The increasing use of Generative Artificial Intelligence (GAI) tools in education highlights the need to understand their influence on individuals' thinking processes and agency. This research explored 20 university students' interaction with GAI during programming. Participants completed surveys, recorded their screens during an hour-long programming session, and reflected on their GAI use. To analyse the data, we developed an AI-augmented thinking coding scheme with four dimensions: Question Formulation, Solution Development, Solution Analysis and Evaluation, and Solution Refinement. Participants were categorised into human-led and AI-led groups based on the time ratio of human-generating source code versus copying source code from GAI. T-tests indicated that the human-led group spent significantly more time on Solution Development and Solution Refinement than the AI-led group. Sequential pattern mining revealed distinct patterns of the two groups: the human-led group often refined GAI outputs, while the AI-led group frequently relied on direct answers from GAI. Correlation analyses found that positive attitudes towards AI, critical thinking, and programming self-efficacy positively correlated with Question Formulation; critical thinking was positively related to Solution Refinement; and programming self-efficacy was negatively associated with Solution Analysis and Evaluation. This study enhances understanding of the thinking process in GAI-supported programming.

翻译：随着生成式人工智能（GAI）工具在教育领域的日益普及，理解其对个体思维过程与自主性的影响变得至关重要。本研究探讨了20名大学生在编程过程中与GAI的互动情况。参与者完成了问卷调查，记录了一小时编程过程中的屏幕操作，并对GAI使用情况进行了反思。为分析数据，我们构建了一个包含四个维度的AI增强思维编码框架：问题构建、解决方案开发、解决方案分析与评估，以及解决方案优化。根据参与者自主生成源代码与从GAI复制源代码的时间比例，将其分为人类主导组和AI主导组。T检验结果显示，人类主导组在解决方案开发和解决方案优化上花费的时间显著多于AI主导组。序列模式挖掘揭示了两组不同的行为模式：人类主导组常对GAI输出进行优化，而AI主导组则频繁依赖GAI的直接答案。相关性分析发现：对AI的积极态度、批判性思维和编程自我效能感与问题构建呈正相关；批判性思维与解决方案优化正相关；编程自我效能感与解决方案分析及评估负相关。本研究深化了对GAI辅助编程中思维过程的理解。