Generative AI across the disciplines: an activity theory perspective on undergraduate students' AI use and disclosure practices

Generative AI across the disciplines — a multi-institutional quantitative study of 560 undergraduates across five academic domains examining how academic disciplines function as activity systems that shape students' GenAI use and disclosure practices. Jiang, Farag, Lucia, Vetter & Silvestro interpret disciplinary differences in GenAI engagement through the lens of activity theory.

Jiang et al. address the under-studied question of disciplinary differences in students' GenAI use and disclosure. Drawing on activity theory, they treat each academic discipline as an activity system in which students, GenAI applications, course policies, and role expectations interact to shape engagement. Survey data were analyzed with descriptive statistics, chi-square tests of association, and ordinal regression.

Method

• Design: Multi-institutional quantitative survey.
• Sample: 560 undergraduates across five academic domains.
• Analysis: Descriptive statistics, chi-square tests of association, ordinal regression; interpreted through disciplinary contradictions in activity theory.

Key Findings

• Significant associations between disciplinary affiliation and students' GenAI usage frequency and disclosure behaviors.
• Disciplines differ systematically in how students use and disclose GenAI, reflecting each field's norms, policies, and role expectations.
• Patterns are interpreted through disciplinary contradictions — tensions within each activity system (e.g., between course policies and actual practice, or between expectations for students and how GenAI is embedded).

Implications

• For discipline-specific AI education: GenAI use and disclosure are shaped by disciplinary context, not uniform across fields — AI policies and instruction should be discipline-aware.
• For Academic Integrity: disclosure practices vary by discipline, suggesting integrity and disclosure guidance must account for disciplinary norms rather than applying a one-size-fits-all rule.
• For higher education and AI Literacy: supports the view that disciplinary activity systems — norms, policies, role expectations — are key to understanding and shaping student GenAI engagement.

Connected Concepts

• Generative AI
• Higher Ed
• Discipline Specific AIED
• Academic Integrity
• AI Literacy
• Student Experience
• Writing Education

Connected Articles

• Walton Bearman Assessment Judgement 2025 — Judgement in students' work with GenAI on assessment
• Stamatoulis GenAI Use Patterns 2026 — Patterns of GenAI use and academic outcomes
• Qu Wang Disclose Or Not GenAI 2026 — Disclosure and peer influence in GenAI use

Citation

Jiang, J., Farag, I., Lucia, B., Vetter, M. A., & Silvestro, J. (2026). Generative AI across the disciplines: an activity theory perspective on undergraduate students' AI use and disclosure practices. SSRN Working Paper.