Introducing Adolescents to the Social Dimensions of AI through Story-Driven Game-Based Learning.

Authors

Jessica Vandenberg, Bradford Mott, Carlos Penilla, James Lester, Elizabeth Ozer

Published in

Artificial intelligence in education : 27th international conference, AIED 2026, Seoul, South Korea, June 27-July 3, 2026, proceedings. Part VI. International Conference on Artificial Intelligence in Education (27th : 2026 : Seoul, Kore.... Volume 16586. Pages 337-345. Epub Jun 27, 2026.

Abstract

AI increasingly shapes high-stakes decisions in domains such as education and healthcare, underscoring the importance of engaging K-12 students in AI literacy experiences that foreground societal dimensions of AI, including fairness, bias, and accountability. This paper presents a story-driven, game-based learning environment designed to support middle school students' reasoning about these societal dimensions. In the game, students take on the role of decision-makers evaluating AI models in simulated clinical scenarios, navigating trade-offs between accuracy, training data representativeness, and model appropriateness for specific populations. We report findings from a classroom-based pilot study with adolescents (ages 11-14) examining how students engaged with issues of fairness, bias, and accountability during the intervention. Data sources included brief pre/post knowledge assessments and attitudinal surveys. Results suggest that participation was associated with improvements in students' understanding of AI-supported decision-making and shifts toward more critical perspectives on AI capabilities and limitations. Given the short duration of the intervention and the modest scope of the assessment measures, these findings should be interpreted as preliminary. Nonetheless, this work provides early evidence that narrative-centered, game-based approaches can support student engagement with the societal dimensions of AI and offers design insights for integrating ethical reasoning into classroom-based AI learning experiences.

PMID:
42840989
Bibliographic data and abstract were imported from PubMed on 07 Oct 2026.

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