This study investigates how undergraduate students engage with ChatGPT in self-directed learning contexts. Analyzing naturalistic interaction logs, we identify five dominant use categories of ChatGPT: information seeking, content generation, language refinement, metacognitive engagement, and conversational repair. Behavioral modeling reveals that structured, goal-driven tasks like coding, multiple-choice solving, and job application writing are strong predictors of continued use. Drawing on Self-Directed Learning (SDL) and the Uses and Gratifications Theory (UGT), we show how students actively manage ChatGPT's affordances and limitations through prompt adaptation, follow-ups, and emotional regulation. Rather than disengaging after breakdowns, students often persist through clarification and repair, treating the assistant as both tool and learning partner. We also offer design and policy recommendations to support transparent, responsive, and pedagogically grounded integration of generative AI in higher education.

翻译：本研究调查了本科生在自主学习情境中如何与ChatGPT进行互动。通过分析自然交互日志，我们识别出ChatGPT的五种主要使用类别：信息搜寻、内容生成、语言润饰、元认知参与和对话修复。行为建模表明，诸如编程、选择题解答和求职信撰写等结构化、目标驱动的任务是持续使用的强预测因子。借鉴自主学习理论和使用与满足理论，我们揭示了学生如何通过提示词调整、后续追问和情绪调节，主动管理ChatGPT的功能与局限。面对交互故障，学生通常不会直接放弃，而是通过澄清和修复持续互动，将助手视为工具与学习伙伴的双重角色。本研究进一步提出设计与政策建议，以支持生成式人工智能在高等教育中实现透明、响应迅速且以教学法为基础的整合。