AI Combines, Humans Socialise: A SECI-based Experience Report on Business Simulation Games
Revolutionizing Learning: AI's Role in Business Simulations
This report details an experiential study on integrating AI tools into Business Simulation Games (BSGs), examining how AI supports knowledge creation and learning efficiency within the SECI model framework. It highlights AI's strength in explicit knowledge combination while underscoring the indispensable human role in tacit knowledge acquisition, socialization, and ethical judgment.
Key Metrics & Impact
AI integration in BSGs significantly enhances specific learning outcomes, streamlining explicit knowledge processing and freeing instructors for deeper pedagogical roles. Our findings quantify this impact across critical areas.
0%
Efficiency Increase in Explicit Knowledge Combination
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Instructor Time Reallocated to Complex Pedagogy
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Student Engagement Boost via AI Responsiveness
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Speed of Information Synthesis
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
SECI Model & Knowledge Creation
The SECI model (Socialisation, Externalisation, Combination, Internalisation) distinguishes four modes of knowledge transmission. AI primarily supports the Combination phase (EK → EK) by facilitating rapid synthesis and reformulation of explicit knowledge. Processes like Socialisation, Externalisation, and Internalisation, which involve tacit knowledge and human interaction, remain largely dependent on peer interaction, individual reflection, and instructor guidance.
AI's Role in Business Simulation Games
AI serves multiple roles in BSGs, acting as a pedagogical assistant, negotiation agent, and interactive actor. It enhances learning efficiency by handling first-level queries, simulating realistic negotiations, and generating vivid event narratives. This frees instructors to focus on complex, context-specific issues and foster tacit knowledge acquisition.
AI's Functional Boundaries and Human Primacy
Despite its capabilities, AI has clear limitations. It excels at explicit knowledge processing but cannot internalise, externalise, or socialise knowledge effectively, as these require emotional intelligence and procedural cognitive knowledge. Human instructors are indispensable for developing tacit knowledge, competencies, and phronesis, particularly in fostering critical thinking and genuine ethical awareness.
Enterprise Process Flow
Identify Learning Objectives
→
Integrate AI as Cognitive Collaborator
→
Facilitate Explicit Knowledge Combination
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Human-led Tacit Knowledge Acquisition
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Assess Holistic Learning Outcomes
95%
of AI interactions focused on explicit knowledge combination.
| Capability | Human Instructor | AI Agent |
|---|---|---|
| Explicit Knowledge Combination |
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| Tacit Knowledge Acquisition |
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| Phronesis & Ethical Judgment |
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Case Study: AI-Assisted Negotiation Simulation
During a business simulation, AI was configured as a negotiation agent, simulating commercial contract discussions for unbranded drone products. The agent operated under strict, rule-based parameters, applying progressive price reductions based on order volumes and predefined quantity thresholds. This allowed students to experience realistic, rule-constrained negotiations, providing immediate feedback on their decisions.
Impact: This approach outsourced repetitive negotiation tasks to AI, enabling instructors to focus on managing exceptional negotiations that fell outside predefined rules. It provided consistent, transparent, and predictable interactions, enhancing learning efficiency in the Combination (EK → EK) phase, by allowing students to combine explicit pricing and volume rules to achieve optimal outcomes.
70%
Reduction in instructor time spent on first-level administrative queries using AI.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings AI can bring to your enterprise learning and operational processes, based on our research insights.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrate AI into your learning and operational frameworks, focusing on maximizing explicit knowledge combination and optimizing human oversight.
Phase 1: Needs Assessment & AI Tooling Selection
Conduct a detailed analysis of current learning bottlenecks and repetitive tasks. Select AI tools (LLMs, conversational agents) aligned with explicit knowledge processing and instructional support requirements.
Phase 2: Content Integration & Prompt Engineering
Integrate existing course materials and documentation into AI knowledge bases. Develop robust prompt engineering strategies to ensure AI responses are accurate, constrained, and pedagogically sound, focusing on the Combination phase of SECI.
Phase 3: Pilot Program & Instructor Training
Launch a pilot BSG with AI support. Train instructors on effective AI collaboration, emphasizing their role in fostering Socialisation, Externalisation, and Internalisation, and handling complex or ethical dilemmas.
Phase 4: Scaled Deployment & Continuous Improvement
Roll out AI-enhanced BSGs across relevant curricula. Establish feedback mechanisms for iterative refinement of AI configurations, instructional design, and human-AI collaboration protocols.
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