Large-scale empirical analysis of authentic student-GenAI interactions reveals concentrated recurring patterns that vary by course and form of academic work.
arXiv preprint arXiv:2505.24126 (2025)
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Large-scale analysis of wild LLM chat logs finds that user interaction patterns stabilize quickly after initial use and correlate with long-term outcomes like retention, creating an agency paradox of limited exploration in unconstrained systems.
Students rationalize AI use in writing via over 20 justifications across five conceptualization sites in ad hoc and post hoc ways, indicating an ethical slippery slope beyond instructor expectations.
Longitudinal analysis of Bing Copilot users shows sticky individual LLM habits, activity-level differences in task complexity and success, and that WildChat is skewed toward power users.
Survey and interview study finds international students view conversational AI as short-term first-aid for cross-cultural challenges with interest in long-term companion use.
AI data firms view human expertise as an extractable, low-cost resource to feed AI systems while treating institutional expertise as something needing liberation or reform to fit this model.
Generative AI applications carry risks of misrepresenting high-risk memories of WWII atrocities in Ukraine via hallucinations and selective moralization.
Presents a three-tension framework for evaluating and designing agentic AI initiatives in K-12 and higher education.
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AI in the Wild: A Large Scale Analysis of Authentic Interactions of College Students with Generative AI
Large-scale empirical analysis of authentic student-GenAI interactions reveals concentrated recurring patterns that vary by course and form of academic work.
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