Group-Differentiated Discourse on Generative AI in High School Education: A Case Study of Reddit Communities

2026-03-25

AI Summary

This study investigates discursive differences among students, teachers, and mixed communities regarding generative AI in secondary education, focusing on its impact on learning, academic integrity, AI detection, and emotional responses. Analyzing 3,789 posts from five education-related Reddit subreddits through keyword retrieval, manual filtering, large language model–assisted annotation, and statistical testing, the research reveals distinct positional stances: teachers emphasize the dual effects of AI on learning, whereas students foreground concerns about grade-related pressures and risks of misidentification. Notably, AI detection tools function not merely as technical instruments but as governance mechanisms that elicit pronounced negative affect—particularly among students. In light of these findings, the paper advocates replacing detection-oriented approaches with process-based assessment to reconceptualize frameworks for academic integrity.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Humans and AI: Emotional Intelligence

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

In this paper, we study how different Reddit communities discuss generative AI in high school education, focusing on learning, academic integrity, AI detection, and emotional framing. Using 3,789 posts from five education-related subreddits, we compare student, teacher, and mixed communities using a pipeline that combines keyword retrieval, human-validated relevance filtering, LLM-assisted annotation, and statistical tests of group differences. We find that stakeholder position strongly shapes discourse: teachers are more likely to articulate explicit pedagogical trade-offs, simultaneously framing AI as both beneficial and harmful for learning, whereas students more often discuss AI tactically in relation to accusations, grades, and enforcement. Across all groups, detector-related discourse is associated with significantly higher negative emotion, with larger effects for students and mixed communities than for teachers. These results suggest that AI detectors function not only as contested technical tools but also as governance mechanisms that impose asymmetric emotional burdens on those subject to institutional enforcement. Finally, we argue that detection-based enforcement should not serve as a primary academic-integrity strategy and that process-based assessment offers a fairer alternative for verifying authorship in AI-mediated classrooms.

Problem

• generative AI
• high school education
• academic integrity
• AI detection
• discourse analysis

Innovation

• LLM-assisted annotation
• group-differentiated discourse analysis
• AI detection governance
• emotional framing
• process-based assessment