Dynamic business-simulation mechanism: generative AI shifts simulation-based learning from executing predetermined rules to decision-making under uncertainty¶

Design · All designs
Evidence · 1 claim (1 for) · 1 study (1 theoretical), q1 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The third of the article's three proposed mechanisms. The article states that "The dynamic business-simulation mechanism shifts simulation-based learning from the execution of predetermined rules to decision-making under uncertainty." Generative AI dynamically generates simulation scenarios and responses, so students practice decision-making in contexts that resemble real-world complexity rather than following scripted classroom simulations.

Design Implications¶

Context¶

Requirements¶

Generative AI tools capable of generating dynamic simulation scenarios
Course time for simulation-based decision-making activities

Constraints¶

Proposed mechanism illustrated with a strategic management course; no empirical evaluation is reported

Target Learners¶

business and management students in higher education

Learning Goals¶

decision-making under uncertainty in complex business environments

Claims¶

Three Challenges Memorization Management Education [+W]

Examples¶

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Key Sources¶

Shi, X., Dai, T., & Zhang, G. (2026). From Memorization to Experiential Learning: Reconfiguring Classroom Pedagogy in Management Education through Generative AI. Journal of Teaching Innovation and Practice, 2(1), 23–32. https://doi.org/10.65170/jtip.v2i1.53

Page metadata

Field	Value
`type`	`design`
`id`	`genai-dynamic-business-simulation-mechanism`
`title`	`Dynamic business-simulation mechanism: generative AI shifts simulation-based learning from executing predetermined rules to decision-making under uncertainty`
`description`	`The third of the article's three proposed mechanisms.`
`status`	`review`
`generated`	by `process:settle-candidates` on `2026-10-11`
`trust`	unverified — no one has confirmed this page's content
`sources`	1 — `shi-2026`

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