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Georgia Institute of Technology
Age Matters: Age-Differentiated Playtesting for AI Educational Games
Key insights
6 takeaways- 01Age-differentiated playtesting is essential for AI educational games because cognitive development dictates how students interact with and evaluate game mechanics, narrative, and pedagogical scaffolding.
- 02Feedback quality varies by cohort: middle and high school students prioritize aesthetic and narrative elements, whereas college students provide sophisticated critiques on AI instruction, pedagogical companions, and generative AI transparency.
- 03Prior AI knowledge acts as a primary mediator for feedback depth, requiring designers to structure playtesting sessions that account for both developmental maturity and subject-matter expertise.
- 04A one-size-fits-all design approach is ineffective for AI-focused educational tools, as it fails to align with the distinct cognitive abilities and perspectives of learners across the K-12 and collegiate spectrum.
- 05Designers should adopt an asset-based approach that treats student feedback as a core component of the iterative refinement process for AI-driven learning tools.
- 06Effective game-based learning requires the integration of developmental psychology with technical AI literacy goals to ensure instructional objectives are successfully met.
Summary
Age Matters: Age-Differentiated Playtesting for AI JARED ORDONA LIM, Georgia Institute of Technology, USA XUERU YU, Georgia Institute of Technology, USA BRYAN EARL WALLACE, Georgia Institute of Technology, USA SOLYANE BERGE, Georgia Institute of Technology, USA JANE AWUAH, Georgia Institute of Technology, USA JUDITH ODILI UCHIDIUNO, Georgia Institute of Technology, USA Playtesting is widely employed in educational game design as a post-hoc method for game design validation within the age groups ...
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