AI Combines, Humans Socialise: A SECI-based Experience Report on Business Simulation Games
New research shows generative AI excels at data synthesis but struggles with tacit knowledge transfer.
A new academic paper by Nordine Benkeltoum, titled 'AI Combines, Humans Socialise: A SECI-based Experience Report on Business Simulation Games,' provides a crucial framework for understanding the division of labor between AI and human instructors in experiential learning. The study, published on arXiv, investigates the integration of generative AI tools into Business Simulation Games (BSG)—complex, scenario-based training environments used to teach managerial decision-making under uncertainty. The central finding establishes a clear functional boundary: AI acts as a powerful cognitive enhancer for processing explicit information, but the core pedagogical processes of guiding reflection, fostering peer interaction, and developing tacit knowledge remain firmly in the human domain. This challenges the notion of AI as a wholesale replacement for teachers, instead positioning it as a specialized support tool within a structured educational design.
The research employed the SECI model (Socialisation, Externalisation, Combination, Internalisation) as an analytical lens, revealing that AI's primary value lies in the 'Combination' phase. Here, it rapidly synthesizes game data, reformulates information, and generates decision-relevant insights for students. However, the processes of 'Socialisation' (peer interaction), 'Externalisation' (articulating ideas), and 'Internalisation' (reflective learning) were largely dependent on human facilitation. The paper concludes that while AI integration can boost learning efficiency and engagement, effective experiential learning continues to rely on active human supervision for developing higher-order competencies and phronesis—the practical wisdom needed for complex judgment. This has significant implications for instructional design, suggesting future research should focus on hybrid models that strategically pair AI's analytical strengths with human mentorship to cultivate the tacit knowledge essential for leadership and strategy.
- AI's role was confined to the 'Combination' phase of the SECI model, excelling at data synthesis and reformulation.
- Critical learning phases—Socialization, Externalization, Internalization—remained dependent on human instructors and peer interaction.
- The study establishes a functional boundary, showing AI enhances cognitive tasks but cannot replace pedagogy for tacit knowledge development.
Why It Matters
Provides a blueprint for effective human-AI collaboration in corporate training and education, maximizing efficiency without sacrificing depth.