AI 中文总结
研究面向对象编程课程中学习者与人工智能交互模式，通过对210名本科生调查，发现学生用GenAI多在寻求解释和调试，聚类出五种交互模式，表明自主使用GenAI不会带来学习提升，强调教学指导下人工智能使用的重要性。
AI 中文摘要
本完整研究论文探讨了在面向对象编程（OOP）课程中，不同形式的学习者与人工智能交互如何与学习成果相关。生成式人工智能（GenAI）工具在编程教育中被学生越来越多地使用，但其教育影响的证据仍好坏参半。本研究调查了学生自主使用GenAI的模式及其与学业表现、感知难度、理解和信任的关系。通过对210名本科生的调查数据发现，学生使用GenAI更多用于寻求解释和调试而非代码生成。聚类分析识别出五种不同的学习者与人工智能交互模式。研究表明，仅自主使用GenAI不会带来可衡量的学习提升，强调了教学指导和过程感知的人工智能支持的必要性。本研究提供了关于学习者与人工智能交互模式的实证证据，凸显了教学指导下人工智能在编程教育中使用的重要性。
英文摘要
This full research paper examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming (OOP) courses. Generative artificial intelligence (GenAI) tools are increasingly used by students in programming education, yet evidence on their educational impact remains mixed. In particular, little is known about how students integrate GenAI tools when learning OOP, and how different patterns of use relate to students' learning experiences and outcomes. This study investigates patterns of students' self-directed GenAI use and their relationship with academic performance, perceived difficulty, understanding, and trust. Survey data were collected from 210 undergraduate students enrolled in a first-year OOP course in which the use of GenAI tools was permitted for coursework but prohibited in assessments. Results show that students used GenAI significantly more often for explanation seeking and debugging than for code generation. Cluster analysis identified five distinct learner-AI interaction profiles, including a "smart" high-usage pattern characterized by low reliance on code generation and high use for conceptual support and debugging. While usage patterns were associated with differences in perceived assignment difficulty, self-assessed understanding, trust in AI-generated code, and norm-related attitudes, no significant differences in assessment performance were found across clusters. These findings suggest that self-directed GenAI use alone does not lead to measurable learning gains, underscoring the need for pedagogically guided and process-aware AI support. The study contributes empirical evidence on learner-AI interaction patterns and highlights the importance of pedagogically guided AI use in programming education.
CommentsThis work has been accepted for publication at the IEEE Frontiers in Education (FIE) 2026 Conference. Copyright may be transferred without notice, after which this version may no longer be accessible
