When Generative AI Meets Socratic Method: Investigating Programming Learning Dynamics through Behaviours, Interaction Qualities and Perceptions
Saved in:
| Title: | When Generative AI Meets Socratic Method: Investigating Programming Learning Dynamics through Behaviours, Interaction Qualities and Perceptions |
|---|---|
| Language: | English |
| Authors: | Dan Sun, Yi Zheng, Jie Xu (ORCID |
| Source: | Journal of Computer Assisted Learning. 2026 42(2). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 17 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | College Students, Programming, Troubleshooting, Questioning Techniques, Artificial Intelligence, Technology Uses in Education, Critical Thinking, Problem Solving, Learning Analytics, Epistemology, Network Analysis, Interaction, Feedback (Response), Scaffolding (Teaching Technique), Computer Assisted Instruction, Prompting |
| DOI: | 10.1002/jcal.70210 |
| ISSN: | 0266-4909 1365-2729 |
| Abstract: | 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1500508 |
| Database: | ERIC |
| Abstract: | 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. |
|---|---|
| ISSN: | 0266-4909 1365-2729 |
| DOI: | 10.1002/jcal.70210 |
