分析1万+对话日志，发现编程新手用AI时如何影响自我反思能力。
Scaffolding Metacognition in Programming Education: Understanding Student-AI Interactions and Design Implications
- 通过分析学生与AI的对话，识别元认知各阶段的互动模式。
- 超10,000条日志显示：多数学生跳过思考直接要代码，削弱自主学习。
- 提出设计建议：让AI助手引导思考，而非直接给答案，适合教育类AI开发
生成式AI工具如ChatGPT为编程初学者提供了前所未有的即时个性化支持。尽管前景广阔，其对学生元认知过程的影响仍缺乏研究。现有工作多关注正确性和可用性，而较少探讨学生使用AI助手是否支持或绕过关键的元认知策略。本研究通过元认知视角分析大学编程课程中的学生-AI交互，基于三年间收集的超过10,000条对话日志，并结合学生与教师的问卷调查。分析聚焦于提示词与回应如何对应元认知阶段与策略。综合多源数据，提炼出旨在支持而非取代元认知参与的AI辅助编程工具设计原则。研究结果为开发能强化编程学习过程的教育型AI工具提供指导。
原文摘要 · Abstract (English)
Generative AI tools such as ChatGPT now provide novice programmers with unprecedented access to instant, personalized support. While this holds clear promise, their influence on students' metacognitive processes remains underexplored. Existing work has largely focused on correctness and usability, with limited attention to whether and how students' use of AI assistants supports or bypasses key metacognitive processes. This study addresses that gap by analyzing student-AI interactions through a metacognitive lens in university-level programming courses. We examined more than 10,000 dialogue logs collected over three years, complemented by surveys of students and educators. Our analysis focused on how prompts and responses aligned with metacognitive phases and strategies. Synthesizing these findings across data sources, we distill design considerations for AI-powered coding assistants that aim to support rather than supplant metacognitive engagement. Our findings provide guidance for developing educational AI tools that strengthen students' learning processes in programming education.
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