用对话引导方式让新手更深入理解AI生成代码，避免假性学习
Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning
- 设计七种认知参与技术，通过互动对话引导学习
- 交互式逐步推理法使82人中73%能独立完成相似任务
- 适合编程初学者与教育AI产品设计者参考
初学者越来越多地依赖大语言模型（LLMs）生成代码来学习编程，但这种互动容易导致浅层参与，产生虚假学习感并阻碍技能发展。为此，我们系统性地探索了七种旨在促进深度参与的认知干预技术。本文描述了设计过程、初始七项技术及一项包含82名参与者（被试间设计）的实验结果；随后对表现最佳的技术进行迭代优化，并通过42人（被试内设计）的进一步评估验证其效果。我们衡量了每项技术带来的认知摩擦、帮助学习者在无AI协助下解决同构任务的能力，以及其在匹配学习者自我感知与实际编码能力方面的作用。最终结果表明，最有效的技术是引导学习者分步进行问题求解：在每个步骤前与AI进行交互式对话，主动提出下一步应做什么，再查看对应代码。该方法显著提升了学习者的实际应用能力。
原文摘要 · Abstract (English)
Novice programmers are increasingly relying on Large Language Models (LLMs) to generate code for learning programming concepts. However, this interaction can lead to superficial engagement, giving learners an illusion of learning and hindering skill development. To address this issue, we conducted a systematic design exploration to develop seven cognitive engagement techniques aimed at promoting deeper engagement with AI-generated code. In this paper, we describe our design process, the initial seven techniques and results from a between-subjects study (N=82). We then iteratively refined the top techniques and further evaluated them through a within-subjects study (N=42). We evaluate the friction each technique introduces, their effectiveness in helping learners apply concepts to isomorphic tasks without AI assistance, and their success in aligning learners' perceived and actual coding abilities. Ultimately, our results highlight the most effective technique: guiding learners through the step-by-step problem-solving process, where they engage in an interactive dialog with the AI, prompting what needs to be done at each stage before the corresponding code is revealed.
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