This work integrates the circular economy (CE) into experiential learning in higher education, focusing on industrial and systems engineering. It addresses the need for suitable learning experiences and pedagogical strategies to enhance CE and sustainability education in active learning research. Accordingly, this study proposes integrating Kolb’s experiential learning cycle with the ADDIE model into an instructional design framework for reflective and active engagement in learning activities within realistic circular supply chain scenarios. The methodology demonstrates this framework through a case study of an undergraduate module for CE problem-solving, focusing on waste reduction within small and medium enterprises in Mexico City. Based on student surveys and achievement metrics, results show positive student feedback and evaluation results, meeting module targets. This work’s main contribution offers a framework for creating novel experiential learning cases and cultivating sustainability-related and disciplinary learning outcomes. It also recognises valuable links for citizenship commitment, problem-solving, community engagement, and CE education. However, this work acknowledges limitations in complex problem-solving difficulties, a resource-demanding nature, restricted transferability, and the limited evaluation of learning effectiveness. Future research will explore this work’s relevance across Kolb’s learning styles and diverse industries, focusing on student interest and motivation, and evaluating its impact on student outcomes in various educational contexts.

The post-pandemic era shaped by COVID-19 has compelled universities to reimagine their learning experiences, adapting to new educational requirements and heightened expectations. However, this transformation brings forth novel pedagogical requirements and learning limitations. In today’s educational landscape, learners seek active and relevant learning experiences that seamlessly integrate interactivity, crisis awareness, and global challenges tied to a resilience and sustainability perspective. To address this imperative, our work introduces an experiential learning lab to articulate Kolb’s experiential learning cycle and authentic assessment principles. By incorporating real-world events as study scenarios, higher-order skill challenges, and self-regulated learning in alignment with reflective and practical activities, we aim to enhance students’ engagement and learning relevance. To illustrate practical implementation, we propose a case study methodology regarding an experiential learning lab for operations management education. Specifically, we delve into a case study centred around the Social Lab for Sustainable Logistics, involving a circular economy challenge as a learning experience during the post-COVID-19 pandemic. Preliminary results indicate that the experiential learning lab helped to create the learning experience in alignment with intended learning outcomes. However, further instances of such learning experiences are necessary to explore the contribution and applicability of the lab across diverse settings and disciplines.

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.

This work aims to integrate the circular economy (CE) into learning experiences to enhance learning outcome development in Higher Education (HE). Building on existing active learning research, this work aligns CE initiatives—particularly waste reduction and elimination— with the Sustainable Development Goals (SDGs). The study proposes an experiential learning framework to cultivate citizenship commitment, sustainability impact, and engineering learning outcomes through realistic problem-solving in the supply chains of small and medium enterprises (SMEs). Using the ADDIE instructional design model and Kolb’s experiential learning cycle, the approach is illustrated in a case study within an undergraduate industrial engineering module. Based on student opinion surveys and achievement metrics, results indicate that the experience significantly enhanced student engagement and motivation. This research contributes to the broader dissem-ination of CE concepts in HE and supports the advancement of SDG #4 Quality Education and #12 Sustainable Consumption and Production. Future research will explore the application of this learning experience across diverse industries and assess its impact on student learning outcomes in various educational contexts.

This paper presents the preliminary results of a research on users decisions in a serious game, called Logistic Simulator orLOST. We provide an analysis of the first stage of an ethnographic decision tree modeling based on 20 interviews