Learning to Live with AI: How Students Develop AI Literacy Through Naturalistic ChatGPT Interaction

2026-01-28

AI Summary

This study addresses the lack of empirical research on how students naturally develop generative AI literacy through everyday practice. Drawing on domestication theory and an AI literacy framework, the authors analyze 10,536 interaction logs between 36 undergraduate students and ChatGPT over one academic year, employing qualitative content analysis and interaction log mining. The work proposes the concept of “repair literacy,” revealing that AI competence emerges through processes of troubleshooting and repair. Five distinct student–AI interaction patterns are identified, demonstrating how learners continuously negotiate their relationship with AI to construct diverse strategies. Through this iterative engagement, students cultivate functional AI capabilities grounded in critical judgment, offering crucial empirical evidence for integrating AI education in informal learning contexts.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Generation
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• AI literacy
• generative AI
• student learning
• naturalistic interaction
• repair literacy

Innovation

• AI literacy
• repair literacy
• generative AI
• naturalistic interaction
• domestication theory