The Integration of Generative Artificial Intelligence into the REACT Strategy to Enhance Self-Regulated Learning among Elementary School Teacher Candidates

Authors

  • Risma Nuriyanti Institut Pendidikan Indonesia
  • Neni Nadiroti Muslihah Institut Pendidikan Indonesia
  • Amadhila Ellina Penehafo Namibian College of Open Learning (NAMCOL), Namibia
  • Abih Gumelar Universitas Pendidikan Indonesia

Keywords:

Elementary Teacher Candidates, Generative Artificial Intilegence, REACT, Self-Regulated Learning

Abstract

The low level of self-regulated learning among university students requires serious attention. This study aimed to determine the effect of the REACT strategy integrated with generative artificial intelligence (GenAI) on the self-regulated learning of prospective elementary school teachers. An experimental method was employed, specifically a true experimental post-test-only control group design. Random sampling was used to select a total of 45 participants, divided into two groups: an experimental group of 22 students and a control group of 23 students. Data were collected using a questionnaire based on six indicators. The results of the t-test showed a significance value of 0.042 (less than 0.05), indicating a statistically significant difference between the groups; the experimental group outperformed the control group, with a mean score of 75.9 compared to 73.0 (a three-point difference) and a t-value of 2.091. Thus, it can be concluded that there is a significant difference between the experimental and control groups. However, not all indicators of self-regulated learning showed improvement; the highest score was observed in the "learning planning" indicator, while the lowest was in the "metacognitive regulation" indicator. Consequently, while the REACT strategy integrated with GenAI offers a potential solution for enhancing students' self-regulated learning, the findings suggest a need for strengthening metacognitive skills, providing balanced scaffolding, and preventing student over-reliance on AI.

References

Astuti, Y., Kania, A., & Januar, P. (2020). A Comparative Study of Guided Discovery Learning and REACT Strategy Toward Problem-Solving Skill and Self- Regulated Learning on Fifth Grade Students. 397(Icliqe 2019), 882–890. DOI: https://10.2991/assehr.k.200129.109

Asyari, L., Nuriyanti, R., Gunawan, D., & Adiredja, R. K. (2021). The Influence of Experiential Learning Model on Primary School Student’s Creative Thinking Skills. Social, Humanities, and Educational Studies (SHEs): Conference Series, 4(1), 70–76. DOI: https://doi.org/10.20961/shes.v4i1.48568

Bakhtiar, A., & Hadwin, A. F. (2022). Motivation from a self-regulated learning perspective: Application to school psychology. Canadian Journal of School Psychology, 37(1), 93–116. DOI https://doi.org/10.1177/08295735211054699

Chandy, M., & Kumar, S. P. (2026). Mapping the Landscape of Self-regulated Learning with Interventions–A Bibliometric Guide for Academic Decision-Making. Journal of Engineering Education Transformations, 357–369. DOI: https://doi.org/10.16920/jeet/2026/v39is2/26044

Chen, jie Tian, Xionan Fu, Yu Liu, P. (2026). How does GenAI support self-regulated learning? Evidence from learners’ questioning behavior analysis TI ES IN ES. (November 2024). DOI: https://doi.org/10.1057/s41599-026-08610-0

Dignath, C., Karlen, Y., Schuster, C., & Stebner, F. (2026). From theory to action: Supporting self-regulated learning in the classroom. Theory Into Practice, 1–17. DOI : https://doi.org/10.1080/00405841.2026.2714717

Farida, R., Suroyo, S., & Sekarwinahyu, M. (2023). The influence of strategy relating, experiencing, applying, cooperating, transferring (react) on science learning outcomes of fifth grade elementary students viewed from critical thinking ability. JPPIPA (Jurnal Penelitian Pendidikan IPA), 8(1), 1–10. DOI: https://doi.org/10.26740/jppipa.v8n1.p1-10

Franza, G., Jackson, M., Horneck, B., Revels III, B., & Hopkins, K. (2026). THINK TANK: WE’RE PREPARING FOR A WORLD THAT DOESN’T EXIST YET: STUDENT PERSPECTIVES ON FUTURE-DRIVEN EDUCATION. Journal for Leadership & Instruction, 25(1), 79.

Hall, Margeret and Hawkins, Daniel. From Tool Use to Evaluative Judgment: A Readiness Framework for Generative AI in Higher Education (April 01, 2026). Available at SSRN: https://ssrn.com/abstract=6598018 or http://dx.doi.org/10.2139/ssrn.6598018

Husnayain, M. F., Ansori, M. R., Saputra, R. S. H., & Paryadi, P. (2026). Implementation of Reflective Learning Logs in Supporting Students’ Learning Development. Journal of Education Review Provision, 6(2), 70–82. DOI: https://doi.org/10.55885/jerp.v6i2.1091

Inayati, N. L., Al Mubarok, F. U., & Rohmani, A. F. (2026). Enhancing Critical Thinking and Self Directed Learning through Integration of Digital Competence and Didactic Strategy in Higher Education. Journal of Interdisciplinary Studies in Education, 15(2), 85–108. DOI: https://doi.org/10.32674/whffna77

Jannah, R. (2022). Implementasi Model Pembelajaran Kontekstual dalam Pembelajaran Menulis Teks Deskripsi. Prosiding Seminar Nasional Daring: Pendidikan Bahasa Dan Sastra Indonesia, 2(1), 770–774. DOI: https://doi.org/10.31332/aladl.v2i1.1420

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., & Hüllermeier, E. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. DOI: https://doi.org/10.1016/j.lindif.2023.102274

Kitsantas, A., Bembenutty, H., Cleary, T. J., Alexander, P. A., DiBenedetto, M. K., Kaplan, A., Kolovelonis, A., Mandell, B. E., Martin, A. J., & McCrudden, M. T. (2025). Barry J. Zimmerman’s Enduring Legacy: The Inspiring Fusion of Self-Regulated Learning Theory, Practice, and Mentorship. Educational Psychology Review, 37(3), 78. DOI: https://doi.org/10.1007/s10648-025-10052-0

Kovačević, S., & Barbir, J. (2025). Frequency of Implementation of Contextual Teaching and Learning in Subject Teaching. Croatian Journal of Education/Hrvatski Časopis Za Odgoj i Obrazovanje, 27(2). DOI: https://doi.org/10.15516/cje.v27i2.6343

Li, C., Cui, H., & Hagedorn, L. S. (2026). The cognitive impact of ChatGPT in higher education: A systematic review of critical and creative thinking outcomes. Computers and Education: Artificial Intelligence, 10, 100571. DOI: https://doi.org/10.1016/j.caeai.2026.100571

López-Pernas, S., Misiejuk, K., Oliveira, E., & Saqr, M. (2025). The dynamics of the self-regulation process in student-AI interactions: The case of problem-solving in programming education. Proceedings of the 25th Koli Calling International Conference on Computing Education Research, 1–12. DOI: https://doi.org/10.1145/3769994.3770043

Manurung, A. S., Fahrurrozi, E. U., & Gumelar, G. (2023). Implementasi berpikir kritis dalam upaya mengembangkan kemampuan berpikir kreatif mahasiswa. Jurnal Papeda, 5(2).

Meece, J. L. (2023). The role of motivation in self-regulated learning. In Self-regulation of learning and performance (pp. 25–44). Routledge.

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. DOI: https://doi.org/10.1111/j.1467-9620.2006.00684.x

Pratama, R. Y., Syaifurrohman, A., & Sari, D. P. (2025). Pengembangan Model SMART dalam PjBL untuk Meningkatkan Kemandirian Belajar Mahasiswa pada Mata Kuliah Akhlakul Karimah. Ideguru: Jurnal Karya Ilmiah Guru, 10(3), 2589–2595. DOI: https://doi.org/10.51169/ideguru.v10i3.2120

Robinson, J. (2024). Likert scale. In Encyclopedia of quality of life and well-being research (pp. 3917–3918). Springer. DOI: https://doi.org/10.1007/978-3-031-17299-1_1654

Rosenthal, A., Hirt, C. N., Eberli, T. D., Jud, J., & Karlen, Y. (2026). Exploring subject-specific variations in teachers’ promotion of self-regulated learning: insights from classroom observations, teachers’ self-reports, and students’ perceptions. Metacognition and Learning, 21(1), 39. DOI: https://doi.org/10.1007/s11409-026-09488-8

Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 342–363. DOI: https://search.informit.org/doi/10.3316/informit.T2025102700003092061569891

Sarker, M., & AL-Muaalemi, M. A. (2022). Sampling techniques for quantitative research. In Principles of social research methodology (pp. 221–234). Springer. DOI: https://doi.org/10.1007/978-981-19-5441-2_15

South, L., Saffo, D., Vitek, O., Dunne, C., & Borkin, M. A. (2022). Effective use of Likert scales in visualization evaluations: A systematic review. Computer Graphics Forum, 41(3), 43–55. DOI: https://doi.org/10.1111/cgf.14521

Sugiyono, D. (2013). Metode penelitian pendidikan pendekatan kuantitatif, kualitatif dan R&D.

Tarumasely, Y. (2024). Meningkatkan Kemampuan Belajar Mandiri (Panduan untuk Mengembangkan Self-Regulated Learning). Academia Publication.

Wayan, N., Puspa, A., Made, N., & Suryanti, N. (2023). Penerapan Strategi Pembelajaran REACT Untuk Meningkatkan Kemampuan Berpikir Kritis Siswa dalam Pembelajaran Sosiologi Pada Siswa Kelas XI IPS 1 di SMA Negeri 1 Lingsar. 8(September 2021), 2021–2024. DOI: 10.29303/jipp.v8i1b.1317

Wigfield, A. (2023). The role of children’s achievement values in the self-regulation of their learning outcomes. In Self-regulation of learning and performance (pp. 101–124). Routledge.

Xia, Q., Yang, Y., Wang, W., & Yin, H. (2026). Promoting Interdisciplinary Learning With Generative AI Through Self‐Regulated Scaffolding. Journal of Computer Assisted Learning, 42(2), e70214. DOI: https://doi.org/10.1002/jcal.70214

Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025). Enhancing self‐regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. British Journal of Educational Technology, 56(5), 1842–1863. DOI: https://doi.org/10.1111/bjet.13599

Zhang, C., Hu, X., & Wei, W. (2025). Concept Maps as Metacognitive Scaffolds: Transforming Student-GenAI Interactions in Academic Counseling. 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 1–8. DOI: https://doi.org/10.1109/TALE66047.2025.11346728

Zilberman, N. N. (2026). Generative AI in Higher Education: Conditions for Supporting Students’ Metacognitive Regulation. Open Education, 30(1), 15–22. DOI: https://doi.org/10.21686/1818-4243-2026-1-15-22

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. DOI: https://doi.org/10.1207/s15430421tip4102_2

Zimmerman, B. J. (2023). Dimensions of academic self-regulation: A conceptual framework for education. In Self-regulation of learning and performance (pp. 3–21). Routledge.

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Published

2026-09-30