Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911203662495744 |
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| author | Hu, Donghan Mahmood, Rameen David, Annabelle Huang, Danny Yuxing |
| author_facet | Hu, Donghan Mahmood, Rameen David, Annabelle Huang, Danny Yuxing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09763 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices Hu, Donghan Mahmood, Rameen David, Annabelle Huang, Danny Yuxing Human-Computer Interaction 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. |
| title | Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.09763 |