When Generative AI Meets Socratic Method: Investigating Programming Learning Dynamics Through Behaviours, Interaction Qualities and Perceptions. - Sun, Dan; Zheng, Yi; Xu, Jie; Yang, Zhanshan (Journal of Computer Assisted Learning, 2026)
Background: The integration of generative artificial intelligence (GAI) tools like GPT into programming education offers transformative potential through personalised guidance and instant feedback, yet risks fostering overreliance and superficial learning due to their tendency to deliver direct, context‐free answers. Objectives: This quasi‐experimental study addresses this gap by proposing a Socratic questioning framework to optimise GAI‐facilitated programming instruction, emphasising critical thinking over passive solution retrieval. Methods: We compared two pedagogical approaches: GAI‐Scaffolded Learning (GSL), where GPT employs structured Socratic dialogue to guide problem‐solving and GAI‐Direct Learning (GDL), which provides immediate answers without guided inquiry. This research collected learners' programming behaviours, interactions data with GPT from screen recordings and platform log data and perceptions data. This research further utilised multiple learning analytics approaches (i.e., click stream analysis, lag‐sequential analysis, epistemic network analysis [ENA] and statistics) to compare learners' programming behaviours, interaction patterns and perceptions under two approaches. Results and Conclusions: Through an analysis of 80 college students' programming behaviours, interaction qualities and perceptions, we found some intriguing results. First, GSL engaged in cyclical, reflective practices (debugging, Socratic questioning, console use), while GDL prioritised rapid fixes via trial‐and‐error with GPT code, risking superficial mimicry and over‐reliance on external resources. Second, ENA highlighted GSL's deeper engagement through interconnected feedback, emotional support and iterative inquiry, reducing frustration and sustaining persistence and GDL interactions focused on surface‐level queries, lacking scaffolding for emotional/heuristic integration. Third, GSL maintained positive attitudes due to structured prompts aligning expectations and easing cognitive load. GDL attitudes declined from mismatched expectations and frustration. Implications: Based on these findings, the study proposes pedagogical and developmental implications for future design and development of AI‐augmented curricula, providing actionable insights for educators seeking to harness GAI's potential while nurturing critical thinking in programming education. Lay Summary: What is currently known about this topic? ○Generative AI provides instant feedback but risks student overreliance on code.○Direct AI answers often lead to superficial learning and lack of critical thinking.○Personalised guidance is vital for effective programming education.○Scaffolding is a proven method to help students solve complex problems.What does this paper add? ○It introduces a Socratic questioning framework to guide AI‐led instruction.○GSL students showed more reflective debugging habits than GDL students.○ENA analysis shows that guided AI dialogue reduces student frustration levels.○The study proves structured prompts better maintain positive student attitudes.Implications for practice and/or policy ○Educators should design AI prompts that ask questions rather than give answers.○AI‐augmented curricula must prioritise critical thinking over code generation.○Training should help students use AI as a tutor rather than a simple tool.○Policy should encourage AI tools that provide emotional and heuristic support.