Towards AI-assisted Board Game-based Learning: Assessing LLMs in Game Personalisation

This paper evaluates the ability of several large language models (ChatGPT, Copilot, Claude) to personalize board games for K-12 educational settings by comparing AI-generated game modifications against human expert recommendations across different classroom scenarios. The study uses blind expert evaluation to assess whether LLMs can effectively adapt board games to align with learning objectives and student needs.

Board games are used in different educational settings to promote acquisition of disciplinary content, soft skills, foster engagement towards learning content, and sustain motivation. However, designing and conducting effective educational activities with board games requires instructional design skills, knowledge of games, as well as the ability to align the player's internal goals with the learning objectives. Board game-based learning (bGBL) design includes choosing appropriate games and pers

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