Pedagogical Promise and Peril of AI: A Text Mining Analysis of ChatGPT Research Discussions in Programming Education

arXiv cs.AI / 5/4/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

Key Points

  • A text-mining study analyzes how academic literature discusses ChatGPT in programming education and finds that the framing is not fully clear or consistent across research.
  • Topic modeling identifies four main themes: how to implement ChatGPT pedagogically, its role in student-centered learning and engagement, the need for AI infrastructure and human-AI collaboration, and issues around assessment and model evaluation.
  • The reviewed literature emphasizes classroom practice and learner interaction, but gives comparatively less attention to assessment design and broader institutional governance.
  • ChatGPT is portrayed both as a learning aid (improving explanations, feedback, and efficiency) and as a pedagogical risk (including overreliance, unreliable outputs, and academic integrity problems), motivating calls for responsible integration.
  • The authors argue for stronger assessment methods and governance mechanisms to mitigate risks while leveraging benefits in programming education.

Abstract

GenAI systems such as ChatGPT are increasingly discussed in programming education, but the ways in which the research literature conceptualizes and frames their role remain unclear. This chapter applies text mining to publications indexed in a leading academic database to map scholarly discourse on ChatGPT in programming education. Term frequency analysis, phrase pattern extraction, and topic modeling reveal four dominant themes: pedagogical implementation, student-centered learning and engagement, AI infrastructure and human-AI collaboration, and assessment, prompting, and model evaluation. The literature prioritizes classroom practice and learner interaction, with comparatively limited attention to assessment design and institutional governance. Across studies, ChatGPT is positioned both as a learning aid that supports explanation, feedback, and efficiency and as a pedagogical risk linked to overreliance, unreliable outputs, and academic integrity concerns. These findings support responsible integration and highlight the need for stronger assessment and governance mechanisms.