AI in the Wild: A Large Scale Analysis of Authentic Interactions of College Students with Generative AI

Karidi, Amir & Roll (2026) present one of the largest empirical analyses to date of authentic (rather than lab-based) interactions between college students and generative AI tools. By analyzing interaction logs at scale, they identify distinct patterns: some students use AI as a LLM-powered learning companion for explanation and exploration, while others offload cognitive work entirely — copying outputs without comprehension. The work provides much-needed ecological validity to a literature that has largely relied on survey self-reports and controlled experiments. These findings connect directly to Student Experience research, showing that the gap between AI 'use' and AI 'learning' is wide and context-dependent. The study offers actionable design implications for AI Higher Ed Bridge Gap: platforms should scaffold metacognitive engagement with AI outputs rather than optimizing for answer correctness alone. The paper also contributes to Generative AI literacy frameworks by mapping what competencies students actually display in uncontrolled settings, informing AI Literacy curriculum design.

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Taelin Karidi, Ofra Amir, Ido Roll (2026). AI in the Wild: A Large Scale Analysis of Authentic Interactions of College Students with Generative AI. arXiv:2606.29442. cs.CY (AIED 2026).