Authors : Deep Kakkad

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/4x6djmeh

Scribd : https://tinyurl.com/muyrfh32

DOI : https://doi.org/10.38124/ijisrt/26mar1885

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The case method has anchored business education for over a century. Yet the business environment it was designed for has fundamentally changed: decision cycles have compressed, AI tools are reshaping industries, and learners increasingly expect adaptive, interactive experiences. This paper presents an integrative review of the pedagogical evolution in business education, tracing four eras from traditional case studies through multimedia cases and rule-based simulations to the emerging frontier of AI-powered adaptive learning environments. Drawing on peer-reviewed literature, institutional reports, and practitioner observations from over 700 professional education sessions delivered across leading global institutions, this study maps the trajectory of pedagogical innovation and identifies where the next transition is underway. Practitioner observations are presented as reflective professional evidence and are clearly distinguished from empirical findings throughout. The paper introduces the AIDE Framework (Awareness, Integration, Deployment, Evolution), an original four-stage maturity model designed to help educators and institutions navigate the expansion from case-centric pedagogy toward AI-driven experiential learning. Critically, this paper goes beyond theory: it provides a practical implementation toolkit demonstrating how educators can build AI-powered simulations using widely accessible tools such as ChatGPT, Claude, and Gemini, requiring no programming expertise. Findings suggest that while the case method retains significant value for foundational analytical reasoning, AI-powered simulations unlock capabilities that static methods cannot match, including real-time adaptivity, personalized difficulty calibration, and dynamic multi-stakeholder scenarios. The paper concludes with actionable recommendations for business schools and corporate training programs worldwide.

Keywords : Generative AI, AI Simulations, Adaptive Learning, Business Pedagogy, Case Method, AIDE Framework.

References :

1. D. A. Kolb, Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice Hall, 1984.
2. C. R. Christensen, D. A. Garvin, and A. Sweet, Eds., Education for Judgment: The Artistry of Discussion Leadership. Boston, MA: Harvard Business School Press, 1991.
3. X. Liu and B. Zhong, "Integrating generative artificial intelligence into student learning: A systematic review from a TPACK perspective," Learning and Instruction, vol. 96, 2025.
4. Poets & Quants, "How Harvard Business School uses generative AI in its MBA classrooms," Mar. 2024. [Online]. Available: https://poetsandquants.com/2024/03/13/
5. T. Bridgman, S. Cummings, and C. McLaughlin, "Restating the case: How revisiting the development of the case method can help us think differently about the future of the business school," Academy of Management Learning & Education, vol. 15, no. 4, pp. 724-741, 2016.
6. D. A. Kolb, "Experiential learning theory and the learning style inventory: A reply to Freedman and Stumpf," Academy of Management Review, vol. 6, no. 2, pp. 289-296, 1981.
7. F. De Beule, A. Jaklic, and H. Voss, "Teaching international business through video case studies," AIB Insights, vol. 25, no. 1, 2024.
8. C. Crovini et al., "Enhancing business higher education through simulation-based learning, problem-based learning, and challenge-based learning," Preprints, 2024.
9. M. P. O'Brien, Y. Costin, and W. Hogan, "Beyond AI: How business simulations preserve human-centric learning," in Proc. European Conf. on e-Learning (ECEL), 2025.
10. Y. Xu and B. Zhong, "Exploring the impact of artificial intelligence on business talent development in higher education: A systematic literature review," The International Journal of Management Education, vol. 23, no. 1, 2025.
11. AACSB, "AI-driven simulations build decision-making skills," AACSB Insights, Feb. 2025. [Online]. Available: https://www.aacsb.edu/insights/
12. AACSB, "Management or mastery: How important is AI for MBA students?," AACSB Insights, Mar. 2024. [Online]. Available: https://www.aacsb.edu/insights/
13. X. Wang, R. Huang, M. Sommer, B. Pei, P. Shidfar, M. S. Rehman, A. D. Ritzhaupt, and F. Martin, "The efficacy of artificial intelligence-enabled adaptive learning systems from 2010 to 2022 on learner outcomes: A meta-analysis," Journal of Educational Computing Research, vol. 62, no. 6, pp. 1568-1603, 2024.
14. Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement," Heliyon, vol. 10, no. 21, 2024.
15. A. L. Wright, G. L. Irving, S. Pereira, and J. Staggs, "An instructional innovation that embeds group learning in case teaching: The TaBLE Case Method," Journal of Management Education, vol. 48, no. 2, pp. 203-234, 2024.
16. S. Puri, "Teaching with the case method to build and teach management theory in offline and online courses," Innovations in Education and Teaching International, 2025.
17. I. Fischer, S. Sweeney, and N. Gupta, "Making sense of generative AI for assessments: Contrasting student claims and assessor evaluations," The International Journal of Management Education, vol. 22, no. 3, 2024.
18. M. Gerlich, "AI tools and the erosion of critical thinking: A study on the correlation between generative AI use and cognitive skills," Societies, vol. 15, no. 1, 2025.
19. A. Ellikkal and S. Rajamohan, "AI-enabled personalized learning: Empowering management students for improving engagement and academic performance," VILAKSHAN - XIMB Journal of Management, vol. 22, no. 1, pp. 28-44, 2025.
20. M. P. O'Brien, Y. Costin, and W. Hogan, "Beyond AI: How business simulations preserve human-centric learning," in Proc. European Conf. on e-Learning (ECEL), 2025.
21. L. Chen, P. Chen, and Z. Lin, "Artificial intelligence in education: A review," IEEE Access, vol. 8, pp. 75264-75278, 2020.
22. W. Holmes, M. Bialik, and C. Fadel, Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA: Center for Curriculum Redesign, 2022.
23. R. Whittemore and K. Knafl, "The integrative review: Updated methodology," Journal of Advanced Nursing, vol. 52, no. 5, pp. 546-553, 2005.
24. D. A. Schon, The Reflective Practitioner: How Professionals Think in Action. New York, NY: Basic Books, 1983.
25. M. Schonewille, "Curating truth or simulating thought? The ethics of AI-generated case studies in business education," Journal of Ethics in Entrepreneurship and Technology, 2025.
26. J. Gosen and J. Washbush, "A review of scholarship on assessing experiential learning effectiveness," Simulation & Gaming, vol. 35, no. 2, pp. 270-293, 2004.
27. F. Bellotti, R. Berta, A. De Gloria, and L. Primavera, "Enhancing the educational value of business simulation games," Computers & Education, vol. 59, no. 4, pp. 129-141, 2012.
28. N. Phan, "Bridging the gap: A framework for experiential learning in higher education," Business and Professional Communication Quarterly, 2024.
29. F. De Beule, A. Jaklic, and H. Voss, "Teaching international business through video case studies," AIB Insights, vol. 25, no. 1, 2024.
30. University of South Florida, "How educators are using AI to enhance, not replace, experiential learning," 2025. [Online]. Available: https://www.stpetersburg.usf.edu/