A Study on Impact of Junior High School Students’ Programming Learning Effect Based on Generative Artificial Intelligence
Abstract
As artificial intelligence technology rapidly advances, its application in the field of education is becoming increasingly widespread. Generative artificial intelligence tools have become pervasively used in programming learning and they have a certain potential in the programming process. This study aims to explore the use of generative artificial intelligence tools to assist learning in junior high school programming learning and to assess their impact on students' learning outcomes. Through a quasi-experiment, this study analyzed the differences in programming learning outcomes between students using generative artificial intelligence tools and those using traditional learning methods. The results showed that the experimental group was better than control group in terms of IT knowledge comprehension, self-efficacy, and problem-solving ability. Therefore, the use of generative artificial intelligence tools could be helpful to improve the learning effect of students in programming learning.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [148]
A second stream configures AI as a just-in-time tutor, providing stepwise explanations or sample code when students encounter obstacles, with teachers monitoring and intervening only when needed.
“A second stream configures AI as a just-in-time tutor. When students encounter obstacles in programming or laboratory work, they query the model and receive stepwise explanations or sample code; teachers monitor frequency and content via dashboards and intervene only when AI misinterprets a problem or students stall for too long [101,148,150].”
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 [148]
Cites
Generative artificial intelligence
Banh, L., & Strobel, G. (2023) · Electronic Markets · doi:10.1007/s12525-023-00680-1 · Cited by 722 · see citing works
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100005 · Cited by 344 · see citing works
ChatGPT and Software Testing Education: Promises & Perils
Jalil, S., Rafi, S., LaToza, T. D., Moran, K., & Lam, W. (2023) · 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw58534.2023.00078 · Cited by 270 · see citing works
Empowering education development through AIGC: A systematic literature review
Chen, X., Hu, Z., & Wang, C. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12549-7 · Cited by 222 · see citing works
Noh, J., & Lee, J. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-019-09708-w · Cited by 207 · see citing works
Tsai, C. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.11.038 · Cited by 166 · see citing works
Huang, K.-L., Liu, Y., Dong, M.-Q., & Lu, C.-C. (2024) · Learning and Instruction · doi:10.1016/j.learninstruc.2024.101929 · Cited by 112 · see citing works
The Rise of Generative Artificial Intelligence and Its Impact on Education: The Promises and Perils
Murugesan, S., & Cherukuri, A. K. (2023) · Computer · doi:10.1109/mc.2023.3253292 · Cited by 92 · see citing works
Durak, H. Y. (2018) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12281 · Cited by 85 · see citing works
The Future of Education: Generative Artificial Intelligence’s Collaborative Role With Teachers
Kshetri, N. (2023) · IT Professional · doi:10.1109/mitp.2023.3333070 · Cited by 26 · see citing works
Research on the Construction of Intelligent Programming Platform Based on AI-generated Content
Wu, H., Fa, D., Wu, X., Tan, W., Chang, X. P., Gao, Y., & Weng, J. (2023) · International Conference on Education Technology and Computer · doi:10.1145/3629296.3629298 · Cited by 12 · see citing works
Lee, C. Y., & Li, K. (2024) · International Journal of Religion · doi:10.61707/k556b110 · Cited by 7 · see citing works
Tian, H., & Wu, F. (2018) · International Conference of Educational Innovation through Technology (EITT) · doi:10.1109/eitt.2018.00010 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Nathaniel, J., Oyelere, S. S., Suhonen, J. T., & Tedre, M. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100460 · Cited by 37 · see citing works
Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
Unpacking the Effects of GenAI on Cultivating Students’ Computational Thinking: A Meta-Analysis
Xu, J. F., Chen, Z., Chen, M., Li, Y., & Xu, X. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261419586 · Cited by 7 · see citing works
Deng, H., Chen, H., & Dong, Y. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261424211 · Cited by 5 · see citing works
The Impact of Generative AI on Self-Efficacy: Evidence From a Meta-Analysis
Heshmati, H., Li, A., & Tullis, J. G. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261483943
- Authors
- Zhang, H., & Li, M.
- Affiliations
- South China Normal University (CN)
- ORCID
- Venue
- 2024 4th International Conference on Educational Technology (ICET)
- Publisher
- IEEE
- Published
- 2024-09-13
- Pages
- 106-109
- OpenAlex
- W4407215110
- Cited by
- 5
- References
- 13
- Citations by year
- 2026: 3 · 2025: 2
- FWCI
- 2.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Educational Technology (ICET) (publishedVersion)
- 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 › Edcuational Technology Systems
- Keywords
- generative AI, programming education, junior high school students, learning outcomes, self-efficacy, problem-solving skills, AI-assisted learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
