A GAI-Based Chatbot for Scaffolding Self-Regulated Learning
Insights from a Design-Based Research Approach
DOI:
https://doi.org/10.18608/jla.2026.9143Keywords:
higher education, learning analytics, generative AI, design-based research, self-reflection, research paperAbstract
Generative AI (GAI) is increasingly integrated into education, particularly through chatbots that support students without direct human intervention. While these tools show promise as personalized learning companions, concerns persist about their potential to foster overreliance, limit creativity, and hinder the development of critical thinking. These risks highlight the need to strengthen students’ metacognitive skills and promote structured self-reflection. This paper presents the design and iterative development of a GAI-based chatbot aimed at scaffolding self-regulated learning. Through three design cycles involving 276 students in 10 courses, the chatbot evolved from a static assistant into a dynamic, course-integrated tool capable of supporting personalized, Socratic-style dialogue. Thematic analysis of diverse qualitative data sources revealed that students seek scaffolded support, such as human tutoring, and require explicit guidance to engage meaningfully with AI. Findings emphasize the importance of dialogic competence, personalization, and educator involvement in shaping effective AI-mediated reflection. This study underscores the need for pedagogically grounded AI tools that position chatbots as collaborative agents, complementing rather than replacing the roles of teachers and learners. It advocates for reflective teaching practices that clearly define the responsibilities of students, educators, and AI systems to ensure that GAI enhances deep learning and independent thought.
References
Anderson, T., & Shattuck, J. (2012). Design-based research: A decade of progress in education research? Educational Researcher, 41(1), 16–25. https://doi.org/10.3102/0013189X11428813
Barab, S. (2014). Design-based research: A methodological toolkit for engineering change. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences, 2, 151–170. Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.011
Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64(1), 417–444. https://doi.org/10.1146/annurev-psych-113011-143823
Buckingham Shum, S. (2024, January). Generative AI for critical analysis: Practical tools, cognitive offloading and human agency. In CEUR Workshop Proceedings. CEUR-WS. https://ceur-ws.org/Vol-3667/GenAILA-paper4.pdf
Chang, C. Y., Hwang, G. J., & Gau, M. L. (2022). Promoting students’ learning achievement and self‐efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53(1), 171–188. https://doi.org/10.1111/bjet.13158
Chu, X., Wang, M., Spector, J. M., Chen, N.-S., Chai, C. S., Hwang, G.-J., & Zhai, X. (2025). Enhancing the flipped classroom model with generative AI and Metaverse technologies: Insights from lag sequential and epistemic network analysis. Educational Technology Research and Development, 73, 1539–1567. https://doi.org/10.1007/s11423-025-10457-2
Dang, C. T., & Nguyen, A. (2025). Distinguishing fact from fiction: Student traits, attitudes, and AI hallucination detection in business school assessment. arXiv. https://arxiv.org/abs/2506.00050
Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338–349. https://doi.org/10.1111/jcal.12288
Dishman, E. (2003). Designing for the new old: Asking, observing and performing future elders. In B. Laurel (Ed.), Design research: Methods and perspectives (pp. 41–48). The MIT Press.
Ferrettini, G., Nascimento, A. C. A., Pérez-Sanagustin, M., & Hilliger, I. (2026). SIMBA: A tool for designing generative AI agents for reflective learning and critical thinking. In K. Tammets, S. Sosnovsky, R. Ferreira Mello, G. Pishtari, & T. Nazaretsky (Eds.), Two decades of TEL. From lessons learnt to challenges ahead (pp. 313–318). Springer, Cham. https://doi.org/10.1007/978-3-032-03873-9_42
Fischer, I., Sweeney, S., Lucas, M., & Gupta, N. (2024). Making sense of generative AI for assessment: Contrasting student claims and assessor evaluations. The International Journal of Management Education, 22(3), Article 101081. https://doi.org/10.1016/j.ijme.2024.101081
Fryer, L. K., & Vermunt, J. D. (2018). Regulating approaches to learning: Testing learning strategy convergences across a year at university. British Journal of Educational Psychology, 88(1), 21–41. https://doi.org/10.1111/bjep.12169
Gomez, C., Cho, S. M., Ke, S., Huang, C. M., & Unberath, M. (2025). Human–AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review. Frontiers in Computer Science, 6, 1521066. https://doi.org/10.3389/fcomp.2024.1521066
Gounder, N., Faniran, I. V. T., & Faloye, S. T. (2024, May). Integrating adaptive intelligent tutoring systems into private higher educational institutes. In 2024 IST-Africa Conference (IST-Africa) (pp. 1–13). IEEE. https://doi.org/10.23919/IST-Africa63983.2024.10569565
Goyal, A. (2025). AI as a cognitive partner: A systematic review of the influence of AI on metacognition and self-reflection in critical thinking. International Journal of Innovative Science and Research Technology, 10(3), 1231–1238. https://doi.org/10.38124/ijisrt/25mar1427
Guan, R., Raković, M., Chen, G., & Gašević, D. (2025). How educational chatbots support self-regulated learning? A systematic review of the literature. Education and Information Technologies, 30(4), 4493–4518. https://doi.org/10.1007/s10639-024-12881-y
Han, I., Ji, H., Jin, S., & Choi, K. (2025). Mobile-based artificial intelligence chatbot for self-regulated learning in a hybrid flipped classroom. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-025-09434-8
Hew, K. F., Huang, W., Du, J., & Jia, C. (2023). Using chatbots to support student goal setting and social presence in fully online activities: Learner engagement and perceptions. Journal of Computing in Higher Education, 35(1), 40–68. https://doi.org/10.1007/s12528-022-09338-x
Hilliger, I., Pérez-Sanagustín, M., Villalobos, E., Ferreira, R., & Gonzalez, C. (2026). Scaffolding learning scenarios with a Socratic chatbot: Insights from educators. In K. Tammets, S. Sosnovsky, R. Ferreira Mello, G. Pishtari, & T. Nazaretsky (Eds.), Two decades of TEL. From lessons learnt to challenges ahead (pp. 155–161). Springer, Cham. https://doi.org/10.1007/978-3-032-03873-9_17
Huang, W., & Hew, K. F. (2024). Facilitating online self-regulated learning and social presence using chatbots: Evidence-based design principles. IEEE Transactions on Learning Technologies, 18, 56–71. https://doi.org/10.1109/TLT.2024.3523199
Kim, Y., & Steiner, P. (2016). Quasi-experimental designs for causal inference. Educational Psychologist, 51(3–4), 395–405.
Kumar, H., Musabirov, I., Reza, M., Shi, J., Wang, X., Williams, J. J., Kuzminykh, A., & Liut, M. (2023). Impact of guidance and interaction strategies for LLM use on learner performance and perception. arXiv preprint arXiv:2310.13712. https://doi.org/10.48550/arXiv.2310.13712
Lai, J. W., Qiu, W., Thway, M., Zhang, L., Jamil, N. B., Su, C. L., Ng, S. S. H., & Lim, F. S. (2025). Leveraging process-action epistemic network analysis to illuminate student self-regulated learning with a Socratic chatbot. Journal of Learning Analytics, 12(1), 32–49. https://doi.org/10.18608/jla.2025.8549
Lee, Y. F., Hwang, G. J., & Chen, P. Y. (2025). Technology-based interactive guidance to promote learning performance and self-regulation: A chatbot-assisted self-regulated learning approach. Educational Technology Research and Development, 73, 2279–2304. https://doi.org/10.1007/s11423-025-10478-x
Lewis, D. G. R., Carlson, S. E., Riesbeck, C. K., Lu, K. J., Gerber, E. M., & Easterday, M. W. (2020). The logic of effective iteration in design-based research. In Proceedings of the 14th International Conference of the Learning Sciences (ICLS 2020) (pp. 1149–1156). ISLS. https://repository.isls.org/bitstream/1/6308/1/1149-1156.pdf
Liu, Q., Wang, Y., & Chang, Y. (2025). Exploring the effects of scaffolded reflective learning on student teachers’ design performance and reflective thinking. Thinking Skills and Creativity, 56, Article 101709. https://doi.org/10.1016/j.tsc.2024.101709
Lin, M. P. C., & Chang, D. (2020). Enhancing post-secondary writers’ writing skills with a chatbot. Journal of Educational Technology & Society, 23(1), 78–92. https://www.jstor.org/stable/26915408
McDonald, N., Johri, A., Ali, A., & Hingle, A. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans, 3, Article 100121. https://doi.org/10.1016/j.chbah.2025.100121
Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12527
Nascimento, A. C. A., Pérez-Sanagustín, M., Parmentier, J. F., Hilliger, I., Ferrettini, G., & Mello, R. F. (2026). Exploring the impact of LLM-based scaffolding on academic performance and the mediating roles of AI literacy and prior knowledge. In K. Tammets, S. Sosnovsky, R. Ferreira Mello, G. Pishtari, & T. Nazaretsky (Eds.), Two decades of TEL. From lessons learnt to challenges ahead (pp. 382–396). Springer, Cham. https://doi.org/10.1007/978-3-032-03870-8_26
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1). https://doi.org/10.1177/1609406917733847
Nuñez-Canal, M., Fernández-Ardavín, A., Marcos Días, L., & Aguado Revar, O. (2024). Aprendizaje generativo integral: Un modelo para la educación superior ante los desafíos de la inteligencia artificial [Integrative generative learning: A higher educational model for artificial intelligence challenges]. European Public and Social Innovation Review, 9, 1–21. https://doi.org/10.31637/epsir-2024-1685
Ortega-Arranz, A., Topali, P., & Molenaar, I. (2025). Configuring and monitoring students’ interactions with generative AI tools: Supporting teacher autonomy. In 15th International Learning Analytics and Knowledge Conference (LAK 2025) (pp. 895–902). ACM Press. https://doi.org/10.1145/3706468.3706533
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, Article 422. https://doi.org/10.3389/fpsyg.2017.00422
Pérez-Sanagustín, M., Pérez-Álvarez, R., Maldonado-Mahauad, J., Villalobos, E., & Sanza, C. (2022). Designing a Moodle plugin for promoting learners’ self-regulated learning in blended learning. In I. Hilliger, P. J. Muñoz-Merino, T. De Laet, A. Ortega-Arranz, & T. Farrell (Eds.), Educating for a new future: Making sense of technology-enhanced learning adoption (pp. 324–339). Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_24
Pérez-Sanagustín, M., Parra, D., Verdugo, R., García-Galleguillos, G., & Nussbaum, M. (2016). Using QR codes to increase user engagement in museum-like spaces. Computers in Human Behavior, 60, 73–85. https://doi.org/10.1016/j.chb.2016.02.012
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press.
Postareff, L., Mattsson, M., Lindblom-Ylänne, S., & Hailikari, T. (2017). The complex relationship between emotions, approaches to learning, study success and study progress during the transition to university. Higher Education, 73(3), 441–457. https://doi.org/10.1007/s10734-016-0096-7
Poquet, O., Kitto, K., Jovanovic, J., Dawson, S., Siemens, G., & Markauskaite, L. (2021). Transitions through lifelong learning: Implications for learning analytics. Computers and Education: Artificial Intelligence, 2, Article 100039. https://doi.org/10.1016/j.caeai.2021.100039
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838
Roschelle, J., Rafanan, K., Estrella, G., Nussbaum, M., & Claro, S. (2010). From handheld collaborative tool to effective classroom module: Embedding CSCL in a broader design framework. Computers & Education, 55(3), 1018–1026. https://doi.org/10.1016/j.compedu.2010.04.012
Russell, J. M., Baik, C., Ryan, A. T., & Molloy, E. (2022). Fostering self-regulated learning in higher education: Making self-regulation visible. Active Learning in Higher Education, 23(2), 97–113. https://doi.org/10.1177/1469787420982378
Schmohl, T., Schelling, K., Go, S., Thaler, K. J., & Watanabe, A. (2022). Development, implementation and acceptance of an AI-based tutoring system: A research-led methodology. In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) (pp. 179–186). SCITEPRESS. https://doi.org/10.5220/0011068500003182
Sharef, N. M., Azmi Murad, M. A., Mansor, E. I., Nasharuddin, N. A., Omar, M. K., & Rokhani, F. Z. (2021). Personalized learning based on learning analytics and chatbot. In 2021 1st Conference on Online Teaching for Mobile Education (OT4ME) (pp. 35–41). IEEE. https://doi.org/10.1109/OT4ME53559.2021.9638893
Sjöström, J., & Dahlin, M. (2020). Tutorbot: A chatbot for higher education. In S. Hofmann, O. Müller, & M. Rossi (Eds.), Designing for digital transformation: Co-creating services with citizens and industry (pp. 93–98). Springer International Publishing. https://doi.org/10.1007/978-3-030-64823-7_10
Spencer, D. (2009). Card sorting: Designing usable categories. Rosenfeld Media.
Urban, M., Děchtěrenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K. (2024). ChatGPT improves creative problem-solving performance in university students: An experimental study. Computers & Education, 215, Article 105031. https://doi.org/10.1016/j.compedu.2024.105031
Verbert, K., Ochoa, X., De Croon, R., Dourado, R. A., & De Laet, T. (2020). Learning analytics dashboards: The past, the present and the future. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (LAK’ 20) (pp. 35–40). ACM Press. https://doi.org/10.1145/3375462.3375504
Villagrán, I., Hernández, R., Schuit, G., Neyem, A., Fuentes, J., Larrondo, L., Margozzini, E., Hurtado, M. T., Iriarte, Z., Miranda, C., Varas, J., & Hilliger, I. (2024). Enhancing feedback uptake and self-regulated learning in procedural skills training: Design and evaluation of a learning analytics dashboard. Journal of Learning Analytics, 11(2), 138–156. https://doi.org/10.18608/jla.2024.8195
Villalobos, E., Pérez-Sanagustín, M., & Broisin, J. (2024). From learning actions to dynamics: Characterizing students’ individual temporal behavior with sequence analysis. In A. M. Olney, I. A. Chounta, Z. Liu, O. C. Santos, & I. I. Bittencourt (Eds.), Artificial Intelligence in Education (pp. 3–17). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64302-6_1
Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Lawrence Erlbaum Associates.
Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297–314). Lawrence Erlbaum Associates.
Yuan, B., & Hu, J. (2024). Generative AI as a tool for enhancing reflective learning in students. arXiv. https://arxiv.org/abs/2412.02603
Zavaleta Bernuy, A., Han, Z., Shaikh, H., Zheng, Q. Y., Lim, L. A., Rafferty, A., Petersen, A., & Williams, J. J. (2022, March). How can email interventions increase students’ completion of online homework? A case study using A/B comparisons. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 107–118). ACM Press. https://doi.org/10.1145/3506860.3506874
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press.
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