ArticleFDG : proceedings of the International Conference on Foundations of Digital Games. International Conference on the Foundations of Digital Games2026
A Theory-Informed Narrative-Centered Model to Foster AI Literacy and Biomedical Career Interest.
Article in FDG : proceedings of the International Conference on Foundations of Digital Games. International Conference on the Foundations of Digital Games, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
As artificial intelligence (AI) becomes increasingly central to healthcare and biomedical research, there is a growing need for learning experiences that help students understand AI concepts while envisioning future career pathways. This paper presents a theory-informed narrative-centered model designed to foster AI literacy and support emerging interest in biomedical careers among early adolescents aged 11-14. Grounded in narrative-centered learning and social cognitive theory, the model articulates how narrative structure, role-based engagement, and consequential decision making can support self-efficacy development, conceptual understanding, and interest in AI-enabled biomedical work. Building on this framework, we describe the design of a narrative-centered educational game in which learners assume the role of a medical intern and investigate patient cases using AI diagnostic tools. We then report findings from a pilot study with 25 students who played the game and participated in focus groups, drawing on gameplay trace data, in-game reflections, and qualitative feedback. Findings suggest high engagement, productive use of AI tools, and evidence of increased awareness of biomedical applications of AI, along with indications of perceived understanding of AI concepts. Together, the theoretical model, game design, and pilot findings illustrate how narrative-centered educational games can serve as research platforms while providing insight into how youth reason about AI in career-connected learning contexts.
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