Structured prompt engineering as scaffolding in generative ai-supported project-based network programming learning: a systematic literature review and conceptual framework

  • Muhammad Rizki Ananda
    Universitas Negeri Padang
    Indonesia
  • Titi Sri Wahyuni 
    Universitas Negeri Padang
    Indonesia
  • Fahmi Rizal
    Universitas Negeri Padang
    Indonesia
  • Resmi Darni
    Universitas Negeri Padang
    Indonesia

Abstract

This systematized literature review examines how prompt engineering in generative AI (GenAI) has been used to support Project-Based Learning (PjBL) and develops a conceptual framework for Network Programming courses. Following the PRISMA 2020 statement, records published from 2020 to 2026 were retrieved from IEEE Xplore, ScienceDirect, SpringerLink, Google Scholar, and Garuda/Sinta. Of 310 records identified, 242 remained after duplicate removal, 56 were assessed in full text, and 24 studies met the eligibility criteria. Screening decisions were cross-checked by a second reviewer (Cohen's κ = [....]), and study quality was appraised with the Mixed Methods Appraisal Tool (MMAT). Data were analyzed through a hybrid deductive–inductive thematic analysis following Braun and Clarke's six phases: the four research questions served as a priori categories, sub-themes were coded inductively until no new codes emerged, and coding disagreements were resolved by consensus. The results revealed that structured and scaffolded prompting is consistently associated with deeper engagement than unstructured prompting in programming classes, and that GenAI-supported PjBL preserves student autonomy and creativity when AI interaction is embedded in project stages and students must justify their technical decisions. Only one included study addressed computer networking directly. Reported challenges were excessive dependence on AI-produced solutions, shallow learning, and unclear assessment practices. Unlike existing PjBL–GenAI models, which treat AI as a generally available tool, the proposed three-layer framework (competency, scaffolding, and assessment) specifies when and how prompting is taught, constrained, and evaluated across network programming project milestones, and it yields testable propositions for future empirical research.

Keywords
  • Prompt engineering
  • Generative AI
  • Project-based learning
  • Network programming
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References
  1. Akçapınar, G., & Sidan, E. (2024). AI chatbots in programming education: Guiding success or encouraging plagiarism. Discover Artificial Intelligence, 4(1), 87. https://doi.org/10.1007/s44163-024-00203-7
  2. Amoozadeh, M., Nam, D., Prol, D., Alfageeh, A., Prather, J., Hilton, M., Srinivasa Ragavan, S., & Alipour, A. (2024). Student-AI interaction: A case study of CS1 students. In Proceedings of the 24th Koli Calling International Conference on Computing Education Research (pp. 1–13). ACM. https://doi.org/10.1145/3699538.3699567
  3. Anwar, M., & Caesar, M. (2025). Understanding misunderstandings: Evaluating LLMs on networking questions. ACM SIGCOMM Computer Communication Review, 54(4), 14–24. https://doi.org/10.1145/3717512.3717515
  4. Balakayeva, G., Zeinolla, S., Akzhalova, A., & Kalmenova, G. (2026). Project-based learning in the age of generative AI: Developing student autonomy and creative problem-solving in software engineering education. International Journal of Learning, Teaching and Educational Research, 25(6), 372–392. https://ijlter.org/index.php/ijlter/article/view/17119
  5. Becker, B. A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J., & Santos, E. A. (2023). Programming is hard – or at least it used to be: Educational opportunities and challenges of AI code generation. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (pp. 500–506). ACM. https://doi.org/10.1145/3545945.3569759
  6. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
  7. Bull, C., & Kharrufa, A. (2024). Generative artificial intelligence assistants in software development education: A vision for integrating generative artificial intelligence into educational practice, not instinctively defending against it. IEEE Software, 41(2), 52–59. https://doi.org/10.1109/MS.2023.3300574
  8. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104
  9. Dai, Y., Xiao, J.-Y., Huang, Y., Zhai, X., Wai, F.-C., & Zhang, M. (2025). How generative AI enables an online project-based learning platform: An applied study of learning behavior analysis in undergraduate students. Applied Sciences, 15(5), 2369. https://doi.org/10.3390/app15052369
  10. Denny, P., Kumar, V., & Giacaman, N. (2023). Conversing with Copilot: Exploring prompt engineering for solving CS1 problems using natural language. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (pp. 1136–1142). ACM. https://doi.org/10.1145/3545945.3569823
  11. Denny, P., Leinonen, J., Prather, J., Luxton-Reilly, A., Amarouche, T., Becker, B. A., & Reeves, B. N. (2024). Prompt problems: A new programming exercise for the generative AI era. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education, Vol. 1 (pp. 296–302). ACM. https://doi.org/10.1145/3626252.3630909
  12. Elnaffar, S., Rashidi, F., & Abualkishik, A. Z. (2026). Teaching with AI: A systematic review of chatbots, generative tools, and tutoring systems in programming education. International Journal of Learning, Teaching and Educational Research, 25(1), 1–28. https://doi.org/10.26803/ijlter.25.1.1
  13. Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
  14. Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., O'Cathain, A., Rousseau, M.-C., Vedel, I., & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291. https://doi.org/10.3233/EFI-180221
  15. Indriati, L., Mai, N., & Yeen-Ju, H. T. (2024). Enhancing authentic assessment in large-class design education through authentic project-based learning. International Journal of Learning, Teaching and Educational Research, 23(9), 432–452. https://doi.org/10.26803/ijlter.23.9.22
  16. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
  17. Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., & Grossman, T. (2023). Studying the effect of AI code generators on supporting novice learners in introductory programming. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–23). ACM. https://doi.org/10.1145/3544548.3580919
  18. Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (Version 2.3). University of Durham.
  19. Kokotsaki, D., Menzies, V., & Wiggins, A. (2016). Project-based learning: A review of the literature. Improving Schools, 19(3), 267–277. https://doi.org/10.1177/1365480216659733
  20. Kosar, T., Ostojić, D., Liu, Y. D., & Mernik, M. (2024). Computer science education in ChatGPT era: Experiences from an experiment in a programming course for novice programmers. Mathematics, 12(5), 629. https://doi.org/10.3390/math12050629
  21. Lee, A., & Palmer, M. (2025). Prompt engineering in higher education: A systematic review to help inform curricula. International Journal of Educational Technology in Higher Education, 22(1). https://doi.org/10.1186/s41239-025-00503-7
  22. Liffiton, M., Sheese, B. E., Savelka, J., & Denny, P. (2023). CodeHelp: Using large language models with guardrails for scalable support in programming classes. In Proceedings of the 23rd Koli Calling International Conference on Computing Education Research (pp. 1–11). ACM. https://doi.org/10.1145/3631802.3631830
  23. Manley, E. D., Urness, T., Migunov, A., & Reza, M. A. (2024). Examining student use of AI in CS1 and CS2. Journal of Computing Sciences in Colleges, 39(6), 41–51.
  24. McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282. https://doi.org/10.11613/BM.2012.031
  25. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
  26. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
  27. Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T., Luxton-Reilly, A., MacNeil, S., Petersen, A., Pettit, R., Reeves, B. N., & Savelka, J. (2023). The robots are here: Navigating the generative AI revolution in computing education. In Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education (pp. 108–159). ACM. https://doi.org/10.1145/3623762.3633499
  28. Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., & Briggs, B. (2024). The widening gap: The benefits and harms of generative AI for novice programmers. In Proceedings of the 2024 ACM Conference on International Computing Education Research – Volume 1 (pp. 469–486). ACM. https://doi.org/10.1145/3632620.3671116
  29. Prvan, M., & Ožegović, J. (2020). Methods in teaching computer networks: A literature review. ACM Transactions on Computing Education, 20(3), 1–35. https://doi.org/10.1145/3394963
  30. Qian, Y. (2025). Prompt engineering in education: A systematic review of approaches and educational applications. Journal of Educational Computing Research. https://doi.org/10.1177/07356331251365189
  31. Raihan, N., Siddiq, M. L., Santos, J. C. S., & Zampieri, M. (2025). Large language models in computer science education: A systematic literature review. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 (pp. 938–944). ACM. https://doi.org/10.1145/3641554.3701863
  32. Saeliang, S., & Chatwattana, P. (2025). The project-based learning model via generative artificial intelligence to promote programming skills for vocational students. International Education Studies, 18(3), 1. https://doi.org/10.5539/ies.v18n3p1
  33. Sun, D., Boudouaia, A., Yang, J., & Xu, J. (2024). Investigating students' programming behaviors, interaction qualities and perceptions through prompt-based learning in ChatGPT. Humanities and Social Sciences Communications, 11, 1447. https://doi.org/10.1057/s41599-024-03991-6
  34. Wang, C., Scazzariello, M., Farshin, A., Ferlin, S., Kostić, D., & Chiesa, M. (2024). NetConfEval: Can LLMs facilitate network configuration? Proceedings of the ACM on Networking, 2(CoNEXT2), 1–25. https://doi.org/10.1145/3656296
  35. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.
  36. Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
  37. Zhang, L., Jiang, Q., Xiong, W., & Zhao, W. (2025). Effects of ChatGPT-based human–computer dialogic interaction programming activities on student engagement. Journal of Educational Computing Research, 63(4), 988–1023. https://doi.org/10.1177/07356331251333874
  38. Zheng, C., Yuan, K., Guo, B., Hadi Mogavi, R., Peng, Z., Ma, S., & Ma, X. (2024). Charting the future of AI in project-based learning: A co-design exploration with students. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1–19). ACM. https://doi.org/10.1145/3613904.3642807

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