研究学生用AI编程时的求助行为，发现高手主动提问，低手直接甩任务。
Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming
- 通过分析1.9万次交互，区分主动探索与被动委托两种求助模式。
- 顶尖学生多问问题促思考，差生常让AI直接写代码。
- 建议AI系统识别无效委托，引导学生主动学习。
生成式AI正通过'氛围编码'重塑高等教育编程教学，即学生以自然语言与AI协作，而非逐行编写代码。本研究将此现象视为一种求助行为，分析了110名本科生的19,418次交互记录。采用归纳编码与异质转移网络分析，比较优劣学生的行为模式。结果表明，高绩效者表现出工具性求助——通过提问与探索获取信息，促使AI给出导师式回应；而低绩效者则依赖管理性求助，频繁委托任务，驱动AI扮演执行者角色，专注提供现成解决方案。研究揭示，当前生成式AI仅反映学生意图（积极或消极），并未优化学习过程。为实现从工具到伙伴的转变，AI系统需超越被动响应。本文主张采用教育对齐设计，识别非生产性委托，并动态引导互动向探究式学习发展，确保人机协作增强而非替代认知努力。
原文摘要 · Abstract (English)
Generative AI is reshaping higher education programming through vibe coding, where students collaborate with AI via natural language rather than writing code line-by-line. We conceptualize this practice as help-seeking, analyzing 19,418 interaction turns from 110 undergraduate students. Using inductive coding and Heterogeneous Transition Network Analysis, we examined interaction sequences to compare top- and low-performing students. Results reveal that top performers engaged in instrumental help-seeking -- inquiry and exploration -- eliciting tutor-like AI responses. In contrast, low performers relied on executive help-seeking, frequently delegating tasks and prompting the AI to assume an executor role focused on ready-made solutions. These findings indicate that currently generative AI mirrors student intent (whether productive or passive) rather than optimizing for learning. To evolve from tools to teammates, AI systems must move beyond passive compliance. We argue for pedagogically aligned design that detect unproductive delegation and adaptively steer educational interactions toward inquiry, ensuring student-AI partnerships augment rather than replace cognitive effort.
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