发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过混合方法试点发现，在编程教育中，脚手架式AI工具与无限制工具对学生作业表现无显著差异，学生自我管理AI使用的能力（即AI素养）比工具设计更重要，建议将AI素养作为核心技能明确教授。
AI 中文摘要
生成式AI已成为编程教育中的常规资源，大多数机构对此的应对措施是试图控制，要么限制访问，要么向学生提供受控版本的技术。本文报告了一项在硕士层次数据分析课程中进行的为期七周的混合方法试点研究，其中33名学生被随机分配到嵌入基于笔记本的实验室课程的脚手架式AI学习教练组，或自行选择无限制使用AI工具组。该教练提供逐步提示，不生成代码，限制每节课的提示数量，并要求每节课结束时进行简短反思。该设计基于脚手架理论和近期实验证据，假设引导性和有限的支持能建立信心并减少过度依赖，且脚手架组的学习效果会更好。作业表现在各条件之间没有差异。教练组的学生报告了更高的信心，但对提示预算管理不善，而无限制组的学生对自己的工具感到满意，但对依赖程度感到不安。在访谈中，两组学生都将意识到自己对AI的依赖视为课程最有价值的成果。在两种条件下，制定了自己何时使用AI规则的学生表现更好，而那些对模型工作原理理解最深的学生（均为自学）使用工具最有目的性，并获得了最高分。工具的设计不如学生管理自身使用的能力重要，而这种能力目前是偶然获得的。本文认为，适当的回应是结构性的：评估应针对AI辅助工作背后的推理进行评分，并将AI素养作为核心技能明确教授。
英文摘要
Generative AI has become a routine resource in programming education, and most institutional responses to it are attempts at control, either by restricting access or by offering students a controlled version of the technology. This paper reports a seven-week mixed-methods pilot study in a master's-level data analytics course, in which 33 students were randomly assigned either to a scaffolded AI Study Coach embedded in the notebook-based laboratory sessions or to unrestricted use of AI tools of their own choosing. The Coach offered stepwise hints, did not generate code, limited the number of hints per session, and required a short reflection at the end of each session. The design assumed, in line with scaffolding theory and recent experimental evidence, that guided and limited support would build confidence and reduce over-reliance, and that the scaffolded group would learn more. Assignment performance did not differ between the conditions. Students in the Coach condition reported higher confidence but managed the hint budget poorly, while students in the unrestricted condition were satisfied with their tools and uneasy about how much they depended on them. In interviews, students in both conditions identified awareness of their own reliance on AI as the most valuable outcome of the course. Students who had formulated their own rules for when to use AI performed better in both conditions, and those with the best understanding of how the models work, in every case self-taught, used the tools most deliberately and achieved the highest scores. The design of the tool mattered less than the students' capacity to govern their own use of it, a capacity that is at present acquired by chance. The paper argues that the appropriate response is structural: assessment that grades the reasoning behind AI-assisted work, and AI literacy taught explicitly as a core skill.
