ChatGPT vs Teachers vs Students: Large-Scale Analysis of Generative AI Discourse in Education Communities on Reddit

2026-05-17

AI Summary

This study addresses the current lack of large-scale empirical research on how students and educators perceive and respond to the educational implications of generative AI. By analyzing 270,000 posts across 26 education-related Reddit subreddits from November 2022 to April 2026, the research employs large-scale web text mining, topic modeling, and sentiment analysis to systematically uncover discussion themes, affective orientations, and interaction patterns among students and teachers regarding AI. The analysis identifies 17 core topics and reveals, for the first time, pronounced and persistent disagreements between students and instructors concerning AI-related academic integrity, which emerges as a central context for cross-role interaction. Notably, 17% of discussions involve student–teacher exchanges, one-third of which center on AI detection and enforcement of policy violations; furthermore, negative sentiment significantly drives community engagement, with mixed-role posts exhibiting more frequent and sustained interactions.

Abstract

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

Problem

• Generative AI
• education
• student-teacher discourse
• academic integrity
• Reddit

Innovation

• generative AI
• discourse analysis
• cross-role interaction
• academic integrity
• topic modeling