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

翻译：生成式人工智能（GenAI）已在教育领域引发广泛讨论，然而关于学生和教师如何感知并应对这一变革的大规模实证证据仍然有限。我们分析了 2022 年 11 月至 2026 年 4 月期间，来自 26 个教育相关 subreddit（涵盖高等教育、K-12 教学和职业培训）的 27 万条与 AI 相关的 Reddit 帖子及评论。主题建模揭示了 17 个主题，涵盖学术诚信、教学法与教学法、职业焦虑、政策以及特定专业情境。话语发展经历了从早期的“检测与规避”的军备竞赛，演变为持续的执法制度，而建设性整合直到 2024 年中期才开始对此提出挑战。利益相关者社群之间存在显著差异：K-12 教师更关注认知依赖，学者聚焦于 AI 检测与深思熟虑，而专业项目学生则集中于职业焦虑。情感与参与度呈强烈负相关，表明对抗性执法主题比建设性整合话语更能动员社群。在考察师生接触的界面时，我们发现 17% 的讨论串涉及跨角色互动，其中三分之一发生在对抗性主题“AI 检测”和“违规执法”中。学生发起了 68% 的混合角色讨论串，但教师贡献了大部分跨角色回复。混合角色讨论串的记录数是同角色讨论串的 2-3 倍，持续时间长 2-4 倍，这使得对抗性的诚信争议成为师生持续接触的核心。我们讨论了其对治理、教学法设计以及跨角色接触设计的启示。代码和数据可在 https://github.com/tugrulz/genai-edu 获取。