Automating board-game based learning. A comprehensive study to assess reliability and accuracy of AI in game evaluation

Andrea Tinterri, Federica Pelizzari, Marilena di Padova, Francesco Palladino, Giordano Vignoli, Anna Dipace
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Abstract

Game-Based Learning (GBL) and its subset, Board Game-Based Learning (bGBL), are dynamic pedagogical approaches leveraging the immersive power of games to enrich the learning experience. bGBL is distinguished by its tactile and social dimensions, fostering interactive exploration, collaboration, and strategic thinking; however, its adoption is limited due to lack of preparation by teachers and educators and of pedagogical and instructional frameworks in scientific literature. Artificial intelligence (AI) tools have the potential to automate or assist instructional design, but carry significant open questions, including bias, lack of context sensitivity, privacy issues, and limited evidence. This study investigates ChatGPT as a tool for selecting board games for educational purposes, testing its reliability, accuracy, and context-sensitivity through comparison with human experts evaluation. Results show high internal consistency, whereas correlation analyses reveal moderate to high agreement with expert ratings. Contextual factors are shown to influence rankings, emphasizing the need to better understand both bGBL expert decision-making processes and AI limitations. This research provides a novel approach to bGBL, provides empirical evidence of the benefits of integrating AI into instructional design, and highlights current challenges and limitations in both AI and bGBL theory, paving the way for more effective and personalized educational experiences.
基于棋盘游戏的自动化学习。评估人工智能在游戏评估中的可靠性和准确性的综合研究
基于游戏的学习(GBL)及其子集--基于棋盘游戏的学习(bGBL)--是一种动态的教学方法,利用游戏的沉浸式力量来丰富学习体验。bGBL的特点在于其触觉和社交维度,可促进互动探索、协作和战略思维;然而,由于教师和教育工作者缺乏准备,以及科学文献中缺乏教学和指导框架,其采用受到了限制。人工智能(AI)工具具有自动或辅助教学设计的潜力,但也存在重大的未决问题,包括偏见、缺乏语境敏感性、隐私问题和证据有限等。本研究将 ChatGPT 作为一种工具,用于为教育目的选择棋盘游戏,并通过与人类专家的评估进行比较,测试其可靠性、准确性和情境敏感性。结果表明,该工具具有较高的内部一致性,而相关性分析表明,该工具与专家评分具有中等到较高的一致性。结果表明,情境因素会影响排名,这强调了更好地理解 bGBL 专家决策过程和人工智能局限性的必要性。这项研究为 bGBL 提供了一种新方法,为将人工智能整合到教学设计中的益处提供了实证证据,并强调了人工智能和 bGBL 理论目前面临的挑战和局限性,为更有效的个性化教育体验铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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