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arXiv · 2601.20749

From Retrieval to Synthesis: Repair Literacy and the Domestication of Generative AI

Tawfiq Ammari·Meilun Chen·S M Mehedi Zaman·Kiran Garimella
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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.

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Tawfiq Ammari, Meilun Chen, S M Mehedi Zaman, Kiran Garimella. 2026-01-28. From Retrieval to Synthesis: Repair Literacy and the Domestication of Generative AI. https://arxiv.org/abs/2601.20749

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