Date
Publisher
arXiv
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.
Study at a glance
- Question: How do students learn to use generative AI through everyday practice rather than formal instruction?
- Setting and data: 10,536 ChatGPT messages from 36 undergraduate students over one academic year
- Method: Analysis of student ChatGPT messages
Why it matters
While normative AI literacy frameworks proliferate, empirical understanding of how students actually learn to work with generative AI through everyday practice rather than formal instruction remains limited.
Key findings
- Across 10,536 ChatGPT messages over an academic year, 36 undergraduates developed functional AI competence through ongoing negotiation rather than one-time adoption.
- Repair work during AI breakdowns produced substantial learning about AI capabilities, developing students' crucial skill of repair literacy.
- Students strategically matched their AI interaction patterns to their learning needs while exercising critical judgment about the technology's limitations.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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