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

Paulina DeVito, Akhil Vallala, Sean Mcmahon, Yaroslav Hinda, Benjamin Thaw, Hanqi Zhuang, Hari Kalva

TL;DR

This study investigates how Generative AI is perceived in education by teachers and students, using a large Reddit dataset and a modular GPT-4o-based analysis pipeline. It benchmarks GPT-4o against traditional NLP methods for sentiment and topic modeling and adds author-role classification. Key findings show 12 latent topics with Ethics & Integrity as the largest, a general tilt toward negative sentiment, and notable concerns about AI detectors and job security. The work demonstrates a generalizable framework for modeling stakeholder discourse in online learning communities and informs policy and practice for AI integration.

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.

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

TL;DR

This study investigates how Generative AI is perceived in education by teachers and students, using a large Reddit dataset and a modular GPT-4o-based analysis pipeline. It benchmarks GPT-4o against traditional NLP methods for sentiment and topic modeling and adds author-role classification. Key findings show 12 latent topics with Ethics & Integrity as the largest, a general tilt toward negative sentiment, and notable concerns about AI detectors and job security. The work demonstrates a generalizable framework for modeling stakeholder discourse in online learning communities and informs policy and practice for AI integration.

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.

Paper Structure

This paper contains 15 sections, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Opportunities and Challenges of Integrating GAI in Education
  4. Analyzing Educational Discourse Using NLP
  5. Positioning this Study in Relation to Prior Work
  6. Methodology
  7. Data Collection and Preprocessing
  8. Topic Modeling
  9. Sentiment Analysis
  10. Authorship Classification
  11. Results and Discussion
  12. Model Comparisons
  13. Topic Analysis
  14. Academic Integrity
  15. Conclusion

Figures (4)

  • Figure 1: Overview of the study's data preparation and analysis pipeline.
  • Figure 2: Distribution of the 12 topics among Reddit posts and comments.
  • Figure 3: Combined analysis on the Reddit posts based on GPT-4o topic modeling, sentiment analysis, and author classification outputs. Green represents positive sentiment, gray represents neutral sentiment, and red represents negative sentiment.
  • Figure 4: Combined analysis on the Reddit comments based on GPT-4o topic modeling, sentiment analysis, and author classification outputs. Green represents positive sentiment, gray represents neutral sentiment, and red represents negative sentiment.