOT TEACHING) METHOD

An Abbreviated Definition

All case study research starts from the same compelling feature: the desire to derive a(n) (up-)close or otherwise in-depth understanding of a single or small number of “cases,” set in their real-world contexts (e.g., Bromley, 1986, p. 1). The closeness aims to produce an invaluable and deep understanding—that is, an insightful appreciation of the “case(s)”—hopefully resulting in new learning about real-world behavior and its meaning. The distinctiveness of the case study, therefore, also serves as its abbreviated definition:

An empirical inquiry about a contemporary phenomenon (e.g., a “case”), set within its real-world context—especially when the boundaries between phenomenon and context are not clearly evident (Yin, 2009a, p. 18) .

Thus, among other features, case study research assumes that examining the context and other complex conditions related to the case(s) being studied are integral to understanding the case(s).

The in-depth focus on the case(s), as well as the desire to cover a broader range of contextual and other complex conditions, produce a wide range of topics to be covered by any given case study. In this sense, case study research goes beyond the study of isolated variables. As a by-product, and as a final feature in appreciating case study research, the relevant case study data are likely to come from multiple and not singular sources of evidence.” (Yin 2012, p.4)

• Chapter 1. A (Very) Brief Refresher on the Case Study Method
• Chapter 2. Field Notes
• Chapter 3. The Role of Theory in Doing Case Studies
• Chapter 4. Start-Up for a Newly-Appointed Education Leader
• Chapter 5. Citizens on Patrol
• Chapter 6. A Case Study of a Neighborhood Organization
• Chapter 7. A Nutshell Example: The Effect of a Federal Award on a University Computer Science Department
• Chapter 8. Essential Ingredients of Explanatory Case Studies: Three Drug Prevention Examples
• Chapter 9. Transforming a Business Firm Through Strategic Planning
• Chapter 10. Rival Explanations
• Chapter 11. Proposal Processing by Public and Private Universities
• Chapter 12. Case Studies of Transformed Firms
• PART V: Case Study Evaluations
• Chapter 13: Evaluation of a Community Coalition
• Chapter 14. Sheriff’s Combined Auto Theft Task Force
• Chapter 15. Technical Assistance for HIV/AIDS Community Planning

Yin, R. (2012).  Applications of Case Study Research . (Third ed.). SAGE Publications.

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Recognized as one of the most cited methodology books in the social sciences, the Sixth Edition of Robert K. Yin′s bestselling text provides a complete portal to the world of case study research. With the integration of 11 applications in this edition, the book gives readers access to exemplary case studies drawn from a wide variety of academic and applied fields. Ultimately, Case Study Research and Applications will guide students in the successful design and use ofthe case study research method.  

New to this Edition

• Includes 11 in depth applications that show how researchers have implemented case study methods successfully.
• Increases reference to relativist and constructivist approaches to case study research, as well as how case studies can be part of mixed methods projects.
• Places greater emphasis on using plausible rival explanations to bolster case study quality.
• Discusses synthesizing findings across case studies in a multiple case study in more detail
• Adds an expanded list of 15 fields that have text or texts devoted to case study research.
• Sharpens discussion of distinguishing research from non research case studies.
• The author brings to light at least three remaining gaps to be filled in the future:
• how rival explanations can become more routinely integrated into all case study research;
• the difference between case based and variable based approaches to designing and analyzing case studies; and
• the relationship between case study research and qualitative research.

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Recognized as one of the most cited methodology books in the social sciences, the Sixth Edition of Robert K. Yin′s bestselling text provides a complete portal to the world of case study research. With the integration of 11 applications in this edition, the book gives readers access to exemplary case studies drawn from a wide variety of academic and applied fields. Ultimately, Case Study Research and Applications will guide students in the successful use and application of the case study research method.

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Editorial Reviews

"The applications enhance the original material because it gives the reader concrete examples."

"The book is filled with tips to the researcher on how to master the craft of doing research overall and specifically how to account for multi-layered cases."

"Yin is much more in-depth on case study methods both within a general qualitative text and any other case study text I have seen."

"Yin covers all of the basic and advanced knowledge for conducting case study and why they are useful for specific research studies without getting lost in the weeds."

About the Author

Robert K. Yin is President of COSMOS Corporation, an applied research and social science firm.  Over the years, COSMOS has successfully completed hundreds of projects for federal agencies, state and local agencies, and private foundations.

Outside of COSMOS, Dr. Yin has assisted numerous other research groups, helping to train their field teams or to design research studies. The most recent such engagements have been with The World Bank, the Division of Special Education and disAbility Research at George Mason University, the Department of Nursing Research and Quality Outcomes at the Children’s National Health System (Washington, DC), and the School of Education, Southern New Hampshire University.

Dr. Yin has authored over 100 publications, including authoring or editing 11 books (not counting the multiple editions of any given book). Earlier editions of the present book have been translated into eight languages (Chinese, Japanese, Korean, Swedish, Romanian, Italian, Polish, and Portuguese), and a second book on Qualitative Research from Start to Finish (2016) is in its 2nd edition and has been translated into four languages (Chinese, Korean, Swedish, and Portuguese).  Dr. Yin received his B.A. in history from Harvard College (magna cum laude) and his Ph.D. in brain and cognitive sciences from MIT.

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• Publisher ‏ : ‎ SAGE Publications, Inc; 6th edition (September 27, 2017)
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About the author

Robert k. yin.

Robert K. Yin, Ph.D., serves as Chairman of the Board and CEO of COSMOS Corporation, an applied research and social science firm that has been in operation since 1980. Over the years, COSMOS has successfully completed hundreds of projects for government agencies, private foundations, and other entrepreneurial and non-profit organizations. At COSMOS, Dr. Yin actively leads various research projects, including those in which the case study method is used. He has authored numerous books and peer-reviewed articles, including Case Study Research and Applications of Case Study Research. In 1998 he founded the “Robert K. Yin Fund” at M.I.T., which supports seminars on brain sciences, as well as other activities related to the advancement of pre-doctoral students in the Department of Brain and Cognitive Sciences.

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• Published: 16 April 2024

Designing a framework for entrepreneurship education in Chinese higher education: a theoretical exploration and empirical case study

• Luning Shao 1 ,
• Yuxin Miao 2 ,
• Shengce Ren 3 ,
• Sanfa Cai 4 &
• Fei Fan   ORCID: orcid.org/0000-0001-8756-5140 5 , 6  

Humanities and Social Sciences Communications volume  11 , Article number:  519 ( 2024 ) Cite this article

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• Business and management

Entrepreneurship education (EE) has rapidly evolved within higher education and has emerged as a pivotal mechanism for cultivating innovative and entrepreneurial talent. In China, while EE has made positive strides, it still faces a series of practical challenges. These issues cannot be effectively addressed solely through the efforts of universities. Based on the triple helix (TH) theory, this study delves into the unified objectives and practical content of EE in Chinese higher education. Through a comprehensive literature review on EE, coupled with educational objectives, planned behavior, and entrepreneurship process theories, this study introduces the 4H objective model of EE. 4H stands for Head (mindset), Hand (skill), Heart (attitude), and Help (support). Additionally, the research extends to a corresponding content model that encompasses entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate as tools for achieving the objectives. Based on a single-case approach, this study empirically explores the application of the content model at T-University. Furthermore, this paper elucidates how the university plays a role through the comprehensive development of entrepreneurial learning, practices, services, and climate in nurturing numerous entrepreneurs and facilitating the flourishing of the regional entrepreneurial ecosystem. This paper provides important contributions in its application of TH theory to develop EE within the Chinese context, and it provides clear guidance by elucidating the core objectives and practical content of EE. The proposed conceptual framework serves not only as a guiding tool but also as a crucial conduit for fostering the collaborative development of the EE ecosystem. To enhance the robustness of the framework, this study advocates strengthening empirical research on TH theory through multiple and comparative case studies.

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In the era of the knowledge economy, entrepreneurship has emerged as a fundamental driver of social and economic development. As early as 1911, Schumpeter proposed the well-known theory of economic development, wherein he first introduced the concepts of entrepreneurship and creative destruction as driving forces behind socioeconomic development. Numerous endogenous growth theories, such as the entrepreneurial ecosystem mechanism of Acs et al. ( 2018 ), which also underscores the pivotal role of entrepreneurship in economic development, are rooted in Schumpeter’s model. Recognized as a key means of cultivating entrepreneurs and enhancing their capabilities (Jin et al., 2023 ), entrepreneurship education (EE) has received widespread attention over the past few decades, especially in the context of higher education (Wong & Chan, 2022 ).

Driven by international trends and economic demands, China places significant emphasis on nurturing innovative talent and incorporating EE into the essential components of its national education system. The State Council’s “Implementation Opinions on Deepening the Reform of Innovation and Entrepreneurship Education in Higher Education” (hereafter referred to as the report) underscores the urgent necessity for advancing reforms in innovation and EE in higher education institutions. This initiative aligns with the national strategy of promoting innovation-driven development and enhancing economic quality and efficiency. Furthermore, institutions at various levels are actively and eagerly engaging in EE.

Despite the positive strides made in EE in China, its development still faces a series of formidable practical challenges. As elucidated in the report, higher education institutions face challenges such as a delay in the conceptualization of EE, inadequate integration with specialized education, and a disconnect from practical applications. Furthermore, educators exhibit a deficiency in awareness and capabilities, which manifests in a singular and less effective teaching methodology. The shortage of practical platforms, guidance, and support emphasizes the pressing need for comprehensive innovation and EE systems. These issues necessitate collaborative efforts from universities, industry, and policymakers.

Internationally established solutions for the current challenges have substantially matured, providing invaluable insights and guidance for the development of EE in the Chinese context. In the late 20th century, the concept of the entrepreneurial university gained prominence (Etzkowitz et al., 2000 ). Then, entrepreneurial universities expanded their role from traditional research and teaching to embrace a “third mission” centered on economic development. This transformation entailed fostering student engagement in entrepreneurial initiatives by offering resources and guidance to facilitate the transition of ideas into viable entrepreneurial ventures. Additionally, these entrepreneurial universities played a pivotal role in advancing the triple helix (TH) model (Henry, 2009 ). The TH model establishes innovation systems that facilitate knowledge conversion into economic endeavors by coordinating the functions of universities, government entities, and industry. The robustness of this perspective has been substantiated through comprehensive theoretical and empirical investigations (Mandrup & Jensen, 2017 ).

Therefore, this study aims to explore how EE in Chinese universities can adapt to new societal trends and demands through the guidance of TH theory. This research involves two major themes: educational objectives and content. Educational objectives play a pivotal role in regulating the entire process of educational activities, ensuring alignment with the principles and norms of education (Whitehead, 1967 ), while content provides a practical pathway to achieving these objectives. Specifically, the study has three pivotal research questions:

RQ1: What is the present landscape of EE research?

RQ2: What unified macroscopic goals should be formulated to guide EE in Chinese higher education?

RQ3: What specific EE system should be implemented to realize the identified goals in Chinese higher education?

The structure of this paper is as follows: First, we conduct a comprehensive literature review on EE to answer RQ1 , thereby establishing a robust theoretical foundation. Second, we outline our research methodology, encompassing both framework construction and case studies and providing a clear and explicit approach to our research process. Third, we derive the objectives and content model of EE guided by educational objectives, entrepreneurial motivations, and entrepreneurial process theories. Fourth, focusing on a typical university in China as our research subject, we conduct a case study to demonstrate the practical application of our research framework. Finally, we end the paper with the findings for RQ2 and RQ3 , discussions on the framework, and conclusions.

Literature review

The notion of TH first appeared in the early 1980s, coinciding with the global transition from an industrial to a knowledge-based economy (Cai & Etzkowitz, 2020 ). At that time, the dramatic increase in productivity led to overproduction, and knowledge became a valuable mechanism for driving innovation and economic growth (Mandrup & Jensen, 2017 ). Recognizing the potential of incorporating cutting-edge university technologies into industry and facilitating technology transfer and innovation, the US government took proactive steps to enhance the international competitiveness of American industries. This initiative culminated in the enactment of relevant legislation in 1980, which triggered a surge in technology transfer, patent licensing, and the establishment of new enterprises within the United States. Subsequently, European and Asian nations adopted similar measures, promoting the transformation of universities’ identity (Grimaldi et al., 2011 ). Universities assumed a central role in technology transfer, the formation of businesses, and regional revitalization within the knowledge society rather than occupying a secondary position within the industrial community. The conventional one-to-one relationships between universities, companies, and the government evolved into a dynamic TH model (Cai & Etzkowitz, 2020 ). Beyond their traditional roles in knowledge creation, wealth production, and policy coordination, these sectors began to engage in multifaceted interactions, effectively “playing the role of others” (Ranga & Etzkowitz, 2013 ).

The TH model encompasses three fundamental elements: 1) In a knowledge-based society, universities assume a more prominent role in innovation than in industry; 2) The three entities engage in collaborative relationships, with innovation policies emerging as a result of their mutual interactions rather than being solely dictated by the government; and 3) Each entity, while fulfilling its traditional functions, also takes on the roles of the other two parties (Henry, 2009 ). This model is closely aligned with EE.

On the one hand, EE can enhance the effectiveness of TH theory by strengthening the links between universities, industry, and government. The TH concept was developed based on entrepreneurial universities. The emerging entrepreneurial university model integrates economic development as an additional function. Etzkowitz’s research on the entrepreneurial university identified a TH model of academia-industry-government relations implemented by universities in an increasingly knowledge-based society (Galvao et al., 2019 ). Alexander and Evgeniy ( 2012 ) articulated that entrepreneurial universities are crucial to the implementation of triple-helix arrangements and that by integrating EE into their curricula, universities have the potential to strengthen triple-helix partnerships and boost the effectiveness of the triple-helix model.

On the other hand, TH theory also drives EE to achieve high-quality development. Previously, universities were primarily seen as sources of knowledge and human resources. However, they are now also regarded as reservoirs of technology. Within EE and incubation programs, universities are expanding their educational capabilities beyond individual education to shaping organizations (Henry, 2009 ). Surpassing their role as sources of new ideas for existing companies, universities blend their research and teaching processes in a novel way, emerging as pivotal sources for the formation of new companies, particularly in high-tech domains. Furthermore, innovation within one field of the TH influences others (Piqué et al., 2020 ). An empirical study by Alexander and Evgeniy ( 2012 ) outlined how the government introduced a series of initiatives to develop entrepreneurial universities, construct innovation infrastructure, and foster EE growth.

Overview of EE

EE occupies a crucial position in driving economic advancement, and this domain has been the focal point of extensive research. Fellnhofer ( 2019 ) examined 1773 publications from 1975 to 2014, introducing a more closely aligned taxonomy of EE research. This taxonomy encompasses eight major clusters: social and policy-driven EE, human capital studies related to self-employment, organizational EE and TH, (Re)design and evaluation of EE initiatives, entrepreneurial learning, EE impact studies, and the EE opportunity-related environment at the organizational level. Furthermore, Mohamed and Sheikh Ali ( 2021 ) conducted a systematic literature review of 90 EE articles published from 2009 to 2019. The majority of these studies focused on the development of EE (32%), followed by its benefits (18%) and contributions (12%). The selected research also addressed themes such as the relationship between EE and entrepreneurial intent, the effectiveness of EE, and its assessment (each comprising 9% of the sample).

Spanning from 1975 to 2019, these two reviews offer a comprehensive landscape of EE research. The perspective on EE has evolved, extending into multiple dimensions (Zaring et al., 2021 ). However, EE does not always achieve the expected outcomes, as challenges such as limited student interest and engagement as well as persistent negative attitudes are often faced (Mohamed & Sheikh Ali, 2021 ). In fact, the challenges faced by EE in most countries may be similar. However, the solutions may vary due to contextual differences (Fred Awaah et al., 2023 ). Furthermore, due to this evolution, there is a need for a more comprehensive grasp of pedagogical concepts and the foundational elements of modern EE (Hägg & Gabrielsson, 2020 ). Based on the objectives of this study, four specific themes were chosen for an in-depth literature review: the objectives, contents and methods, outcomes, and experiences of EE.

Objectives of EE

The objectives of EE may provide significant guidance for its implementation and the assessment of its effectiveness, and EE has evolved to form a diversified spectrum. Mwasalwiba ( 2010 ) presented a multifaceted phenomenon in which EE objectives are closely linked to entrepreneurial outcomes. These goals encompass nurturing entrepreneurial attitudes (34%), promoting new ventures (27%), contributing to local community development (24%), and imparting entrepreneurial skills (15%). Some current studies still emphasize particular dimensions of these goals, such as fostering new ventures or value creation (Jones et al., 2018 ; Ratten & Usmanij, 2021 ). These authors further stress the significance of incorporating practical considerations related to the business environment, which prompts learners to contemplate issues such as funding and resource procurement. This goal inherently underscores the importance of entrepreneurial thinking and encourages learners to transition from merely being students to developing entrepreneurial mindsets.

Additionally, Kuratko and Morris ( 2018 ) posit that the goal of EE should not be to produce entrepreneurs but to cultivate entrepreneurial mindsets in students, equipping them with methods for thinking and acting entrepreneurially and enabling them to perceive opportunities rapidly in uncertain conditions and harness resources as entrepreneurs would. While the objectives of EE may vary based on the context of the teaching institution, the fundamental goal is increasingly focused on conveying and nurturing an entrepreneurial mindset among diverse stakeholders. Hao’s ( 2017 ) research contends that EE forms a comprehensive system in which multidimensional educational objectives are established. These objectives primarily encompass cultivating students’ foundational qualities and innovative entrepreneurial personalities, equipping them with essential awareness of entrepreneurship, psychological qualities conducive to entrepreneurship, and a knowledge structure for entrepreneurship. Such a framework guides students towards independent entrepreneurship based on real entrepreneurial scenarios.

Various studies and practices also contain many statements about entrepreneurial goals. The Entrepreneurship Competence Framework, which was issued by the EU in 2016, delineates three competency domains: ideas and opportunities, resources and action. Additionally, the framework outlines 15 specific entrepreneurship competencies (Jun, 2017 ). Similarly, the National Content Standards for EE published by the US Consortium encompass three overarching strategies for articulating desired competencies for aspiring entrepreneurs: entrepreneurial skills, ready skills, and business functions (Canziani & Welsh, 2021 ). First, entrepreneurial skills are unique characteristics, behaviors, and experiences that distinguish entrepreneurs from ordinary employees or managers. Second, ready skills, which include business and entrepreneurial knowledge and skills, are prerequisites and auxiliary conditions for EE. Third, business functions help entrepreneurs create and operate business processes in business activities. These standards explain in the broadest terms what students need to be self-employed or to develop and grow a new venture. Although entrepreneurial skills may be addressed in particular courses offered by entrepreneurship faculties, it is evident that business readiness and functional skills significantly contribute to entrepreneurial success (Canziani & Welsh, 2021 ).

Contents and methods of EE

The content and methods employed in EE are pivotal factors for ensuring the delivery of high-quality entrepreneurial instruction, and they have significant practical implications for achieving educational objectives. The conventional model of EE, which is rooted in the classroom setting, typically features an instructor at the front of the room delivering concepts and theories through lectures and readings (Mwasalwiba, 2010 ). However, due to limited opportunities for student engagement in the learning process, lecture-based teaching methods prove less effective at capturing students’ attention and conveying new concepts (Rahman, 2020 ). In response, Okebukola ( 2020 ) introduced the Culturo-Techno-Contextual Approach (CTCA), which offers a hybrid teaching and learning method that integrates cultural, technological, and geographical contexts. Through a controlled experiment involving 400 entrepreneurship development students from Ghana, CTCA has been demonstrated to be a model for enhancing students’ comprehension of complex concepts (Awaah, 2023 ). Furthermore, learners heavily draw upon their cultural influences to shape their understanding of EE, emphasizing the need for educators to approach the curriculum from a cultural perspective to guide students in comprehending entrepreneurship effectively.

In addition to traditional classroom approaches, research has highlighted innovative methods for instilling entrepreneurial spirit among students. For instance, students may learn from specific university experiences or even engage in creating and running a company (Kolb & Kolb, 2011 ). Some scholars have developed an educational portfolio that encompasses various activities, such as simulations, games, and real company creation, to foster reflective practice (Neck & Greene, 2011 ). However, some studies have indicated that EE, when excessively focused on applied and practical content, yields less favorable outcomes for students aspiring to engage in successful entrepreneurship (Martin et al., 2013 ). In contrast, students involved in more academically oriented courses tend to demonstrate improved intellectual skills and often achieve greater success as entrepreneurs (Zaring et al., 2021 ). As previously discussed, due to the lack of a coherent theoretical framework in EE, there is a lack of uniformity and consistency in course content and methods (Ribeiro et al., 2018 ).

Outcomes of EE

Research on the outcomes of EE is a broad and continually evolving field, with most related research focusing on immediate or short-term impact factors. For example, Anosike ( 2019 ) demonstrated the positive effect of EE on human capital, and Chen et al. ( 2022 ) proposed that EE significantly moderates the impact of self-efficacy on entrepreneurial competencies in higher education students through an innovative learning environment. In particular, in the comprehensive review by Kim et al. ( 2020 ), six key EE outcomes were identified: entrepreneurial creation, entrepreneurial intent, opportunity recognition, entrepreneurial self-efficacy and orientation, need for achievement and locus of control, and other entrepreneurial knowledge. One of the more popular directions is the examination of the impact of EE on entrepreneurial intentions. Bae et al. ( 2014 ) conducted a meta-analysis of 73 studies to examine the relationship between EE and entrepreneurial intention and revealed little correlation. However, a meta-analysis of 389 studies from 2010 to 2020 by Zhang et al. ( 2022 ) revealed a positive association between the two variables.

Nabi et al. ( 2017 ) conducted a systematic review to determine the impact of EE in higher education. Their findings highlight that studies exploring the outcomes of EE have primarily concentrated on short-term and subjective assessments, with insufficient consideration of longer-term effects spanning five or even ten years. These longer-term impacts encompass factors such as the nature and quantity of startups, startup survival rates, and contributions to society and the economy. As noted in the Eurydice report, a significant impediment to advancing EE is the lack of comprehensive delineation concerning education outcomes (Bourgeois et al., 2016 ).

Experiences in the EE system

With the deepening exploration of EE, researchers have turned to studying university-centered entrepreneurship ecosystems (Allahar and Sookram, 2019 ). Such ecosystems are adopted to fill gaps in “educational and economic development resources”, such as entrepreneurship curricula. A growing number of universities have evolved an increasingly complex innovation system that extends from technology transfer offices, incubators, and technology parks to translational research and the promotion of EE across campuses (Cai & Etzkowitz, 2020 ). In the university context, the entrepreneurial ecosystem aligns with TH theory, in which academia, government, and industry create a trilateral network and hybrid organization (Ranga & Etzkowitz, 2013 ).

The EE system is also a popular topic in China. Several researchers have summarized the Chinese experience in EE, including case studies and overall experience, such as the summary of the progress and system development of EE in Chinese universities over the last decade by Weiming et al. ( 2013 ) and the summary of the Chinese experience in innovation and EE by Maoxin ( 2017 ). Other researchers take an in-depth look at the international knowledge of EE, such as discussions on the EE system of Denmark by Yuanyuan ( 2015 ), analyzes of the ecological system of EE at the Technical University of Munich by Yubing and Ziyan ( 2015 ), and comparisons of international innovation and EE by Ke ( 2017 ).

In general, although there has been considerable discussion on EE, the existing body of work has not properly addressed the practical challenges faced by EE in China. On the one hand, the literature is fragmented and has not yet formed a unified and mature theoretical framework. Regarding what should be taught and how it can be taught and assessed, the answers in related research are ambiguous (Hoppe, 2016 ; Wong & Chan, 2022 ). On the other hand, current research lacks empirical evidence in the context of China, and guidance on how to put the concept of EE into practice is relatively limited. These dual deficiencies impede the effective and in-depth development of EE in China. Consequently, it is imperative to comprehensively redefine the objectives and contents of EE to provide clear developmental guidance for Chinese higher education institutions.

Research methodology

To answer the research questions, this study employed a comprehensive approach by integrating both literature-based and empirical research methods. The initial phase focused on systematically reviewing the literature related to entrepreneurial education, aiming to construct a clear set of frameworks for the objectives and content of EE in higher education institutions. The second phase involved conducting a case study at T-University, in which the theoretical frameworks were applied to a real-world context. This case not only contributed to validating the theoretical constructs established through the literature review but also provided valuable insights into the practical operational dynamics of entrepreneurial education within the specific university setting.

Conceptual framework stage

This paper aims to conceptualize the objective and content frameworks for EE. The methodology sequence is as follows: First, we examine the relevant EE literature to gain insights into existing research themes. Subsequently, we identify specific research articles based on these themes, such as “entrepreneurial intention”, “entrepreneurial self-efficacy”, and “entrepreneurial approach”, among others. Third, we synthesize the shared objectives of EE across diverse research perspectives through an analysis of the selected literature. Fourth, we construct an objective model for EE within higher education by integrating Bloom’s educational objectives ( 1956 ) and Gagne’s five learning outcomes ( 1984 ), complemented by entrepreneurship motivation and process considerations. Finally, we discuss the corresponding content framework.

Case study stage

To further elucidate the conceptual framework, this paper delves into the methods for the optimization of EE in China through a case analysis. Specifically, this paper employs a single-case approach. While a single case study may have limited external validity (Onjewu et al., 2021 ), if a case study informs current theory and conceptualizes the explored issues, it can still provide valuable insights from its internal findings (Buchanan, 1999 ).

T-University, which is a comprehensive university in China, is chosen as the subject of the case study for the following reasons. First, T-University is located in Shanghai, which is a Chinese international technological innovation center approved by the State Council. Shanghai’s “14th Five-Year Plan” proposes the establishment of a multichannel international innovation collaboration platform and a global innovation cooperation network. Second, T-University has initiated curriculum reforms and established a regional knowledge economy ecosystem by utilizing EE as a guiding principle, which aligns with the characteristics of its geographical location, history, culture, and disciplinary settings. This case study will showcase T-University’s experiences in entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate, elucidating the positive outcomes of this triangular interaction and offering practical insights for EE in other contexts.

The data collection process of this study was divided into two main stages: field research and archival research. The obtained data included interview transcripts, field notes, photos, internal documents, websites, reports, promotional materials, and published articles. In the initial stage, we conducted a 7-day field trip, including visits to the Innovation and Entrepreneurship Institute, the Career Development Centre, the Academic Affairs Office, and the Graduate School. Moreover, we conducted semistructured interviews with several faculty members and students involved in entrepreneurship education at the university to understand the overall state of implementation of entrepreneurship education at the university. In the second stage, we contacted the Academic Affairs Office and the Student Affairs Office at the university and obtained internal materials related to entrepreneurship education. Additionally, we conducted a comprehensive collection and created a summary of publicly available documents, official school websites, public accounts, and other electronic files. To verify the validity of the multisource data, we conducted triangulation and ultimately used consistent information as the basis for the data analysis.

For the purpose of our study, thematic analysis was employed to delve deeply into the TH factors, the objective and content frameworks, and their interrelationships. Thematic analysis is a method for identifying, analyzing, and reporting patterns within data. This approach emphasizes a comprehensive interpretation of the data, as it extracts information from multiple perspectives and derives valuable conclusions through summary and induction (Onjewu et al., 2021 ). Therefore, thematic analysis likely serves as the foundation for most other qualitative data analysis methods (Willig, 2013 ). In this study, three researchers individually conducted rigorous analyses and comprehensive reviews to ensure the accuracy and reliability of the data. Subsequently, they engaged in collaborative discussions to explore their differences and ultimately reach a consensus.

Framework construction

Theoretical basis of ee in universities.

The study is grounded in the theories of educational objectives, planned behavior, and the entrepreneurial process. Planned behavior theory can serve to elucidate the emergence of entrepreneurial activity, while entrepreneurial process theory can be used to delineate the essential elements of successful entrepreneurship.

Theory of educational objectives. The primary goal of education is to assist students in shaping their future. Furthermore, education should directly influence students and facilitate their future development. Education can significantly enhance students’ prospects by imparting specific skills and fundamental principles and cultivating the correct attitudes and mindsets (Bruner, 2009 ). According to “The Aims of Education” by Whitehead, the objective of education is to stimulate creativity and vitality. Gagne identifies five learning outcomes that enable teachers to design optimal learning conditions based on the presentation of these outcomes, encompassing “attitude,” “motor skills,” “verbal information,” “intellectual skills,” and “cognitive strategies”. Bloom et al. ( 1956 ) argue that education has three aims, which concern the cognitive, affective, and psychomotor domains. Gedeon ( 2017 ) posits that EE involves critical input and output elements. The key objectives encompass mindset (Head), skill (hand), attitude (heart), and support (help). The input objectives include EE teachers, resources, facilities, courses, and teaching methods. The output objectives encompass the impacts of the input factors, such as the number of students, the number of awards, and the establishment of new companies. The primary aims of Gedeon ( 2017 ) correspond to those of Bloom et al. ( 1956 ).

Theory of planned behavior. The theory of planned behavior argues that human behavior is the outcome of well-thought-out planning (Ajzen, 1991 ). Human behavior depends on behavioral intentions, which are affected by three main factors. The first is derived from the individual’s “attitude” towards taking a particular action; the second is derived from the influence of “subjective norms” from society; and the third is derived from “perceived behavioral control” (Ajzen, 1991 ). Researchers have adopted this theory to study entrepreneurial behavior and EE.

Theory of the entrepreneurship process. Researchers have proposed several entrepreneurial models, most of which are processes (Baoshan & Baobao, 2008 ). The theory of the entrepreneurship process focuses on the critical determinants of entrepreneurial success. The essential variables of the entrepreneurial process model significantly impact entrepreneurial performance. Timmons et al. ( 2004 ) argue that successful entrepreneurial activities require an appropriate match among opportunities, entrepreneurial teams, resources, and a dynamic balance as the business develops. Their model emphasizes flexibility and equilibrium, and it is believed that entrepreneurial activities change with time and space. As a result, opportunities, teams, and resources will be unbalanced and need timely adjustment.

4H objective model of EE

Guided by TH theory, the objectives of EE should consider universities’ transformational identity in the knowledge era and promote collaboration among students, faculty, researchers, and external players (Mandrup & Jensen, 2017 ). Furthermore, through a comprehensive analysis of the literature and pertinent theoretical underpinnings, the article introduces the 4H model for the EE objectives, as depicted in Fig. 1 .

The 4H objective model of entrepreneurship education.

The model comprises two levels. The first level pertains to outcomes at the entrepreneurial behavior level, encompassing entrepreneurial intention and entrepreneurial performance. These two factors support universities’ endeavors to nurture individuals with an entrepreneurial mindset and potential and contribute to the region’s growth of innovation and entrepreneurship. The second level pertains to fundamentals, which form the foundation of the first level. The article defines these as the 4H model, representing mindset (Head), skill (Hand), attitude (Heart), and support (Help). This model integrates key theories, including educational objectives, the entrepreneurship process, and planned behavior.

First, according to the theory of educational objectives, the cognitive, emotional, and skill objectives proposed by Bloom et al. ( 1956 ) correspond to the key goals of education offered by Gedeon ( 2017 ), namely, Head, Hand, and Heart; thus, going forward, in this study, these three objectives are adopted. Second, according to the theory of planned behavior, for the promotion of entrepreneurial intention, reflection on the control of beliefs, social norms, and perceptual behaviors must be included. EE’s impact on the Head, Hand, and Heart will promote the power of entrepreneurs’ thoughts and perceptual actions. Therefore, this approach is beneficial for enhancing entrepreneurial intentions. Third, according to entrepreneurship process theory, entrepreneurial performance is affected by various factors, including entrepreneurial opportunities, teams, and resources. Consideration of the concepts of Head, Hand, and Heart can enhance entrepreneurial opportunity recognition and entrepreneurial team capabilities. However, as the primary means of obtaining external resources, social networks play an essential role in improving the performance of innovation and entrepreneurship companies (Gao et al., 2023 ). Therefore, an effective EE program should tell students how to take action, connect them with those who can help them succeed (Ronstadt, 1985 ), and help them access the necessary resources. If EE institutions can provide relevant help, they will consolidate entrepreneurial intentions and improve entrepreneurial performance, enabling the EE’s objective to better support the Head, Hand, and Heart.

Content model of EE

EE necessitates establishing a systematic implementation framework to achieve the 4H objectives. Current research on EE predominantly focuses on two facets: one focuses on EE methods to improve students’ skills, and the other focuses on EE outcome measurements, which consider the impact of EE on different stakeholders. Based on this, to foster innovation in EE approaches and enable long-term sustainable EE outcomes, the 4H Model of EE objectives mandates that pertinent institutions provide entrepreneurial learning, entrepreneurial practice, startup services, and a suitable entrepreneurial climate. These components constitute the four integral facets of the content model for EE, as depicted in Fig. 2 .

The content model of entrepreneurship education.

Entrepreneurial learning

Entrepreneurial learning mainly refers to the learning of innovative entrepreneurial knowledge and theory. This factor represents the core of EE and can contribute significantly to the Head component. It can also improve the entrepreneurial thinking ability of academic subjects through classroom teaching, lectures, information reading and analysis, discussion, debates, etc. Additionally, it can positively affect the Hand and Heart elements of EE.

Entrepreneurial practice

Entrepreneurial practice mainly refers to academic subjects comprehensively enhancing their cognition and ability by participating in entrepreneurial activities. This element is also a key component of EE and plays a significant role in the cultivation of the Hand element. Entrepreneurial practice is characterized by participation in planning and implementing entrepreneurial programs, competitions, and simulation activities. Furthermore, it positively impacts EE’s Head, Heart, and Help factors.

Startup services

Startup services mainly refer to entrepreneurial-related support services provided by EE institutions, which include investment and financing, project declaration, financial and legal support, human resources, marketing, and intermediary services. These services can improve the success of entrepreneurship projects. Therefore, they can reinforce the expectations of entrepreneurs’ success and positively impact the Heart, Hand, and Head objectives of EE.

Entrepreneurial climate

The entrepreneurial climate refers to the entrepreneurial environment created by EE institutions and their community and is embodied mainly in the educational institutions’ external and internal entrepreneurial culture and ecology. The environment can impact the entrepreneurial attitude of educated individuals and the Heart objective of EE. Additionally, it is beneficial for realizing EE’s Head, Hand, and Help goals.

Case study: EE practice of T-University

Overview of ee at t-university.

T-University is one of the first in China to promote innovation and EE. Since the 1990s, a series of policies have been introduced, and different platforms have been set up. After more than 20 years of teaching, research, and practice, an innovation and entrepreneurship education system with unique characteristics has gradually evolved. The overall goal of this system is to ensure that 100% of students receive such education, with 10% of students completing the program and 1% achieving entrepreneurship with a high-quality standard. The overall employment rate of 2020 graduates reached 97.49%. In recent years, the proportion of those pursuing entrepreneurship has been more than 1% almost every year. The T-Rim Knowledge-Based Economic Circle, an industrial cluster formed around knowledge spillover from T-University’s dominant disciplines, employs more than 400 T-University graduates annually.

In 2016, T-University established the School of Innovation & Entrepreneurship, with the president serving as its dean. This school focuses on talent development and is pivotal in advancing innovation-driven development strategies. It coordinates efforts across various departments and colleges to ensure comprehensive coverage of innovation and EE, the integration of diverse academic disciplines, and the transformation of interdisciplinary scientific and technological advancements (see Fig. 3 ).

T-University innovation and entrepreneurship education map.

T-University is dedicated to integrating innovation and EE into every stage of talent development. As the guiding framework for EE, the university has established the Innovation and EE sequence featuring “three-dimensional, linked, and cross-university cooperation” with seven educational elements. These elements include the core curriculum system of innovation and entrepreneurship, the “one top-notch and three excellences” and experimental zones of innovation and entrepreneurship talent cultivation model, the four-level “China-Shanghai-University-School” training programs for innovation and entrepreneurship, four-level “International-National-Municipal-University” science and technology competitions, four-level “National-Municipal-University-School” innovation and entrepreneurship practice bases, three-level “Venture Valley-Entrepreneurship Fund-Industry Incubation” startup services and a high-level teaching team with both full-time and part-time personnel.

T-University has implemented several initiatives. First, the university has implemented 100% student innovation and EE through reforming the credit setting and curriculum system. Through the Venture Valley class, mobile class, and “joint summer school”, more than 10% of the students completed the Innovation and EE program. Moreover, through the professional reform pilot and eight professional incubation platforms in the National Science and Technology Park of T-University and other measures, 1% of the students established high-quality entrepreneurial enterprises. Second, the university is committed to promoting the integration of innovation and entrepreneurship and training programs, exploring and practising a variety of innovative talent cultivation models, and adding undergraduate innovation ability development as a mandatory component of the training program. In addition, pilot reforms have been conducted in engineering, medicine, and law majors, focusing on integrating research and education.

T-University has constructed a high-level integrated innovation and entrepreneurship practice platform by combining internal and external resources. This platform serves as the central component in Fig. 3 , forming a sequence of innovation and entrepreneurship practice opportunities, including 1) the On-and-off Campus Basic Practice Platform, 2) the Entrepreneurship Practice Platform with the Integration of Production, Learning, and Research, 3) the Transformation Platform of Major Scientific Research Facilities and Achievements, and 4) the Strategic Platform of the T-Rim Knowledge-Based Economic Circle. All these platforms are accessible to students based on their specific tasks and objectives.

Moreover, the university has reinforced its support for entrepreneurship and collaborated with local governments in Sichuan, Dalian, and Shenzhen to establish off-campus bases jointly. In 2016, in partnership with other top universities in China, the university launched the Innovation and Entrepreneurship Alliance of Universities in the Yangtze River Delta. This alliance effectively brings together government bodies, businesses, social communities, universities, and funding resources in the Yangtze River Delta, harnessing the synergistic advantages of these institutions. In 2018, the university assumed the director role for the Ministry of Education’s Steering Committee for Innovation and Entrepreneurship. Through collaborations with relevant government agencies and enterprises, T-University has continued its efforts to reform and advance innovation and EE, establishing multiple joint laboratories to put theory into practice.

Startup service

In terms of entrepreneurial services, T-University has focused on the employment guidance center and the science and technology Park, working closely with the local industrial and commercial bureaus in the campus area to provide centralized entrepreneurial services. Through entities such as the Shanghai Municipal College Entrepreneurship Guidance Station, entrepreneurship seedling gardens, the science and technology park, and off-campus bases such as the entrepreneurship valley, the university has established a full-cycle service system that is tailored to students’ innovative and entrepreneurial activities, providing continuous professional guidance and support from the early startup stage to maturity.

Notably, the T-University Science and Technology Park has set up nine professional incubation service platforms that cover investment and financing, human resources, entrepreneurship training, project declaration, financial services, professional intermediaries, market promotion, advanced assessment, and the labor union. Moreover, the Technology Park has established a corporate service mechanism for liaison officers, counselors, and entrepreneurship mentors to ensure that enterprises receive comprehensive support and guidance. Through these services, T-University has successfully cultivated numerous high-tech backbone enterprises, such as New Vision Healthcare, Zhong Hui Ecology, Tongjie Technology, Tonglei Civil Engineering, and Tongchen Environmental Protection, which indicates the positive effect of these entrepreneurial services.

T-University places significant emphasis on fostering the entrepreneurial climate, which is effectively nurtured through the T-Rim Knowledge-Based Economic Circle and on-campus entrepreneurship activities. Moreover, T-University is dedicated to establishing and cultivating a dynamic T-Rim Knowledge-Based Economic Circle in strategic alignment with the district government and key agencies. This innovative ecosystem strategically centers around three prominent industrial clusters: the creative and design industry, the international engineering consulting services industry, and the new energy/materials and environmental technology industry. These industrial clusters provide fertile ground for graduates’ employment and entrepreneurial pursuits and have yielded remarkable economic outputs. In 2020, the combined value of these clusters surged to a staggering RMB 50 billion, with 80% of entrepreneurs being teachers, students, or alumni from T-University.

This commitment has led to the establishment of an intricate design industry chain featuring architectural design and urban planning design; it also supports services in automobile design, landscape design, software design, environmental engineering design, art media design, and associated services such as graphic production, architectural modeling, and engineering consulting.

The EE system at T-University

T-University has undertaken a comprehensive series of initiatives to promote EE, focusing on four key aspects: entrepreneurial learning, entrepreneurial practice, startup service, and the entrepreneurial climate. As of the end of 2021, the National Technology Park at T-University has cumulatively supported more than 3000 enterprises. Notably, the park has played a pivotal role in assisting more than 300 enterprises established by college students.

In its commitment to EE, the university maintains an open approach to engaging with society. Simultaneously, it integrates innovative elements such as technology, information, and talent to facilitate students’ entrepreneurial endeavors. Through the synergy between the university, government entities, and the market, EE cultivates a cadre of entrepreneurial talent. The convergence of these talents culminates in the formation of an innovative and creative industry cluster within the region, representing the tangible outcome of the university’s “disciplinary chain—technology chain—industry chain” approach to EE. This approach has gradually evolved into the innovative ecosystem of the T-Rim Knowledge-Based Economic Circle.

Findings and discussion

Unified macroscopic objectives of ee.

To date, a widespread consensus on defining EE in practical terms has yet to be achieved (Mwasalwiba, 2010 ; Nabi et al., 2017 ). Entrepreneurial education should strive towards a common direction, which is reflected in the agreement on educational objectives and recommended teaching methods(Aparicio et al., 2019 ). Mason and Arshed ( 2013 ) criticized that entrepreneurial education should teach about entrepreneurship rather than for entrepreneurship. Therefore, EE should not only focus on singular outcome-oriented aspects but also emphasize the cultivation of fundamental aspects such as cognition, abilities, attitudes, and skills.

This study embarks on a synthesis of the EE-related literature, integrating educational objective theory, planned behavior theory, and entrepreneurial process theory. The 4H model of EE objectives, which consists of basic and outcome levels, is proposed. This model aims to comprehensively capture the core elements of EE, addressing both students’ performance in entrepreneurial outcomes and their development of various aspects of foundational cognitive attributes and skills.

The basic level of the EE objective model includes the 4Hs, namely Head (mindset), Hand (skill), Heart (attitude), and Help (support). First, Head has stood out as a prominent learning outcome within EE over the past decade (Fretschner & Lampe, 2019 ). Attention given to the “Head” aspect not only highlights the development of individuals recognized as “entrepreneurs” (Mitra, 2017 ) but also underscores its role in complementing the acquisition of skills and practical knowledge necessary for initiating new ventures and leading more productive lives (Neck & Corbett, 2018 ).

Second, the Hand aspect also constitutes a significant developmental goal and learning outcome of EE. The trajectory of EE is evolving towards a focus on entrepreneurial aspects, and the learning outcomes equip students with skills relevant to entrepreneurship (Wong & Chan, 2022 ). Higher education institutions should go beyond fundamental principles associated with knowledge and actively cultivate students’ entrepreneurial skills and spirit.

Third, Heart represents EE objectives that are related to students’ psychological aspects, as students’ emotions, attitudes, and other affective factors impact their perception of entrepreneurship (Cao, 2021 ). Moreover, the ultimate goal of EE is to instill an entrepreneurial attitude and pave the way for future success as entrepreneurs in establishing new businesses and fostering job creation (Kusumojanto et al., 2021 ). Thus, the cultivation of this mindset is not only linked to the understanding of entrepreneurship but also intricately tied to the aspiration for personal fulfillment (Yang, 2013 ).

Fourth, entrepreneurship support (Help) embodies the goal of providing essential resource support to students to establish a robust foundation for their entrepreneurial endeavors. The establishment of a comprehensive support system is paramount for EE in universities. This establishment encompasses the meticulous design of the curriculum, the development of training bases, and the cultivation of teacher resources (Xu, 2017 ). A well-structured support system is crucial for equipping students with the necessary knowledge and skills to successfully navigate the complexities of entrepreneurship (Greene & Saridakis, 2008 ).

The outcome level of the EE objective model encompasses entrepreneurial intention and entrepreneurial performance, topics that have been extensively discussed in the previous literature. Entrepreneurial intention refers to individuals’ subjective willingness and plans for entrepreneurial behavior (Wong & Chan, 2022 ) and represents the starting point of the entrepreneurial process. Entrepreneurial performance refers to individuals’ actual behaviors and achievements in entrepreneurial activities (Wang et al., 2021 ) and represents the ultimate manifestation of entrepreneurial goals. In summary, the proposed 4H model of the EE objectives covers fundamental attitudes, cognition, skills, support, and ultimate outcomes, thus answering the question of what EE should teach.

Specific implementable system of EE

To facilitate the realization of EE goals, this study developed a corresponding content model as an implementable system and conducted empirical research through a case university. Guided by the 4H objectives, the content model also encompasses four dimensions: entrepreneurial learning, entrepreneurial practice, startup service, and entrepreneurial climate. Through a detailed exposition of the practical methods at T-university, this study provides support for addressing the question of how to teach EE.

In the traditional EE paradigm, there is often an overreliance on the transmission of theoretical knowledge, which leads to a deficiency in students’ practical experience and capabilities (Kremel and Wetter-Edman, 2019 ). Moreover, due to the rapidly changing and dynamic nature of the environment, traditional educational methods frequently become disconnected from real-world demands. In response to these issues, the approach of “learning by doing” has emerged as a complementary and improved alternative to traditional methods (Colombelli et al., 2022 ).

The proposed content model applies the “learning by doing” approach to the construction of the EE system. For entrepreneurial learning, the university has constructed a comprehensive innovation and EE chain that encompasses courses, experimental areas, projects, competitions, practice bases, and teaching teams. For entrepreneurial practice, the university has built a high-level, integrated innovation and entrepreneurship practice platform that provides students with the opportunity to turn their ideas into actual projects. For startup services, the university has established close collaborative relationships with local governments and enterprises and has set up nine professional incubation service platforms. For the entrepreneurial climate, the university cultivated a symbiotic innovation and EE ecosystem by promoting the construction of the T-Rim Knowledge-Based Economic Circle. Through the joint efforts of multiple parties, the entrepreneurial activities of teachers, students, and alumni have become vibrant and have formed a complete design industry chain and an enterprise ecosystem that coexists with numerous SMEs.

Development of a framework based on the TH theory

Through the exploration of the interactive relationships among universities, governments, and industries, TH theory points out a development direction for solving the dilemma of EE. Through the lens of TH theory, this study developed a comprehensive framework delineating the macroscopic objectives and practical methods of EE, as depicted in Fig. 4 . In this context, EE has become a common undertaking for multiple participants. Therefore, universities can effectively leverage the featured external and internal resources, facilitating the organic integration of entrepreneurial learning, practice, services, and climate. This, in turn, will lead to better achievement of the unified goals of EE.

Practical contents and objectives based on the triple helix theory.

Numerous scholars have explored the correlation between EE and the TH theory. Zhou and Peng ( 2008 ) articulated the concept of an entrepreneurial university as “the university that strongly influences the regional development of industries as well as economic growth through high-tech entrepreneurship based on strong research, technology transfer, and entrepreneurship capability.” Moreover, Tianhao et al. ( 2020 ) emphasized the significance of fostering collaboration among industry, academia, and research as the optimal approach to enhancing the efficacy of EE. Additionally, Ribeiro et al. ( 2018 ) underscored the pivotal role of MIT’s entrepreneurial ecosystem in facilitating startup launches. They called upon educators, university administrators, and policymakers to allocate increased attention to how university ecosystems can cultivate students’ knowledge, skills, and entrepreneurial mindsets. Rather than viewing EE within the confines of universities in isolation, we advocate for establishing an integrated system that encompasses universities, government bodies, and businesses. Such a system would streamline their respective roles and ultimately bolster regional innovation and entrepreneurship efforts.

Jones et al. ( 2021 ) reported that with the widespread embrace of EE by numerous countries, the boundaries between universities and external ecosystems are becoming increasingly blurred. This convergence not only fosters a stronger entrepreneurial culture within universities but also encourages students to actively establish startups. However, these startups often face challenges related to limited value and long-term sustainability. From the perspective of TH theory, each university can cultivate an ecosystem conducive to specialized entrepreneurial activities based on its unique resources and advantages. To do so, universities should actively collaborate with local governments and industries, leveraging shared resources and support to create a more open, inclusive, and innovation-supporting ecosystem that promotes lasting reform and sustainability.

There are two main ways in which this paper contributes to the literature. First, this study applies TH theory to both theoretical and empirical research on EE in China, presenting a novel framework for the operation of EE. Previous research has applied TH theory in contexts such as India, Finland, and Russia, showcasing the unique contributions of TH in driving social innovation. This paper introduces the TH model to the Chinese context, illustrating collaborative efforts and support for EE from universities, industries, and governments through the construction of EE objectives and content models. Therefore, this paper not only extends the applicability of the TH theory globally but also provides valuable insights for EE in the Chinese context.

Second, the proposed conceptual framework clarifies the core goals and practical content of EE. By emphasizing the comprehensive cultivation of knowledge, skills, attitudes, and resources, this framework provides a concrete reference for designing EE courses, activities, and support services. Moreover, the framework underscores the importance of collaborative efforts among stakeholders, facilitating resource integration to enhance the quality and impact of EE. Overall, the conceptual framework presented in this paper serves not only as a guiding tool but also as a crucial bridge for fostering the collaborative development of the EE ecosystem.

While EE has widespread global recognition, many regions still face similar developmental challenges, such as a lack of organized objectives and content delivery methods. This article, grounded in the context of EE in Chinese higher education institutions, seeks to address the current challenges guided by TH theory. By aligning EE with socioeconomic demands and leveraging TH theory, this study offers insights into the overall goals and practical content of EE.

This study presents a 4H objective model of EE comprising two levels. The first level focuses on outcomes related to entrepreneurial behavior, including entrepreneurial intentions and performance, which highlight the practical effects of EE. The second level is built as the foundation of the outcomes and encompasses the four elements of mindset, skill, attitude, and support. This multilayered structure provides a more systematic and multidimensional consideration for the cultivation of entrepreneurial talent. The framework offers robust support for practical instructional design and goal setting. Additionally, the research extends to the corresponding content model, incorporating four elements: entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate. This content model serves as a practical instructional means to achieve EE goals, enhancing the feasibility of implementing these objectives in practice.

Moreover, this study focused on a representative Chinese university, T-University, to showcase the successful implementation of the 4H and content models. Through this case, we may observe how the university, through comprehensive development in entrepreneurial learning, practice, services, and climate, nurtured many entrepreneurs and facilitated the formation of the innovation and entrepreneurship industry cluster. This approach not only contributes to the university’s reputation and regional economic growth but also offers valuable insights for other regions seeking to advance EE.

This study has several limitations that need to be acknowledged. First, the framework proposed is still preliminary. While its application has been validated through a case study, further exploration is required to determine the detailed classification and elaboration of its constituent elements to deepen the understanding of the EE system. Second, the context of this study is specific to China, and the findings may not be directly generalizable to other regions. Future research should investigate the adaptability of the framework in various cultural and educational contexts from a broader international perspective. Finally, the use of a single-case approach limits the generalizability of the research conclusions. Subsequent studies can enhance comprehensiveness by employing a comparative or multiple-case approach to assess the framework’s reliability and robustness.

In conclusion, this study emphasizes the need to strengthen the application of TH theory in EE and advocates for the enhancement of framework robustness through multiple and comparative case studies. An increase in the quantity of evidence will not only generate greater public interest but also deepen the dynamic interactions among universities, industries, and the nation. This, in turn, may expedite the development of EE in China and foster the optimization of the national economy and the overall employment environment.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available. Making the full data set publicly available could potentially breach the privacy that was promised to participants when they agreed to take part, in particular for the individual informants who come from a small, specific population, and may breach the ethics approval for the study. The data are available from the corresponding author on reasonable request.

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Acknowledgements

We express our sincere gratitude to all individuals who contributed to the data collection process. Furthermore, we extend our appreciation to Linlin Yang and Jinxiao Chen from Tongji University for their invaluable suggestions on the initial draft. Special thanks are also due to Prof. Yuzhuo Cai from Tampere University for his insightful contributions to this paper. Funding for this study was provided by the Chinese National Social Science Funds [BIA190205] and the Shanghai Educational Science Research General Project [C2023033].

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All the authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Luning Shao, Yuxin Miao, Sanfa Cai and Fei Fan. The first Chinese outline and draft were written by Luning Shao, Yuxin Miao, and Shengce Ren. The English draft of the manuscript was prepared by Fei Fan. All the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

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Shao, L., Miao, Y., Ren, S. et al. Designing a framework for entrepreneurship education in Chinese higher education: a theoretical exploration and empirical case study. Humanit Soc Sci Commun 11 , 519 (2024). https://doi.org/10.1057/s41599-024-03024-2

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Artificial intelligence and medical education: application in classroom instruction and student assessment using a pharmacology & therapeutics case study

• Kannan Sridharan 1 &
• Reginald P. Sequeira 1  

BMC Medical Education volume  24 , Article number:  431 ( 2024 ) Cite this article

Metrics details

Artificial intelligence (AI) tools are designed to create or generate content from their trained parameters using an online conversational interface. AI has opened new avenues in redefining the role boundaries of teachers and learners and has the potential to impact the teaching-learning process.

In this descriptive proof-of- concept cross-sectional study we have explored the application of three generative AI tools on drug treatment of hypertension theme to generate: (1) specific learning outcomes (SLOs); (2) test items (MCQs- A type and case cluster; SAQs; OSPE); (3) test standard-setting parameters for medical students.

Analysis of AI-generated output showed profound homology but divergence in quality and responsiveness to refining search queries. The SLOs identified key domains of antihypertensive pharmacology and therapeutics relevant to stages of the medical program, stated with appropriate action verbs as per Bloom’s taxonomy. Test items often had clinical vignettes aligned with the key domain stated in search queries. Some test items related to A-type MCQs had construction defects, multiple correct answers, and dubious appropriateness to the learner’s stage. ChatGPT generated explanations for test items, this enhancing usefulness to support self-study by learners. Integrated case-cluster items had focused clinical case description vignettes, integration across disciplines, and targeted higher levels of competencies. The response of AI tools on standard-setting varied. Individual questions for each SAQ clinical scenario were mostly open-ended. The AI-generated OSPE test items were appropriate for the learner’s stage and identified relevant pharmacotherapeutic issues. The model answers supplied for both SAQs and OSPEs can aid course instructors in planning classroom lessons, identifying suitable instructional methods, establishing rubrics for grading, and for learners as a study guide. Key lessons learnt for improving AI-generated test item quality are outlined.

Conclusions

AI tools are useful adjuncts to plan instructional methods, identify themes for test blueprinting, generate test items, and guide test standard-setting appropriate to learners’ stage in the medical program. However, experts need to review the content validity of AI-generated output. We expect AIs to influence the medical education landscape to empower learners, and to align competencies with curriculum implementation. AI literacy is an essential competency for health professionals.

Peer Review reports

Artificial intelligence (AI) has great potential to revolutionize the field of medical education from curricular conception to assessment [ 1 ]. AIs used in medical education are mostly generative AI large language models that were developed and validated based on billions to trillions of parameters [ 2 ]. AIs hold promise in the incorporation of history-taking, assessment, diagnosis, and management of various disorders [ 3 ]. While applications of AIs in undergraduate medical training are being explored, huge ethical challenges remain in terms of data collection, maintaining anonymity, consent, and ownership of the provided data [ 4 ]. AIs hold a promising role amongst learners because they can deliver a personalized learning experience by tracking their progress and providing real-time feedback, thereby enhancing their understanding in the areas they are finding difficult [ 5 ]. Consequently, a recent survey has shown that medical students have expressed their interest in acquiring competencies related to the use of AIs in healthcare during their undergraduate medical training [ 6 ].

Pharmacology and Therapeutics (P & T) is a core discipline embedded in the undergraduate medical curriculum, mostly in the pre-clerkship phase. However, the application of therapeutic principles forms one of the key learning objectives during the clerkship phase of the undergraduate medical career. Student assessment in pharmacology & therapeutics (P&T) is with test items such as multiple-choice questions (MCQs), integrated case cluster questions, short answer questions (SAQs), and objective structured practical examination (OSPE) in the undergraduate medical curriculum. It has been argued that AIs possess the ability to communicate an idea more creatively than humans [ 7 ]. It is imperative that with access to billions of trillions of datasets the AI platforms hold promise in playing a crucial role in the conception of various test items related to any of the disciplines in the undergraduate medical curriculum. Additionally, AIs provide an optimized curriculum for a program/course/topic addressing multidimensional problems [ 8 ], although robust evidence for this claim is lacking.

The existing literature has evaluated the knowledge, attitude, and perceptions of adopting AI in medical education. Integration of AIs in medical education is the need of the hour in all health professional education. However, the academic medical fraternity facing challenges in the incorporation of AIs in the medical curriculum due to factors such as inadequate grounding in data analytics, lack of high-quality firm evidence favoring the utility of AIs in medical education, and lack of funding [ 9 ]. Open-access AI platforms are available free to users without any restrictions. Hence, as a proof-of-concept, we chose to explore the utility of three AI platforms to identify specific learning objectives (SLOs) related to pharmacology discipline in the management of hypertension for medical students at different stages of their medical training.

Study design and ethics

The present study is observational, cross-sectional in design, conducted in the Department of Pharmacology & Therapeutics, College of Medicine and Medical Sciences, Arabian Gulf University, Kingdom of Bahrain, between April and August 2023. Ethical Committee approval was not sought given the nature of this study that neither had any interaction with humans, nor collection of any personal data was involved.

Study procedure

We conducted the present study in May-June 2023 with the Poe© chatbot interface created by Quora© that provides access to the following three AI platforms:

Sage Poe [ 10 ]: A generative AI search engine developed by Anthropic © that conceives a response based on the written input provided. Quora has renamed Sage Poe as Assistant © from July 2023 onwards.

Claude-Instant [ 11 ]: A retrieval-based AI search engine developed by Anthropic © that collates a response based on pre-written responses amongst the existing databases.

ChatGPT version 3.5 [ 12 ]: A generative architecture-based AI search engine developed by OpenAI © trained on large and diverse datasets.

We queried the chatbots to generate SLOs, A-type MCQs, integrated case cluster MCQs, integrated SAQs, and OSPE test items in the domain of systemic hypertension related to the P&T discipline. Separate prompts were used to generate outputs for pre-clerkship (preclinical) phase students, and at the time of graduation (before starting residency programs). Additionally, we have also evaluated the ability of these AI platforms to estimate the proportion of students correctly answering these test items. We used the following queries for each of these objectives:

Specific learning objectives

Can you generate specific learning objectives in the pharmacology discipline relevant to undergraduate medical students during their pre-clerkship phase related to anti-hypertensive drugs?

Can you generate specific learning objectives in the pharmacology discipline relevant to undergraduate medical students at the time of graduation related to anti-hypertensive drugs?

A-type MCQs

In the initial query used for A-type of item, we specified the domains (such as the mechanism of action, pharmacokinetics, adverse reactions, and indications) so that a sample of test items generated without any theme-related clutter, shown below:

Write 20 single best answer MCQs with 5 choices related to anti-hypertensive drugs for undergraduate medical students during the pre-clerkship phase of which 5 MCQs should be related to mechanism of action, 5 MCQs related to pharmacokinetics, 5 MCQs related to adverse reactions, and 5 MCQs should be related to indications.

The MCQs generated with the above search query were not based on clinical vignettes. We queried again to generate MCQs using clinical vignettes specifically because most medical schools have adopted problem-based learning (PBL) in their medical curriculum.

Write 20 single best answer MCQs with 5 choices related to anti-hypertensive drugs for undergraduate medical students during the pre-clerkship phase using a clinical vignette for each MCQ of which 5 MCQs should be related to the mechanism of action, 5 MCQs related to pharmacokinetics, 5 MCQs related to adverse reactions, and 5 MCQs should be related to indications.

We attempted to explore whether AI platforms can provide useful guidance on standard-setting. Hence, we used the following search query.

Can you do a simulation with 100 undergraduate medical students to take the above questions and let me know what percentage of students got each MCQ correct?

Integrated case cluster MCQs

Write 20 integrated case cluster MCQs with 2 questions in each cluster with 5 choices for undergraduate medical students during the pre-clerkship phase integrating pharmacology and physiology related to systemic hypertension with a case vignette.

Write 20 integrated case cluster MCQs with 2 questions in each cluster with 5 choices for undergraduate medical students during the pre-clerkship phase integrating pharmacology and physiology related to systemic hypertension with a case vignette. Please do not include ‘none of the above’ as the choice. (This modified search query was used because test items with ‘None of the above’ option were generated with the previous search query).

Write 20 integrated case cluster MCQs with 2 questions in each cluster with 5 choices for undergraduate medical students at the time of graduation integrating pharmacology and physiology related to systemic hypertension with a case vignette.

Integrated short answer questions

Write a short answer question scenario with difficult questions based on the theme of a newly diagnosed hypertensive patient for undergraduate medical students with the main objectives related to the physiology of blood pressure regulation, risk factors for systemic hypertension, pathophysiology of systemic hypertension, pathological changes in the systemic blood vessels in hypertension, pharmacological management, and non-pharmacological treatment of systemic hypertension.

Write a short answer question scenario with moderately difficult questions based on the theme of a newly diagnosed hypertensive patient for undergraduate medical students with the main objectives related to the physiology of blood pressure regulation, risk factors for systemic hypertension, pathophysiology of systemic hypertension, pathological changes in the systemic blood vessels in hypertension, pharmacological management, and non-pharmacological treatment of systemic hypertension.

Write a short answer question scenario with questions based on the theme of a newly diagnosed hypertensive patient for undergraduate medical students at the time of graduation with the main objectives related to the physiology of blood pressure regulation, risk factors for systemic hypertension, pathophysiology of systemic hypertension, pathological changes in the systemic blood vessels in hypertension, pharmacological management, and non-pharmacological treatment of systemic hypertension.

Can you generate 5 OSPE pharmacology and therapeutics prescription writing exercises for the assessment of undergraduate medical students at the time of graduation related to anti-hypertensive drugs?

Can you generate 5 OSPE pharmacology and therapeutics prescription writing exercises containing appropriate instructions for the patients for the assessment of undergraduate medical students during their pre-clerkship phase related to anti-hypertensive drugs?

Can you generate 5 OSPE pharmacology and therapeutics prescription writing exercises containing appropriate instructions for the patients for the assessment of undergraduate medical students at the time of graduation related to anti-hypertensive drugs?

Both authors independently evaluated the AI-generated outputs, and a consensus was reached. We cross-checked the veracity of answers suggested by AIs as per the Joint National Commission Guidelines (JNC-8) and Goodman and Gilman’s The Pharmacological Basis of Therapeutics (2023), a reference textbook [ 13 , 14 ]. Errors in the A-type MCQs were categorized as item construction defects, multiple correct answers, and uncertain appropriateness to the learner’s level. Test items in the integrated case cluster MCQs, SAQs and OSPEs were evaluated with the Preliminary Conceptual Framework for Establishing Content Validity of AI-Generated Test Items based on the following domains: technical accuracy, comprehensiveness, education level, and lack of construction defects (Table  1 ). The responses were categorized as complete and deficient for each domain.

The pre-clerkship phase SLOs identified by Sage Poe, Claude-Instant, and ChatGPT are listed in the electronic supplementary materials 1 – 3 , respectively. In general, a broad homology in SLOs generated by the three AI platforms was observed. All AI platforms identified appropriate action verbs as per Bloom’s taxonomy to state the SLO; action verbs such as describe, explain, recognize, discuss, identify, recommend, and interpret are used to state the learning outcome. The specific, measurable, achievable, relevant, time-bound (SMART) SLOs generated by each AI platform slightly varied. All key domains of antihypertensive pharmacology to be achieved during the pre-clerkship (pre-clinical) years were relevant for graduating doctors. The SLOs addressed current JNC Treatment Guidelines recommended classes of antihypertensive drugs, the mechanism of action, pharmacokinetics, adverse effects, indications/contraindications, dosage adjustments, monitoring therapy, and principles of monotherapy and combination therapy.

The SLOs to be achieved by undergraduate medical students at the time of graduation identified by Sage Poe, Claude-Instant, and ChatGPT listed in electronic supplementary materials 4 – 6 , respectively. The identified SLOs emphasize the application of pharmacology knowledge within a clinical context, focusing on competencies needed to function independently in early residency stages. These SLOs go beyond knowledge recall and mechanisms of action to encompass competencies related to clinical problem-solving, rational prescribing, and holistic patient management. The SLOs generated require higher cognitive ability of the learner: action verbs such as demonstrate, apply, evaluate, analyze, develop, justify, recommend, interpret, manage, adjust, educate, refer, design, initiate & titrate were frequently used.

The MCQs for the pre-clerkship phase identified by Sage Poe, Claude-Instant, and ChatGPT listed in the electronic supplementary materials 7 – 9 , respectively, and those identified with the search query based on the clinical vignette in electronic supplementary materials ( 10 – 12 ).

All MCQs generated by the AIs in each of the four domains specified [mechanism of action (MOA); pharmacokinetics; adverse drug reactions (ADRs), and indications for antihypertensive drugs] are quality test items with potential content validity. The test items on MOA generated by Sage Poe included themes such as renin-angiotensin-aldosterone (RAAS) system, beta-adrenergic blockers (BB), calcium channel blockers (CCB), potassium channel openers, and centrally acting antihypertensives; on pharmacokinetics included high oral bioavailability/metabolism in liver [angiotensin receptor blocker (ARB)-losartan], long half-life and renal elimination [angiotensin converting enzyme inhibitors (ACEI)-lisinopril], metabolism by both liver and kidney (beta-blocker (BB)-metoprolol], rapid onset- short duration of action (direct vasodilator-hydralazine), and long-acting transdermal drug delivery (centrally acting-clonidine). Regarding the ADR theme, dry cough, angioedema, and hyperkalemia by ACEIs in susceptible patients, reflex tachycardia by CCB/amlodipine, and orthostatic hypotension by CCB/verapamil addressed. Clinical indications included the drug of choice for hypertensive patients with concomitant comorbidity such as diabetics (ACEI-lisinopril), heart failure and low ejection fraction (BB-carvedilol), hypertensive urgency/emergency (alpha cum beta receptor blocker-labetalol), stroke in patients with history recurrent stroke or transient ischemic attack (ARB-losartan), and preeclampsia (methyldopa).

Almost similar themes under each domain were identified by the Claude-Instant AI platform with few notable exceptions: hydrochlorothiazide (instead of clonidine) in MOA and pharmacokinetics domains, respectively; under the ADR domain ankle edema/ amlodipine, sexual dysfunction and fatigue in male due to alpha-1 receptor blocker; under clinical indications the best initial monotherapy for clinical scenarios such as a 55-year old male with Stage-2 hypertension; a 75-year-old man Stage 1 hypertension; a 35-year-old man with Stage I hypertension working on night shifts; and a 40-year-old man with stage 1 hypertension and hyperlipidemia.

As with Claude-Instant AI, ChatGPT-generated test items on MOA were mostly similar. However, under the pharmacokinetic domain, immediate- and extended-release metoprolol, the effect of food to enhance the oral bioavailability of ramipril, and the highest oral bioavailability of amlodipine compared to other commonly used antihypertensives were the themes identified. Whereas the other ADR themes remained similar, constipation due to verapamil was a new theme addressed. Notably, in this test item, amlodipine was an option that increased the difficulty of this test item because amlodipine therapy is also associated with constipation, albeit to a lesser extent, compared to verapamil. In the clinical indication domain, the case description asking “most commonly used in the treatment of hypertension and heart failure” is controversial because the options listed included losartan, ramipril, and hydrochlorothiazide but the suggested correct answer was ramipril. This is a good example to stress the importance of vetting the AI-generated MCQ by experts for content validity and to assure robust psychometrics. The MCQ on the most used drug in the treatment of “hypertension and diabetic nephropathy” is more explicit as opposed to “hypertension and diabetes” by Claude-Instant because the therapeutic concept of reducing or delaying nephropathy must be distinguished from prevention of nephropathy, although either an ACEI or ARB is the drug of choice for both indications.

It is important to align student assessment to the curriculum; in the PBL curriculum, MCQs with a clinical vignette are preferred. The modification of the query specifying the search to generate MCQs with a clinical vignette on domains specified previously gave appropriate output by all three AI platforms evaluated (Sage Poe; Claude- Instant; Chat GPT). The scenarios generated had a good clinical fidelity and educational fit for the pre-clerkship student perspective.

The errors observed with AI outputs on the A-type MCQs are summarized in Table  2 . No significant pattern was observed except that Claude-Instant© generated test items in a stereotyped format such as the same choices for all test items related to pharmacokinetics and indications, and all the test items in the ADR domain are linked to the mechanisms of action of drugs. This illustrates the importance of reviewing AI-generated test items by content experts for content validity to ensure alignment with evidence-based medicine and up-to-date treatment guidelines.

The test items generated by ChatGPT had the advantage of explanations supplied rendering these more useful for learners to support self-study. The following examples illustrate this assertion: “ A patient with hypertension is started on a medication that works by blocking beta-1 receptors in the heart (metoprolol)”. Metoprolol is a beta blocker that works by blocking beta-1 receptors in the heart, which reduces heart rate and cardiac output, resulting in a decrease in blood pressure. However, this explanation is incomplete because there is no mention of other less important mechanisms, of beta receptor blockers on renin release. Also, these MCQs were mostly recall type: Which of the following medications is known to have a significant first-pass effect? The explanation reads: propranolol is known to have a significant first pass-effect, meaning that a large portion of the drug is metabolized by the liver before it reaches systemic circulation. Losartan, amlodipine, ramipril, and hydrochlorothiazide do not have significant first-pass effect. However, it is also important to extend the explanation further by stating that the first-pass effect of propranolol does not lead to total loss of pharmacological activity because the metabolite hydroxy propranolol also has potent beta-blocking activity. Another MCQ test item had a construction defect: “A patient with hypertension is started on a medication that can cause photosensitivity. Which of the following medications is most likely responsible?” Options included: losartan, amlodipine, ramipril, hydrochlorothiazide, hydrochlorothiazide/triamterene. The explanation states that hydrochlorothiazide (HCTZ) can cause photosensitivity, which is increased sensitivity to sunlight that can cause skin rashes and sunburns. Losartan, amlodipine, ramipril, and HCTZ/triamterene are less likely to cause photosensitivity. However, it is irrational to claim that HCTZ/triamterene, one of the wrong options, is less likely to cause photosensitivity. The difficulty index of such test items is likely unacceptable in high-stake tests to make equitable psychometric decisions.

The responses from Sage Poe, Claude-Instant, and ChatGPT on guidance for standard-setting methods are listed in the electronic supplementary materials 13 – 15 . Sage Poe offered this information with the caveat that “the results of this simulation were purely hypothetical and do not reflect the actual performance of any real students”. The simulation estimates of examinees correctly answering test items ranged from 60 to 90% for the mechanism of action, 65–85% for pharmacokinetics, 60–80% for adverse reactions, and 75–90% for clinical indications of antihypertensives.

In contrast, Claude-Instant admitted that “Performing a full simulation with 100 medical students is beyond the scope of what I can do as an AI assistant. I can generate sample MCQ questions and answers, but I do not have access to a population of medical students to administer the questions. A true simulation that administers questions to real students and analyzes the results would require access to a database of medical students, a platform to deliver the questions, and code to analyze the responses. As an AI assistant, I lack these capabilities. While I cannot perform a full simulation, here are some considerations for administering MCQ questions to medical students: provide details on the purpose and instructions for the test to ensure students understand what is expected out of them; consider including some easier questions in addition to more challenging ones to boost students’ confidence and motivation; avoid ambiguous wording and ensure each question has only one unambiguously correct answer option; when analyzing results, look for questions factors that may have impacted performance, like unclear wording or answer options that are too similar. Focusing on weak questions can help improve future tests; compare performance across different student groups to identify areas where more teaching may be needed; provide detailed answer explanations and review sessions after the test to help students learn from any errors and reinforce correct concepts”. However, ChatGPT offered the simulation result estimates for the 20 MCQ on antihypertensive drugs: mechanism of action ranged from 67 to 92%, pharmacokinetics 63–86%, adverse effects 65–82%, and clinical indications 64–89%. Furthermore, it also stated that “Overall, the performance of the students was quite good, with most questions having a response rate of over 70%. However, there were some questions where the projected correct response rate was rather low, such as Question #5 (Mechanism of Action of Hydrochlorothiazide; see Electronic Supplementary Material 12 ) and Question 18 (Indications for Verapamil; see Electronic Supplementary Material 10 ). This may suggest areas where students need more focused education or review.”

We asked AI assistants to generate 20 integrated case cluster MCQs with 2 test items in each cluster with five options for undergraduate medical students in the pre-clerkship phase integrating pharmacology and physiology related to systemic hypertension with a case vignette and the responses by Sage Poe, Claude-Instant, and ChatGPT are listed in the electronic supplementary materials ( 16 – 18 ). In all instances, the test items generated had focused case descriptions in the form of a clinical vignette, and horizontal integration across the pathophysiology of hypertension and pharmacology of antihypertensive drugs. These test items mostly targeted the ‘knows (knowledge)’ or ‘knows how (competence)’ level on Miller’s pyramid and are suitable for assessing the clinical competence of pre-clerkship medical students, especially in an integrated PBL curriculum. Both the AI assistants generated excellent clinical vignettes and themes; however, most of the cluster MCQs by ChatGPT had “None of the above” as an option, which is often considered a test item construction flaw. Notwithstanding these limitations, case cluster integrated test items are valuable for learners to integrate their knowledge of different basic medical sciences and their application to clinical sciences. This integrated approach can be used for both instructional and student assessment purposes to make the course more meaningful. Indeed, one of the basic tenets of PBL is curriculum integration.

In the next query, we asked AI assistants to write integrated case cluster MCQs with 2 test items in each cluster with 5 options for undergraduate medical students at the time of graduation integrating pharmacology and physiology of hypertension with a case vignette. The responses are listed in the electronic supplementary materials 19 – 21 . Claude-Instant has provided only partial responses to both the queries. Overall, the information provided in case vignettes requires the learner to interpret the clinical information and make therapeutic decisions. Such test items are appropriate for assessing the minimum competency of medical school graduates to start internship or residency programs; these MCQs are generally comparable to test items used in USMLE Part 2. The evaluation of the integrated case cluster test items provided by all the AI platforms is summarized in Table  3 . In general, the AI outputs had at least one construction defect, and did not include all the core sub-topics to a sufficient degree.

When asked to simulate the above-generated test items to 100 undergraduate medical students at the time of graduation and provide a percentage of students who are likely to correctly answer each item, the AI assistant responded:

“ As an AI language model, I cannot simulate the responses of 100 undergraduate medical students likely to obtain the correct answer for each question. However, I can provide some general information about the difficulty level of each question based on the complexity of the concepts being tested and the level of integration between pharmacology and physiology ”.

AI assistant went on to clarify that questions that require knowledge of basic pharmacology principles, such as the mechanism of action of specific drugs, are likely to be easier for students to answer correctly. Test items that require an understanding of the physiological mechanisms underlying hypertension and correlating with symptoms are likely to be more challenging for students. The AI assistant sorted these test items into two categories accordingly. Overall, the difficulty level of the test item is based on the level of integration between pharmacology and pathophysiology. Test items that require an understanding of both pharmacological and physiological mechanisms are likely to be more challenging for students requiring a strong foundation in both pharmacology and physiology concepts to be able to correctly answer integrated case-cluster MCQs.

Short answer questions

The responses to a search query on generating SAQs appropriate to the pre-clerkship phase Sage Poe, Claude-Instant, and ChatGPT generated items are listed in the electronic supplementary materials 22 – 24 for difficult questions and 25–27 for moderately difficult questions.

It is apparent from these case vignette descriptions that the short answer question format varied. Accordingly, the scope for asking individual questions for each scenario is open-ended. In all instances, model answers are supplied which are helpful for the course instructor to plan classroom lessons, identify appropriate instructional methods, and establish rubrics for grading the answer scripts, and as a study guide for students.

We then wanted to see to what extent AI can differentiate the difficulty of the SAQ by replacing the search term “difficult” with “moderately difficult” in the above search prompt: the changes in the revised case scenarios are substantial. Perhaps the context of learning and practice (and the level of the student in the MD/medical program) may determine the difficulty level of SAQ generated. It is worth noting that on changing the search from cardiology to internal medicine rotation in Sage Poe the case description also changed. Thus, it is essential to select an appropriate AI assistant, perhaps by trial and error, to generate quality SAQs. Most of the individual questions tested stand-alone knowledge and did not require students to demonstrate integration.

The responses of Sage Poe, Claude-Instant, and ChatGPT for the search query to generate SAQs at the time of graduation are listed in the electronic supplementary materials 28 – 30 . It is interesting to note how AI assistants considered the stage of the learner while generating the SAQ. The response by Sage Poe is illustrative for comparison. “You are a newly graduated medical student who is working in a hospital” versus “You are a medical student in your pre-clerkship.”

Some questions were retained, deleted, or modified to align with competency appropriate to the context (Electronic Supplementary Materials 28 – 30 ). Overall, the test items at both levels from all AI platforms were technically accurate and thorough addressing the topics related to different disciplines (Table  3 ). The differences in learning objective transition are summarized in Table  4 . A comparison of learning objectives revealed that almost all objectives remained the same except for a few (Table  5 ).

A similar trend was apparent with test items generated by other AI assistants, such as ChatGPT. The contrasting differences in questions are illustrated by the vertical integration of basic sciences and clinical sciences (Table  6 ).

Taken together, these in-depth qualitative comparisons suggest that AI assistants such as Sage Poe and ChatGPT consider the learner’s stage of training in designing test items, learning outcomes, and answers expected from the examinee. It is critical to state the search query explicitly to generate quality output by AI assistants.

The OSPE test items generated by Claude-Instant and ChatGPT appropriate to the pre-clerkship phase (without mentioning “appropriate instructions for the patients”) are listed in the electronic supplementary materials 31 and 32 and with patient instructions on the electronic supplementary materials 33 and 34 . For reasons unknown, Sage Poe did not provide any response to this search query.

The five OSPE items generated were suitable to assess the prescription writing competency of pre-clerkship medical students. The clinical scenarios identified by the three AI platforms were comparable; these scenarios include patients with hypertension and impaired glucose tolerance in a 65-year-old male, hypertension with chronic kidney disease (CKD) in a 55-year-old woman, resistant hypertension with obstructive sleep apnea in a 45-year-old man, and gestational hypertension at 32 weeks in a 35-year-old (Claude-Instant AI). Incorporating appropriate instructions facilitates the learner’s ability to educate patients and maximize safe and effective therapy. The OSPE item required students to write a prescription with guidance to start conservatively, choose an appropriate antihypertensive drug class (drug) based on the patients’ profile, specifying drug name, dose, dosing frequency, drug quantity to be dispensed, patient name, date, refill, and caution as appropriate, in addition to prescribers’ name, signature, and license number. In contrast, ChatGPT identified clinical scenarios to include patients with hypertension and CKD, hypertension and bronchial asthma, gestational diabetes, hypertension and heart failure, and hypertension and gout (ChatGPT). Guidance for dosage titration, warnings to be aware, safety monitoring, and frequency of follow-up and dose adjustment. These test items are designed to assess learners’ knowledge of P & T of antihypertensives, as well as their ability to provide appropriate instructions to patients. These clinical scenarios for writing prescriptions assess students’ ability to choose an appropriate drug class, write prescriptions with proper labeling and dosing, reflect drug safety profiles, and risk factors, and make modifications to meet the requirements of special populations. The prescription is required to state the drug name, dose, dosing frequency, patient name, date, refills, and cautions or instructions as needed. A conservative starting dose, once or twice daily dosing frequency based on the drug, and instructions to titrate the dose slowly if required.

The responses from Claude-Instant and ChatGPT for the search query related to generating OSPE test items at the time of graduation are listed in electronic supplementary materials 35 and 36 . In contrast to the pre-clerkship phase, OSPEs generated for graduating doctors’ competence assessed more advanced drug therapy comprehension. For example, writing a prescription for:

(1) A 65-year- old male with resistant hypertension and CKD stage 3 to optimize antihypertensive regimen required the answer to include starting ACEI and diuretic, titrating the dosage over two weeks, considering adding spironolactone or substituting ACEI with an ARB, and need to closely monitor serum electrolytes and kidney function closely.

(2) A 55-year-old woman with hypertension and paroxysmal arrhythmia required the answer to include switching ACEI to ARB due to cough, adding a CCB or beta blocker for rate control needs, and adjusting the dosage slowly and monitoring for side effects.

(3) A 45-year-old man with masked hypertension and obstructive sleep apnea require adding a centrally acting antihypertensive at bedtime and increasing dosage as needed based on home blood pressure monitoring and refer to CPAP if not already using one.

(4) A 75-year-old woman with isolated systolic hypertension and autonomic dysfunction to require stopping diuretic and switching to an alpha blocker, upward dosage adjustment and combining with other antihypertensives as needed based on postural blood pressure changes and symptoms.

(5) A 35-year-old pregnant woman with preeclampsia at 29 weeks require doubling methyldopa dose and consider adding labetalol or nifedipine based on severity and educate on signs of worsening and to follow-up immediately for any concerning symptoms.

These case scenarios are designed to assess the ability of the learner to comprehend the complexity of antihypertensive regimens, make evidence-based regimen adjustments, prescribe multidrug combinations based on therapeutic response and tolerability, monitor complex patients for complications, and educate patients about warning signs and follow-up.

A similar output was provided by ChatGPT, with clinical scenarios such as prescribing for patients with hypertension and myocardial infarction; hypertension and chronic obstructive pulmonary airway disease (COPD); hypertension and a history of angina; hypertension and a history of stroke, and hypertension and advanced renal failure. In these cases, wherever appropriate, pharmacotherapeutic issues like taking ramipril after food to reduce side effects such as giddiness; selection of the most appropriate beta-blocker such as nebivolol in patients with COPD comorbidity; the importance of taking amlodipine at the same time every day with or without food; preference for telmisartan among other ARBs in stroke; choosing furosemide in patients with hypertension and edema and taking the medication with food to reduce the risk of gastrointestinal adverse effect are stressed.

The AI outputs on OSPE test times were observed to be technically accurate, thorough in addressing core sub-topics suitable for the learner’s level and did not have any construction defects (Table  3 ). Both AIs provided the model answers with explanatory notes. This facilitates the use of such OSPEs for self-assessment by learners for formative assessment purposes. The detailed instructions are helpful in creating optimized therapy regimens, and designing evidence-based regimens, to provide appropriate instructions to patients with complex medical histories. One can rely on multiple AI sources to identify, shortlist required case scenarios, and OSPE items, and seek guidance on expected model answers with explanations. The model answer guidance for antihypertensive drug classes is more appropriate (rather than a specific drug of a given class) from a teaching/learning perspective. We believe that these scenarios can be refined further by providing a focused case history along with relevant clinical and laboratory data to enhance clinical fidelity and bring a closer fit to the competency framework.

In the present study, AI tools have generated SLOs that comply with the current principles of medical education [ 15 ]. AI tools are valuable in constructing SLOs and so are especially useful for medical fraternities where training in medical education is perceived as inadequate, more so in the early stages of their academic career. Data suggests that only a third of academics in medical schools have formal training in medical education [ 16 ] which is a limitation. Thus, the credibility of alternatives, such as the AIs, is evaluated to generate appropriate course learning outcomes.

We observed that the AI platforms in the present study generated quality test items suitable for different types of assessment purposes. The AI-generated outputs were similar with minor variation. We have used generative AIs in the present study that could generate new content from their training dataset [ 17 ]. Problem-based and interactive learning approaches are referred to as “bottom-up” where learners obtain first-hand experience in solving the cases first and then indulge in discussion with the educators to refine their understanding and critical thinking skills [ 18 ]. We suggest that AI tools can be useful for this approach for imparting the core knowledge and skills related to Pharmacology and Therapeutics to undergraduate medical students. A recent scoping review evaluating the barriers to writing quality test items based on 13 studies has concluded that motivation, time constraints, and scheduling were the most common [ 19 ]. AI tools can be valuable considering the quick generation of quality test items and time management. However, as observed in the present study, the AI-generated test items nevertheless require scrutiny by faculty members for content validity. Moreover, it is important to train faculty in AI technology-assisted teaching and learning. The General Medical Council recommends taking every opportunity to raise the profile of teaching in medical schools [ 20 ]. Hence, both the academic faculty and the institution must consider investing resources in AI training to ensure appropriate use of the technology [ 21 ].

The AI outputs assessed in the present study had errors, particularly with A-type MCQs. One notable observation was that often the AI tools were unable to differentiate the differences between ACEIs and ARBs. AI platforms access several structured and unstructured data, in addition to images, audio, and videos. Hence, the AI platforms can commit errors due to extracting details from unauthenticated sources [ 22 ] created a framework identifying 28 factors for reconstructing the path of AI failures and for determining corrective actions. This is an area of interest for AI technical experts to explore. Also, this further iterates the need for human examination of test items before using them for assessment purposes.

There are concerns that AIs can memorize and provide answers from their training dataset, which they are not supposed to do [ 23 ]. Hence, the use of AIs-generated test items for summative examinations is debatable. It is essential to ensure and enhance the security features of AI tools to reduce or eliminate cross-contamination of test items. Researchers have emphasized that AI tools will only reach their potential if developers and users can access full-text non-PDF formats that help machines comprehend research papers and generate the output [ 24 ].

AI platforms may not always have access to all standard treatment guidelines. However, in the present study, it was observed that all three AI platforms generally provided appropriate test items regarding the choice of medications, aligning with recommendations from contemporary guidelines and standard textbooks in pharmacology and therapeutics. The prompts used in the study were specifically focused on the pre-clerkship phase of the undergraduate medical curriculum (and at the time of their graduation) and assessed fundamental core concepts, which were also reflected in the AI outputs. Additionally, the recommended first-line antihypertensive drug classes have been established for several decades, and information regarding their pharmacokinetics, ADRs, and indications is well-documented in the literature.

Different paradigms and learning theories have been proposed to support AI in education. These paradigms include AI- directed (learner as recipient), AI-supported (learner as collaborator), and AI-empowered (learner as leader) that are based on Behaviorism, Cognitive-Social constructivism, and Connectivism-Complex adaptive systems, respectively [ 25 ]. AI techniques have potential to stimulate and advance instructional and learning sciences. More recently a three- level model that synthesizes and unifies existing learning theories to model the roles of AIs in promoting learning process has been proposed [ 26 ]. The different components of our study rely upon these paradigms and learning theories as the theoretical underpinning.

Strengths and limitations

To the best of our knowledge, this is the first study evaluating the utility of AI platforms in generating test items related to a discipline in the undergraduate medical curriculum. We have evaluated the AI’s ability to generate outputs related to most types of assessment in the undergraduate medical curriculum. The key lessons learnt for improving the AI-generated test item quality from the present study are outlined in Table  7 . We used a structured framework for assessing the content validity of the test items. However, we have demonstrated using a single case study (hypertension) as a pilot experiment. We chose to evaluate anti-hypertensive drugs as it is a core learning objective and one of the most common disorders relevant to undergraduate medical curricula worldwide. It would be interesting to explore the output from AI platforms for other common (and uncommon/region-specific) disorders, non-/semi-core objectives, and disciplines other than Pharmacology and Therapeutics. An area of interest would be to look at the content validity of the test items generated for different curricula (such as problem-based, integrated, case-based, and competency-based) during different stages of the learning process. Also, we did not attempt to evaluate the generation of flowcharts, algorithms, or figures for generating test items. Another potential area for exploring the utility of AIs in medical education would be repeated procedural practices such as the administration of drugs through different routes by trainee residents [ 27 ]. Several AI tools have been identified for potential application in enhancing classroom instructions and assessment purposes pending validation in prospective studies [ 28 ]. Lastly, we did not administer the AI-generated test items to students and assessed their performance and so could not comment on the validity of test item discrimination and difficulty indices. Additionally, there is a need to confirm the generalizability of the findings to other complex areas in the same discipline as well as in other disciplines that pave way for future studies. The conceptual framework used in the present study for evaluating the AI-generated test items needs to be validated in a larger population. Future studies may also try to evaluate the variations in the AI outputs with repetition of the same queries.

Notwithstanding ongoing discussions and controversies, AI tools are potentially useful adjuncts to optimize instructional methods, test blueprinting, test item generation, and guidance for test standard-setting appropriate to learners’ stage in the medical program. However, experts need to critically review the content validity of AI-generated output. These challenges and caveats are to be addressed before the use of widespread use of AIs in medical education can be advocated.

Data availability

All the data included in this study are provided as Electronic Supplementary Materials.

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Sridharan, K., Sequeira, R.P. Artificial intelligence and medical education: application in classroom instruction and student assessment using a pharmacology & therapeutics case study. BMC Med Educ 24 , 431 (2024). https://doi.org/10.1186/s12909-024-05365-7

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Fatal Traffic Risks With a Total Solar Eclipse in the US

• 1 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
• 2 Evaluative Clinical Science Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
• 3 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
• 4 Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 5 Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada
• 6 Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• 7 Centre for Clinical Epidemiology & Evaluation, University of British Columbia, Vancouver, British Columbia, Canada

A total solar eclipse occurs when the moon temporarily obscures the sun and casts a dark shadow across the earth. This astronomical spectacle has been described for more than 3 millennia and can be predicted with high precision. Eclipse-related solar retinopathy (vision loss from staring at the sun) is an established medical complication; however, other medical outcomes have received little attention. 1

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Redelmeier DA , Staples JA. Fatal Traffic Risks With a Total Solar Eclipse in the US. JAMA Intern Med. Published online March 25, 2024. doi:10.1001/jamainternmed.2023.5234

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