Artificial Intelligence in Experiential Learning for Business Education: A Systematic Literature Review

Abstract

While experiential learning in business education increasingly integrates artificial intelligence, the current body of evidence remains fragmented across diverse pedagogical approaches and research designs. To synthesize these insights, this study presents a systematic literature review conducted in accordance with the PRISMA 2020 statement guidelines. The analysis examines 29 peer-reviewed publications, highlighting a marked escalation in research activity published between 2020 and 2026. By employing Kolb’s experiential learning cycle as a theoretical lens, the study identifies how AI facilitates knowledge acquisition, critical reflection, and practical application. The findings reveal that AI integration most frequently occurs within project-based, case-based, scenario-driven, and design-thinking frameworks. In these instructional settings, AI acts as a primary catalyst for real-time feedback, introspection, and iterative experimentation. These applications are positively associated with enhanced student engagement, critical thinking, creativity, and professional readiness. However, the literature also identifies critical challenges, specifically regarding assessment validity, the risk of shallow learning, and cognitive overreliance on automated tools. Consequently, this study outlines future research trajectories, advocating for more robust empirical designs, innovative assessment methodologies, and the development of sophisticated AI-enhanced learning ecosystems. This review provides a comprehensive foundation for educators and researchers aiming to navigate the rapidly evolving technological landscape of modern management education.