用大模型聊天机器人在迷宫编程游戏中辅助计算思维训练
MazeMate: An LLM-Powered Chatbot to Support Computational Thinking in Gamified Programming Learning
- 在3D迷宫编程游戏里嵌入大模型聊天助手，提供上下文相关的引导
- 247名本科生参与测试，对解迷宫的帮助评价较高，设计支持较弱
- 能有效促进分解、抽象和算法思维，但生成建议有时不匹配或虚构
计算思维（CT）是基础性问题解决能力，而游戏化编程环境是培养该能力的常用方法。尽管大语言模型（LLM）可提供即时编程支持，但现有应用很少真正促进计算思维发展。我们提出MazeMate，一个嵌入3D迷宫编程游戏的LLM驱动聊天机器人，旨在提供与迷宫求解和迷宫设计中计算思维过程相匹配的自适应、情境敏感的支架支持。我们在247名本科生中进行了首次课堂实践。学生普遍认为MazeMate moderately helpful，对迷宫求解的支持感知价值高于迷宫设计。主题分析证实其支持了分解、抽象和算法思维等计算思维过程，但也暴露出在迷宫设计中的局限性，如建议不匹配、生成虚构的算法解决方案。这些发现表明，基于LLM的支架在支持计算思维方面具有潜力，同时指明了提升MazeMate在真实课堂中可用性的设计优化方向。
原文摘要 · Abstract (English)
Computational Thinking (CT) is a foundational problem-solving skill, and gamified programming environments are a widely adopted approach to cultivating it. While large language models (LLMs) provide on-demand programming support, current applications rarely foster CT development. We present MazeMate, an LLM-powered chatbot embedded in a 3D Maze programming game, designed to deliver adaptive, context-sensitive scaffolds aligned with CT processes in maze solving and maze design. We report on the first classroom implementation with 247 undergraduates. Students rated MazeMate as moderately helpful, with higher perceived usefulness for maze solving than for maze design. Thematic analysis confirmed support for CT processes such as decomposition, abstraction, and algorithmic thinking, while also revealing limitations in supporting maze design, including mismatched suggestions and fabricated algorithmic solutions. These findings demonstrate the potential of LLM-based scaffolding to support CT and underscore directions for design refinement to enhance MazeMate usability in authentic classrooms.
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