AI4Health: A Narrative-Centered Educational Game for AI-Infused Biomedical Career Exploration

As AI becomes increasingly central to healthcare and biomedical research, there is a growing need for tools that introduce students to AI in engaging, authentic contexts. This paper presents AI4Health, a narrative-centered educational game that places middle school students in the role of a medical intern investigating real-world health mysteries. In the game's first episode, players gather evidence, interview characters, and use machine learning models with different training data and performance characteristics. Through branching dialogue and contextual problem-solving, players explore the impact of training data on model reliability and consider the ethical implications of AI-assisted diagnosis. This paper showcases the core mechanics, dialogue system, and interactive narrative structure that support AI literacy and health career exploration, and highlights plans to extend the game through additional biomedical scenarios and classroom integration.

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Publication Details

Published
2025-08-19
DOI
https://doi.org/10.1109/cog64752.2025.11114206
Primary Topic
Empathy and Medical Education
Type
article
Field-Weighted Citation Impact
0.00

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article

AI4Health: A Narrative-Centered Educational Game for AI-Infused Biomedical Career Exploration

Elizabeth M. Ozer, S. Thomas Hennigan, Carlos Penilla, Jessica Vandenberg et al.
Empathy and Medical Education
article

AI4Health: A Narrative-Centered Educational Game for AI-Infused Biomedical Career Exploration

Elizabeth M. Ozer, S. Thomas Hennigan, Carlos Penilla, Jessica Vandenberg, Bradford Mott, James Lester
article en

Abstract

As AI becomes increasingly central to healthcare and biomedical research, there is a growing need for tools that introduce students to AI in engaging, authentic contexts. This paper presents AI4Health, a narrative-centered educational game that places middle school students in the role of a medical intern investigating real-world health mysteries. In the game's first episode, players gather evidence, interview characters, and use machine learning models with different training data and performance characteristics. Through branching dialogue and contextual problem-solving, players explore the impact of training data on model reliability and consider the ethical implications of AI-assisted diagnosis. This paper showcases the core mechanics, dialogue system, and interactive narrative structure that support AI literacy and health career exploration, and highlights plans to extend the game through additional biomedical scenarios and classroom integration.

North Carolina State University (US), University of California, San Francisco (US), University of San Francisco (US)
National Institutes of Health, National Institute of General Medical Sciences
Openalex Percentile: Top 75%
Empathy and Medical Education
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