{"ID":2857715,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.09763","arxiv_id":"2510.09763","title":"Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices","abstract":"AI-driven applications have become woven into students' academic and creative workflows, influencing how they learn, write, and produce ideas. Gaining a nuanced understanding of these usage patterns is essential, yet conventional survey and interview methods remain limited by recall bias, self-presentation effects, and the underreporting of habitual behaviors. While ethnographic methods offer richer contextual insights, they often face challenges of scale and reproducibility. To bridge this gap, we introduce a privacy-conscious approach that repurposes VPN-based network traffic analysis as a scalable ethnographic technique for examining students' real-world engagement with AI tools. By capturing anonymized metadata rather than content, this method enables fine-grained behavioral tracing while safeguarding personal information, thereby complementing self-report data. A three-week field deployment with university students reveals fragmented, short-duration interactions across multiple tools and devices, with intense bursts of activity coinciding with exam periods-patterns mirroring institutional rhythms of academic life. We conclude by discussing methodological, ethical, and empirical implications, positioning network traffic analysis as a promising avenue for large-scale digital ethnography on technology-in-practice.","short_abstract":"AI-driven applications have become woven into students' academic and creative workflows, influencing how they learn, write, and produce ideas. Gaining a nuanced understanding of these usage patterns is essential, yet conventional survey and interview methods remain limited by recall bias, self-presentation effects, and...","url_abs":"https://arxiv.org/abs/2510.09763","url_pdf":"https://arxiv.org/pdf/2510.09763v1","authors":"[\"Donghan Hu\",\"Rameen Mahmood\",\"Annabelle David\",\"Danny Yuxing Huang\"]","published":"2025-10-10T18:12:07Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}