Pedagogical Promise and Peril of AI: A Text Mining Analysis of ChatGPT Research Discussions in Programming Education
New study maps both promise and peril of AI in the classroom with data-driven insights.
A new text mining analysis of academic literature on ChatGPT in programming education, published as a book chapter by Grume and eight co-authors, maps how the research community conceptualizes generative AI's role. Using term frequency analysis, phrase pattern extraction, and topic modeling on publications from a leading database, the study identifies four dominant themes: pedagogical implementation, student-centered learning and engagement, AI infrastructure and human-AI collaboration, and assessment, prompting, and model evaluation.
The findings reveal a literature that prioritizes classroom practice and learner interaction, with comparatively limited attention to assessment design and institutional governance. Across studies, ChatGPT is consistently positioned as both a learning aid—supporting explanation, feedback, and efficiency—and a pedagogical risk linked to overreliance, unreliable outputs, and academic integrity concerns. The authors argue that responsible integration requires stronger assessment mechanisms and governance frameworks to balance the promise and peril of AI in programming education.
- Four themes identified: pedagogical implementation, student engagement, AI collaboration, and assessment/prompting.
- ChatGPT framed as both a learning aid (efficiency, feedback) and a risk (overreliance, integrity issues).
- Assessment design and institutional governance receive limited attention in current research.
Why It Matters
For educators and policymakers, this study highlights critical gaps in AI governance and assessment that must be addressed.