Date
Publisher
arXiv
Generative Artificial Intelligence (GenAI) has prompted significant discussion in education, yet large-scale empirical evidence on how students and teachers perceive and navigate this shift remains limited. We analyse 270k AI-related Reddit posts and comments from 26 education-related subreddits spanning higher education, K-12 teaching, and professional training between November 2022 and April 2026. Topic modelling reveals seventeen themes covering academic integrity, teaching & pedagogy, career anxiety, policy, and niche professional contexts. Discourse evolves from an early detection-and-evasion arms race into a sustained enforcement regime that constructive integration only begins to challenge in mid-2024. Stakeholder communities differ sharply: K-12 teachers foreground cognitive dependency, academics focus on AI detection and deliberation, and professional-programme students concentrate on career anxiety. Sentiment correlates strongly negatively with engagement, showing adversarial enforcement themes mobilise communities far more than constructive integration discourse. Examining where faculty and students meet, we find 17% of threads are cross-role, and one third of such contact occurs in the adversarial themes AI Detection and Misconduct Enforcement. Students initiate 68% of mixed threads, but faculty produce most cross-role replies. Mixed threads contain 2-3 times more records and last 2-4 times longer than same-role threads, making adversarial integrity disputes the center of sustained faculty-student contact. We discuss implications for governance, pedagogical design, and cross-role contact design. The code and data is available at https://github.com/tugrulz/genai-edu
Study at a glance
- Question: How do teachers and students discuss generative AI across education communities, and how do they interact?
- Setting and data: 26 education subreddits across the Anglosphere, November 2022 to April 2026, 270,929 AI-related Reddit posts and comments.
- Method: Topic modeling (LDA), sentiment analysis, and LLM-based role classification.
Why it matters
A gap persists between university generative AI policies and the lived realities of instructors and students. Large-scale empirical evidence on how both groups perceive, navigate, and discuss generative AI across education remains limited.
Key findings
- Academic integrity concerns dominate discussion, with misconduct enforcement, AI detection, student narratives, and evasion accounting for over one-third of all generative AI records.
- Sentiment correlates strongly negatively with engagement, showing that adversarial enforcement issues mobilize communities far more than constructive integration topics.
- Faculty and students interact in 17.4% of threads, with about one-third of this cross-role contact centered on AI detection disputes and misconduct enforcement.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Why use AI?
Study design
