Effects of ChatGPT-Based Human–Computer Dialogic Interaction Programming Activities on Student Engagement
Abstract
This study seeks to deepen the understanding of the direct and indirect effects of human–computer dialogic interaction programming activities, facilitated by ChatGPT, on student engagement. Data were collected from 109 Chinese high school students who engaged in programming tasks using either ChatGPT-driven dialogic interaction or traditional pair programming. A quasi-experimental analysis revealed that ChatGPT-based programming activities remarkably boost student engagement, outperforming pair programming in behavioral, cognitive, and emotional dimensions. Results demonstrated that such activities help minimize off-task behaviors, promote higher-order cognitive skills, and foster greater interest in programming. Additionally, these interactions enhance students’ self-efficacy and reduce learning anxiety. The findings underscore the potential of ChatGPT-driven dialogic interaction in programming education. This study offers practical recommendations to enhance student engagement in programming learning.
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GenAI functions as a reflective evaluator by continually tracking students' help-seeking behaviors and programming dialogues, automatically calibrating the granularity of its prompts, and generating visual or textual feedback to help students monitor their progress and adjust learning strategies, thus enhancing metacognitive self-regulation.
“Third, GenAI functions as a reflective evaluator. By continually tracking students' help-seeking behaviors and programming dialogues, the system automatically calibrates the granularity of its prompts and generates visual or textual feedback to help students monitor their progress and adjust learning strategies, thus enhancing metacognitive selfregulation [103,141,143,149].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [141]
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Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
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- Authors
- Zhang, L., Jiang, Q., Xiong, W., & Zhao, W.
- Affiliations
- Northeast Normal University (CN)
- Education University of Hong Kong (HK)
- Corresponding author
- Jiang, Qiang
- Venue
- Journal of Educational Computing Research
- Publisher
- SAGE Publications
- Published
- 2025-04-14
- Volume
- 63
- Issue
- 4
- Pages
- 988-1023
- ISSN
- 0735-6331 (ISSN-L)
- 1541-4140
- OpenAlex
- W4409435267
- Cited by
- 15
- References
- 91
- Citations by year
- 2026: 11 · 2025: 4
- FWCI
- 13.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Computing Research (publishedVersion)
- Funders
- National Natural Science Foundation of China (62077012)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- ChatGPT, student engagement, programming education, pair programming, high school students, off-task behavior, self-efficacy, student anxiety, higher-order thinking skills
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