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    Item type:Publication,
    Live Case Studies in Industrial Engineering Education for Experiential Learning and Authentic Assessment
    (MDPI AG, 2026)
    Salinas-Navarro, David Ernesto  
    ;
    Palma-Mendoza, Jaime Alberto
    ;
    Da Silva-Ovando, Agatha Clarice
    Live case studies are widely used in higher education to support active learning; however, their pedagogical potential is often limited by weak integration with learning theories and assessments. This research-to-practice study examines the systematic design of live case studies by integrating Kolb’s experiential learning cycle (ELC) and authentic assessment (AA) principles. This paper presents a framework that conceptualises live cases as the learning context, ELC as the learning process, and AA as evaluative logic. The framework is illustrated through a case study of an undergraduate Quality Management module in industrial engineering at a Mexican university, involving 31 final-year students. The study is design-oriented and illustrative, aiming to demonstrate framework enactment rather than evaluating causal effectiveness. Using a case study methodology, the instructional design and enactment were documented using the ADDIE model. Data were obtained from educational artefacts, assessment results, and student feedback surveys. The findings suggest that aligning teaching and assessment activities with the ELC stages and the AA principles effectively supports learning trajectories. This support covers experience, reflection, conceptualisation, and application. Live case studies enabled the integration of multiple assessment methods around shared organisational problems and supported personalised learning through students’ case selection. This study contributes a design logic and operational framework for distributing authentic assessment across Kolb’s experiential learning stages within live case pedagogy. Rather than offering statistical generalisation, the framework serves as a foundation for adaptation and research, emphasising transferability across disciplines, educational levels, and delivery modes. Limitations are acknowledged regarding the conceptual scope, methodological design, and empirical context. © The authors © MDPI.
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    Integrating Generative AI into Live Case Studies for Experiential Learning in Operations Management
    (MDPI AG, 2025)
    Salinas-Navarro, David Ernesto  
    ;
    Vilalta-Perdomo, Eliseo
    ;
    Palma-Mendoza, Jaime Alberto
    ;
    Carlos-Arroyo, Martina
    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. ©Los autores ©MDPI.