Integrating GenAI with Live Case Studies: Advancing Experiential Learning in Operations Management Education
Journal
2026 IEEE Engineering Education World Conference (EDUNINE)
Publisher
IEEE
Date Issued
2026
Author(s)
Vilalta-Perdomo, Eliseo
Palma-Mendoza, Jaime Alberto
Type
text::conference output::conference proceedings
Abstract
This paper examines how Generative Artificial Intelligence (GenAI) can be combined with live case studies to improve experiential learning in operations management education. GenAI can transform education through advanced human-like text generation capabilities, but raises concerns regarding pedagogy, ethics, and academic integrity. This research introduces a framework that combines Kolb's experiential learning theory with live case studies, utilising GenAI as a, agent to learn with to foster student involvement in practical situations. This framework was illustrated using a case study approach in a postgraduate module on operations management. Students used GenAI tools to navigate a live case study to upscale operations for a UK-based ethnic restaurant, illustrating the approach's potential. The findings suggest that students appreciated GenAI's contribution to tackling intricate problems and considered the live case study to be captivating. This study contributes a pedagogical framework for integrating GenAI into experiential learning. However, the limitations include the use of case study research, moderate student participation, and the complexity of live case studies. Future studies should overcome these limitations and explore how GenAI affects learning outcomes. ©The authors © IEEE.
License
Acceso Restringido
How to cite
Salinas-Navarro, D. E., Vilalta-Perdomo, E., & Palma-Mendoza, J. A. (2026). Integrating GenAI with Live Case Studies: Advancing Experiential Learning in Operations Management Education. In 2026 IEEE Engineering Education World Conference (EDUNINE) (pp. 1–6). IEEE. 2026 IEEE Engineering Education World Conference (EDUNINE). https://doi.org/10.1109/edunine62390.2026.11547092
Table of contents
I. Introduction -- II. Theoretical Background -- III. Methodology -- IV. Results -- V. Discussion.