AI in board Game-Based Learning

Despite receiving less attention in educational research compared to digital games, boardgames show great potential as a learning environment in many educational scenarios. They promote acquisition of disciplinary knowledge and key competences, generate a sense of physical “togetherness”, can be employed in situation of social and economic disadvantage, and can be modified (or “modded”), for better alignment with disciplinary content. The use of games in European schools is very limited; teachers see the potential of games for learning, but their competence in the use of games for learning is superficial and limited to personal experience. High-performance AI systems such as GPT-4 have emerged as a potential game-changer in education, as a collaborative partner to assist teachers in learning design or to automatize decision-making processes. Despite known limitations, trained LLMs show promise in executing educational tasks. This study explores whether trained High-performance AI can facilitate teachers in the creation of boardgame-based learning units, by bridging their knowledge gap in game knowledge and game-based instructional skills. Using the GDBL ID model, the most comprehensive available instructional model for the creation of boardgame-based learning units, in this exploratory study we instructed Chat GPT to address two key phases of bGBL design: the choice of the game for the learning activity and the personalization of the game for constructive alignment and inclusion. Evaluation of the output by GBL experts highlights the potential of AI tools for bGBL