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.

本研究针对新手程序员使用大型语言模型生成代码时，往往导致浅层参与而妨碍技能发展的现象，开展了系统的设计探索，开发了七种认知参与技术以促进与AI生成代码的深入互动。研究结果显示，最有效的技术是通过引导学习者逐步解决问题，并与AI进行互动对话，在展示相应代码之前明确每个阶段需要进行的操作。

利用AI生成代码探索认知参与技术的设计空间以提升学习