How do students develop AI literacy through everyday practice rather than formal instruction? While normative AI literacy frameworks proliferate, empirical understanding of how students actually learn to work with generative AI remains limited. This study analyzes 10,536 ChatGPT messages from 36 undergraduates over one academic year, revealing five use genres -- academic workhorse, emotional companion, metacognitive partner, repair and negotiation, and trust calibration -- that constitute distinct configurations of student-AI learning. Drawing on domestication theory and emerging frameworks for AI literacy, we demonstrate that functional AI competence emerges through ongoing relational negotiation rather than one-time adoption. Students develop sophisticated genre portfolios, strategically matching interaction patterns to learning needs while exercising critical judgment about AI limitations. Notably, repair work during AI breakdowns produces substantial learning about AI capabilities, developing what we term "repair literacy" -- a crucial but underexplored dimension of AI competence. Our findings offer educators empirically grounded insights into how students actually learn to work with generative AI, with implications for AI literacy pedagogy, responsible AI integration, and the design of AI-enabled learning environments that support student agency.

翻译：学生如何通过日常实践而非正式教学发展AI素养？尽管规范性AI素养框架不断涌现，但关于学生如何实际学习与生成式AI协作的实证理解仍然有限。本研究分析了36名本科生在一个学年内产生的10,536条ChatGPT消息，揭示了五种使用类型——学术主力、情感伴侣、元认知伙伴、修复与协商以及信任校准——这些构成了学生-AI学习的独特配置。借鉴驯化理论和新兴的AI素养框架，我们证明功能性AI能力是通过持续的关系协商而非一次性采纳形成的。学生发展出复杂的使用类型组合，策略性地将交互模式与学习需求相匹配，同时对AI局限性行使批判性判断。值得注意的是，AI故障期间的修复工作产生了关于AI能力的实质性学习，形成了我们称之为"修复素养"的能力——这是AI素养中关键但尚未被充分探索的维度。我们的研究结果为教育工作者提供了基于实证的见解，揭示了学生如何实际学习与生成式AI协作，对AI素养教学法、负责任的AI整合以及支持学生能动性的AI赋能学习环境设计具有重要启示。