TY - JOUR
T1 - Integrating Generative AI into Live Case Studies for Experiential Learning in Operations Management
AU - Salinas-Navarro, David Ernesto
AU - Vilalta-Perdomo, Eliseo
AU - Palma-Mendoza, Jaime Alberto
AU - Carlos-Arroyo, Martina
N1 - Copyright © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
PY - 2025/12/23
Y1 - 2025/12/23
N2 - This research-to-practice study examines how Generative Artificial Intelligence (GenAI) can be integrated into live case studies to enhance experiential learning in higher education. It explores GenAI’s potential as an agent to learn with scaffolding reflection and engagement and addresses gaps in existing applications that often focus narrowly on content generation. To explore GenAI’s agentive potential, the methodology illustrates this approach in a UK postgraduate operations management module. Students engaged in a live case study of a local ethnic restaurant to refine its business model and operations. The data sources used to examine students’ results included module materials, outputs, and feedback surveys. Thematic analysis was employed to assess how GenAI facilitated experiential learning. The findings suggest that GenAI integration facilitated exploration, reflection, conceptualisation, and experimentation. Students reported that the activity was engaging and relevant, facilitating critical decision-making and understanding of operations management. However, the outcomes varied according to GenAI literacy and student participation. Although GenAI-enriched learning is beneficial, human agency and contextual knowledge remain crucial. Overall, this study integrates GenAI as a cognitive partner throughout Kolb’s ELC. This study offers a transferable framework for active learning, illustrating how technology can enhance critical and reflective learning in authentic educational contexts. However, limitations include uneven student participation and engagement, resource constraints, overreliance on artificial intelligence outputs, differentiated impact on learning outcomes, and a single-case report, which must be addressed before the framework can be scaled up. Future research should test this through multi-case studies while developing GenAI literacy, measuring GenAI impact, and implementing ethical practices in the field.
AB - This research-to-practice study examines how Generative Artificial Intelligence (GenAI) can be integrated into live case studies to enhance experiential learning in higher education. It explores GenAI’s potential as an agent to learn with scaffolding reflection and engagement and addresses gaps in existing applications that often focus narrowly on content generation. To explore GenAI’s agentive potential, the methodology illustrates this approach in a UK postgraduate operations management module. Students engaged in a live case study of a local ethnic restaurant to refine its business model and operations. The data sources used to examine students’ results included module materials, outputs, and feedback surveys. Thematic analysis was employed to assess how GenAI facilitated experiential learning. The findings suggest that GenAI integration facilitated exploration, reflection, conceptualisation, and experimentation. Students reported that the activity was engaging and relevant, facilitating critical decision-making and understanding of operations management. However, the outcomes varied according to GenAI literacy and student participation. Although GenAI-enriched learning is beneficial, human agency and contextual knowledge remain crucial. Overall, this study integrates GenAI as a cognitive partner throughout Kolb’s ELC. This study offers a transferable framework for active learning, illustrating how technology can enhance critical and reflective learning in authentic educational contexts. However, limitations include uneven student participation and engagement, resource constraints, overreliance on artificial intelligence outputs, differentiated impact on learning outcomes, and a single-case report, which must be addressed before the framework can be scaled up. Future research should test this through multi-case studies while developing GenAI literacy, measuring GenAI impact, and implementing ethical practices in the field.
UR - https://www.mdpi.com/2227-7102/16/1/15
U2 - 10.3390/educsci16010015
DO - 10.3390/educsci16010015
M3 - Article
SN - 2227-7102
VL - 16
JO - Education Sciences
JF - Education Sciences
IS - 1
M1 - 15
ER -