A Generative Artificial Intelligence (AI)-Based Human-Computer Collaborative Programming Learning Method to Improve Computational Thinking, Learning Attitudes, and Learning Achievement
Abstract
Human-computer collaboration is an effective way to learn programming courses. However, most existing human-computer collaborative programming learning is supported by traditional computers with a relatively low level of personalized interaction, which greatly limits the efficiency of students’ efficiency of programming learning and development of computational thinking. To address the above issues, this study introduces generative AI into human-computer collaborative programming learning and proposes a dialogue-negotiated human-computer collaborative programming learning method based on generative AI. The method focuses on the problems-solving process and constructs multiple agents through Prompt design, which enable students to improve their computational thinking and master programming skills in the process of human-computer interaction for problem-solving. Finally, a quasi-experiment was conducted to verify the effectiveness of the proposed method in a 10th grade computer programming course in a high school. 43 students in the experimental group learned with the proposed method, while 42 students in the control group adopted the traditional computer-supported human-computer collaborative programming learning method. The experimental results showed that the proposed method more significantly improved students’ computational thinking, programming learning attitudes, and learning achievement. This study provides theoretical foundations and application reference for future generative AI-assisted human-computer collaborative teaching.
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A second major objective is to situate the development of computational thinking and programming competence within authentic practices, using AI-powered programming environments and intelligent feedback mechanisms to lower entry barriers, provide personalized guidance, and ensure timely feedback for novice learners.
“A second major objective is to situate the development of computational thinking and programming competence within authentic practices. Here, the target is to support students in refining their algorithmic reasoning, problem decomposition, and coding practices. The integration of AI-powered programming environments and intelligent feedback mechanisms serves to meet these goals, particularly by low”
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 [95]
Evidence spans STEM and the humanities, including gains in understanding physics and programming, as well as improved reading and poetry interpretation.
“Evidence spans STEM and the humanities, including gains in understanding physics [91,101,104,147] and programming [95,148,150], as well as improved reading and poetry interpretation [72,75,81,116].”
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 [95]
Affective gains often translate into identity-level shifts: students' self-efficacy and domain confidence increase-particularly in programming, mathematics, and writing-with corresponding reductions in learning anxiety.
“These affective gains often translate into identity-level shifts: students' self-efficacy and domain confidence increase-particularly in programming [95], mathematics [77,103], and writing [86]-with corresponding reductions in learning anxiety [80,141].”
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 [95]
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Lai, C.-H., Lin, Y.-H., Chien, W., & Yang, Y.-S. (2026) · Electronics · doi:10.3390/electronics15173967
Chiu, M., Huang, Z.-H., & Kao, M.-C. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21896-1
Liu, C., Wei, L., & Tseng, J. C. R. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14107-9
“This tool not only enables the generation, clarification, and explanation of code examples in response to students’ queries, thereby offering immediate content support (Zhao et al., 2025), but also presents a range of signifi - cant advantages over traditional programming tools.”
Integrating Virtual Assistants into the Programming Learning Process Within IT Studies
Veljić, A., Marković, M., & Maricic, S. (2026) · International Conference on Electrical, Electronic and Computing Engineering (IcETRAN) · doi:10.1109/icetran71739.2026.11727842
“In these frameworks, virtual assistants are used as intelligent agents to provide detailed explanations for the codes, assist in debugging, or provide students with practice exercises based on their individual learning needs [10].”
Khodjaev, B. K., Masalimova, A. R., Chistyakov, A. A., & Ay, Y. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1971476
- Authors
- Zhao, G., Yang, L., Hu, B., & Wang, J.
- Affiliations
- Central China Normal University (CN)
- Shenzhen Experimental School, Guangdong City, China
- Corresponding author
- Yang, Lijun
- Venue
- Journal of Educational Computing Research
- Publisher
- SAGE Publications
- Published
- 2025-04-17
- Volume
- 63
- Issue
- 5
- Pages
- 1059-1087
- ISSN
- 0735-6331 (ISSN-L)
- 1541-4140
- OpenAlex
- W4409555455
- Cited by
- 27
- References
- 87
- Citations by year
- 2026: 21 · 2025: 6
- FWCI
- 24.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Computing Research (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (CCNU22JC011)
- National Natural Science Foundation of China (62377020)
- Thematic Research Project 2.0 on Improving Information and Technology Application Ability of Primary and Secondary School Teachers in Guangdong Province, China (TSGCKT2023072)
- Open Fund of Hubei Key Laboratory of Digital Education (F2024K02)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI, programming education, computational thinking, prompt engineering, multi-agent systems, learning attitude, academic performance, AI in teaching
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