Development of SEKAPAI: An AI-Based Scaffolding Platform for Programming Education
Article Sidebar
Main Article Content
Abstract
The rapid adoption of generative artificial intelligence in programming education has raised concerns regarding student over-dependence and the erosion of computational thinking skills. This study presents the design and internal validation of SEKAPAI, an AI-based scaffolding platform developed to support computational thinking while promoting responsible use of generative AI. Using an Agile-oriented Research and Development approach, SEKAPAI integrates three adaptive scaffolding modules—Solution Assessment, Code Assessment, and Free Interaction—to deliver context-aware feedback without providing direct solutions. System requirements were derived through stakeholder analysis and translated into a modular, web-based architecture supported by GPT-based services. Internal validation was conducted using comprehensive black-box testing to evaluate functional correctness, feedback behavior, and alignment with computational thinking components. The results indicate that SEKAPAI operates reliably across core system features and consistently implements progressive scaffolding strategies that regulate AI assistance. This study demonstrates how pedagogical scaffolding principles can be operationalized within AI-assisted learning systems and provides a technically feasible reference model for responsible AI integration in programming education.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
How to Cite
References
Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25(4), 471–533. https://doi.org/10.1207/S15516709COG2504_1
Dela Calzada, K. P. (2024). Anti-dependency teaching strategy for innovation in the age of ai among technology-based students. Environment and Social Psychology, 9(8). https://doi.org/10.59429/ESP.V9I8.3026
Faber, T. J. E., Dankbaar, M. E. W., van den Broek, W. W., Bruinink, L. J., Hogeveen, M., & van Merriënboer, J. J. G. (2024). Effects of adaptive scaffolding on performance, cognitive load and engagement in game-based learning: a randomized controlled trial. BMC Medical Education, 24(1), 943-. https://doi.org/10.1186/S12909-024-05698-3
Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A swot analysis of chatgpt: implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846
Gheorghe, A.-M., Gheorghe, I. D., & Iatan, I. L. (2020). Agile software development. Informatica Economica, 24(2/2020), 90–100. https://doi.org/10.24818/issn14531305/24.2.2020.08
Gunawan, R., Wibisono, Y. P., Primasari, C. H., Budiyanto, D., & Cininta, M. (2023). Blackbox testing on virtual reality gamelan saron using equivalence partition method. Jurnal Buana Informatika, 14(01), 11–19. https://doi.org/10.24002/JBI.V14I01.6606
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
Larasati, I., Yusril, A. N., & Zukri, P. Al. (2021). Systematic literature review analisis metode agile dalam pengembangan aplikasi mobile. Sistemasi: Jurnal Sistem Informasi, 10(2), 369–380. https://doi.org/10.32520/STMSI.V10I2.1237
Laval, J., Fleury, A., Karami, A. B., Lebis, A., Lozenguez, G., Pinot, R., & Vermeulen, M. (2021). Toward an innovative educational method to train students to agile approaches in higher education: the a.l.p.e.s. Education Sciences, 11(6), 267. https://doi.org/10.3390/EDUCSCI11060267
Liao, J., Zhong, L., Zhe, L., Xu, H., Liu, M., & Xie, T. (2024). Scaffolding computational thinking with chatgpt. IEEE Transactions on Learning Technologies, 17, 1668–1682. https://doi.org/10.1109/TLT.2024.3392896
Mhlanga, D. (2023). Open ai in education, the responsible and ethical use of chatgpt towards lifelong learning. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.4354422
Moon, J., Do, J., Lee, D., & Choi, G. W. (2020). A conceptual framework for teaching computational thinking in personalized oers. Smart Learning Environments 2020 7:1, 7(1), 6-. https://doi.org/10.1186/S40561-019-0108-Z
Nouri, J., Zhang, L., Mannila, L., & Norén, E. (2020). Development of computational thinking, digital competence and 21st century skills when learning programming in k-9. Education Inquiry, 11(1), 1–17. https://doi.org/10.1080/20004508.2019.1627844
Raghavan, S., M, S., S, S., & Devi M, Dr. D. (2021). Student support system an one stop portal. International Journal of Advanced Research in Science, Communication and Technology, 66–71. https://doi.org/10.48175/IJARSCT-1211
Rahman, M. M., & Watanobe, Y. (2023). Chatgpt for education and research: opportunities, threats, and strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/APP13095783
Rowlett, P., & Corner, A. S. (2022). Flexible, student-centred remote learning for programming skills development. International Journal of Mathematical Education in Science and Technology, 53(3), 619–626. https://doi.org/10.1080/0020739X.2021.1989067
Saputra, J. P. B., Prabowo, H., Gaol, F. L., & Hertono, G. F. (2025). Development of gamification-based learning management system (lms) with agile approach and personalization of fslsm learning style to improve learning effectiveness. Journal of Applied Data Sciences, 6(1), 714–725. https://doi.org/10.47738/JADS.V6I1.486
Sayed, W. S., Noeman, A. M., Abdellatif, A., Abdelrazek, M., Badawy, M. G., Hamed, A., & El-Tantawy, S. (2022). AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging e-learning platform. Multimedia Tools and Applications, 82(3), 3303–3333. https://doi.org/10.1007/S11042-022-13076-8
Tiwari, R. (2023). The integration of ai and machine learning in education and its potential to personalize and improve student learning experiences. International Journal of Scientific Research in Engineering and Management, 07(02). https://doi.org/10.55041/IJSREM17645
Van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher–student interaction: a decade of research. Educational Psychology Review, 22(3), 271–296. https://doi.org/10.1007/S10648-010-9127-6
Weintrop, D., Wise Rutstein, D., Bienkowski, M., & McGee, S. (2021). Assessing computational thinking: an overview of the field. Computer Science Education, 31(2), 113–116. https://doi.org/10.1080/08993408.2021.1918380
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (ai)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/J.CAEAI.2023.100147
Yusoff, K. M., Ashaari, N. S., Wook, T. S. M. T., & Ali, N. M. (2020). Analysis on the requirements of computational thinking skills to overcome the difficulties in learning programming. International Journal of Advanced Computer Science and Applications, 11(3), 244–253. https://doi.org/10.14569/IJACSA.2020.0110329
Zhen, Z. (2024). Research and practice of agile software development methods. Applied and Computational Engineering, 114(1), 186–190. https://doi.org/10.54254/2755-2721/2024.18284