AI Combines, Humans Socialise: A SECI-based Experience Report on Business Simulation Games

TL;DR

This study explores how generative AI tools support knowledge processes in business simulation games, primarily enhancing explicit knowledge synthesis while human instructors foster tacit knowledge development.

Contribution

It provides a SECI-based analysis of AI integration in BSG, highlighting AI's role in explicit knowledge synthesis and the boundaries of human-AI collaboration.

Findings

AI supports the SECI 'Combination' phase by facilitating knowledge synthesis.

Human interaction remains essential for tacit knowledge and skills development.

AI enhances responsiveness and engagement in simulation-based learning.

Abstract

Background. Business Simulation Games (BSG) are widely used to foster experiential learning in complex managerial and organisational contexts by exposing students to decision-making under uncertainty. In parallel, Artificial Intelligence (AI) is increasingly integrated into higher education to support learning activities. However, despite growing interest of AI in education, its specific role in BSG and its implications for knowledge creation processes remain under-theorised. Intervention. This paper reports on the integration of generative AI tools into a BSG designed for engineering students. AI was embedded as a support mechanism during the simulation to assist students in analysing events, reformulating information, and generating decision-relevant insights, while instructors retained responsibility for supervision, debriefing, and complex issues. Methods. Adopting a qualitative experience-report approach, the study draws on the SECI model (Socialisation, Externalisation, Combination, Internalisation) as an analytical framework to examine how students and instructors interacted with AI during the simulation and how different forms of knowledge were mobilised and developed. Results. The findings indicate that AI primarily supports the Combination phase of the SECI model by facilitating the rapid synthesis, reformulation, and contextualisation of explicit knowledge. In contrast, the processes of Socialisation, Externalisation, and Internalisation remained largely dependent on peer interaction, individual reflection, and instructor guidance. Discussion. The results suggest a functional boundary in human-AI collaboration within simulation-based learning. AI acts as a cognitive enhancer that improves responsiveness and access to explicit knowledge, but it does not replace the pedagogical role of instructors in supporting the development of tacit knowledge, competencies, and phronesis. Conclusion. Integrating AI into BSG can enhance learning efficiency and engagement, but effective experiential learning continues to rely on active human supervision. Future research should investigate instructional designs that better support tacit knowledge acquisition in AI-assisted simulations.