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

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.

Figures (5)

• Figure 1: Data filtering pipeline. From 33.9M posts in selected subreddits, keyword matching identified 10,435 candidates, of which 3,789 (36.31%) were classified as relevant to generative AI in education by the LLM filter.
• Figure 2: Monthly distribution of relevant posts (January 2023 to October 2024).
• Figure 3: Overall label prevalence across all 3,789 posts. Discourse is dominated by negative emotions and concerns about academic integrity, while positive learning outcomes are rare.
• Figure 4: AI detector discussion by stakeholder group. Students discuss detectors at higher rates than teachers, consistent with students being more directly subject to detection-based enforcement.
• Figure 5: Learning stance distribution by stakeholder group. Students show the largest "neither" share (red), discussing AI without explicit learning framing. Teachers more frequently articulate explicit trade-offs ("both," green) and positions on AI's learning impact.