Laboratory safety preparation in science teacher education often relies on lectures, demonstrations, and decontextualised rule learning. Although virtual environments permit low-risk rehearsal, exploratory simulations may leave novices without timely support to interpret risk and justify decisions. This study presents an AI-enhanced Minecraft laboratory designed to move learning from open exploration toward bounded, dialogic guidance. A one-group pre-test/post-test pilot was specified with 64 Malaysian pre-service science teachers completing four missions on hazard recognition, chemical management, safe procedure, and incident response. An embedded AI mentor, grounded in instructor-approved materials, elicited evidence, delivered graduated hints, prompted verification, and structured reflection. Outcomes comprised laboratory safety competency, multidimensional engagement, and laboratory learning achievement. In the simulated dataset retained solely to demonstrate reporting, paired tests indicated improvements in safety competency, t(63) = 15.82, p < .001, dz = 1.98; engagement, t(63) = 11.60, p < .001, dz = 1.45; and learning outcomes, t(63) = 14.39, p < .001, dz = 1.80. Safety competency correlated with achievement (r = .61) and engagement (r = .48). The manuscript proposes a Guided Experiential Safety Learning model integrating situated action, bounded AI scaffolding, collaborative verification, and reflective transfer. Because all numerical findings are simulated and the design lacks a comparison group, no causal or empirical claim is made. The contribution is a theoretically grounded architecture and research agenda for ethically evaluating AI-supported game-based safety education.
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