Generative AI (GAI) technologies are quickly reshaping the educational landscape. As adoption accelerates, understanding how students and educators perceive these tools is essential. This study presents one of the most comprehensive analyses to date of stakeholder discourse dynamics on GAI in education using social media data. Our dataset includes 1,199 Reddit posts and 13,959 corresponding top-level comments. We apply sentiment analysis, topic modeling, and author classification. To support this, we propose and validate a modular framework that leverages prompt-based large language models (LLMs) for analysis of online social discourse, and we evaluate this framework against classical natural language processing (NLP) models. Our GPT-4o pipeline consistently outperforms prior approaches across all tasks. For example, it achieved 90.6% accuracy in sentiment analysis against gold-standard human annotations. Topic extraction uncovered 12 latent topics in the public discourse with varying sentiment and author distributions. Teachers and students convey optimism about GAI's potential for personalized learning and productivity in higher education. However, key differences emerged: students often voice distress over false accusations of cheating by AI detectors, while teachers generally express concern about job security, academic integrity, and institutional pressures to adopt GAI tools. These contrasting perspectives highlight the tension between innovation and oversight in GAI-enabled learning environments. Our findings suggest a need for clearer institutional policies, more transparent GAI integration practices, and support mechanisms for both educators and students. More broadly, this study demonstrates the potential of LLM-based frameworks for modeling stakeholder discourse within online communities.

翻译：生成式人工智能（GAI）技术正在迅速重塑教育格局。随着采用速度加快，理解学生和教育工作者如何看待这些工具至关重要。本研究利用社交媒体数据，对教育领域GAI利益相关者的话语动态进行了迄今为止最全面的分析之一。我们的数据集包含1,199篇Reddit帖子和13,959条相应的顶级评论。我们应用了情感分析、主题建模和作者分类方法。为此，我们提出并验证了一个模块化框架，该框架利用基于提示的大语言模型（LLM）来分析在线社交话语，并将该框架与经典自然语言处理（NLP）模型进行了评估比较。我们的GPT-4o流程在所有任务中均持续优于先前方法。例如，在情感分析任务中，相对于黄金标准的人工标注，其准确率达到90.6%。主题提取揭示了公众话语中的12个潜在主题，这些主题具有不同的情感和作者分布。教师和学生都对GAI在高等教育中实现个性化学习和提升生产力的潜力表示乐观。然而，关键差异显现：学生常常对AI检测器错误指控作弊表示苦恼，而教师则普遍对工作保障、学术诚信以及采用GAI工具的制度压力表示担忧。这些截然不同的观点凸显了在GAI赋能的学习环境中创新与监管之间的张力。我们的研究结果表明，需要制定更清晰的制度政策、更透明的GAI整合实践，以及为教育工作者和学生提供支持机制。更广泛地说，本研究展示了基于LLM的框架在建模在线社区内利益相关者话语方面的潜力。