Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse

2025-06-19

AI Summary

This study investigates divergent attitudes toward generative AI (GAI) in education among teachers and students on Reddit. Analyzing 1,200 posts and 13,900 comments, we propose the first modular, LLM-driven analytical framework integrating GPT-4o prompt engineering, fine-grained sentiment analysis, dynamic topic modeling, and author-role classification—rigorously benchmarked against traditional NLP models. Our method achieves 90.6% accuracy against human annotations and identifies 12 semantically coherent themes. Results reveal a fundamental attitudinal divide: educators prioritize concerns about false accusations of AI cheating, occupational insecurity, and institutional pressures, whereas students emphasize personalized learning benefits yet express widespread anxiety regarding AI governance. This work establishes a novel, interpretable, multi-task computational discourse analysis paradigm for studying societal acceptance of educational AI.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Analyze teacher and student perceptions of Generative AI in education
• Evaluate LLM-based framework for social media discourse analysis
• Identify tensions between innovation and oversight in GAI adoption

Innovation

• Modular framework using prompt-based LLMs
• GPT-4o pipeline outperforms classical NLP
• Sentiment analysis with 90.6% accuracy