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

2026-06-28

AI Summary

This study addresses the current lack of large-scale empirical research on how university students naturally interact with generative AI in authentic course settings. Drawing on over 15,000 real-world student–AI interaction logs across multiple disciplines, the work employs instruction-guided large-scale manual annotation combined with mixed-methods analysis to systematically characterize student usage patterns along two dimensions: cognitive intent and interaction context. The findings reveal that student–AI interactions exhibit highly structured behavior, dominated by a limited set of recurring patterns rather than highly idiosyncratic strategies, and uncover course-specific interaction profiles. These results provide a critical empirical foundation for understanding the practical integration of generative AI in higher education.

Abstract

Generative AI tools (GenAI) are increasingly used by students during coursework, yet empirical understanding of how students engage with these systems in authentic learning contexts remains limited. Existing studies have largely relied on controlled settings, single-domain analyses, or small-scale qualitative data, leaving open how student-AI interaction unfolds across courses and forms of academic work. We present a large-scale analysis of naturally occurring student-AI interactions collected from undergraduate students across multiple university courses and academic domains. The dataset comprises over 15,000 student-AI interaction units drawn from voluntary use of generative AI during real coursework. To characterize these interactions, we analyze each student turn along two complementary dimensions, cognitive intent and interaction context, capturing whether requests are directed toward the task or domain, the student's own work, or prior AI output. Using instruction-guided annotation applied at scale, we examine how these interaction patterns are distributed overall and how they vary across courses. Our analysis reveals that student-AI interaction is highly structured. Across courses, interactions concentrate in a small number of recurring patterns rather than exhibiting highly idiosyncratic use. At the same time, systematic differences emerge across courses, giving rise to distinct interaction profiles associated with different forms of academic work.

Problem

• Generative AI
• student-AI interaction
• authentic learning contexts
• academic work
• large-scale analysis

Innovation

• generative AI
• large-scale analysis
• student-AI interaction
• cognitive intent
• interaction context