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Indonesian Journal of Education Research (IJoER)

Open Access Journal

Artificial Intelligence for Science Learning: A Systematic Review of Self-Regulation and Conceptual Understanding

Riska Fitriani
Jambi University ROR
Indonesia
Riskafitrian04.rf@gmail.com
Rayandra Ayshar
Jambi University ROR
Indonesia
Asrial Asrial
Jambi University ROR
Indonesia
Syaiful Syaiful
Jambi University ROR
Indonesia
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  • Purpose of the study: This study systematically reviews and synthesises the empirical literature on the role of Artificial Intelligence (AI) in science education, focusing specifically on how distinct AI features support the phases of students’ self-regulated learning (SRL) and the construction of conceptual understanding in science.

    Methodology: A qualitative systematic literature review reported in line with PRISMA principles was combined with bibliometric mapping. Peer-reviewed articles were retrieved from the Scopus and Sinta databases (2020–2026). Metadata and keyword co-occurrence were analysed using VOSviewer, followed by a thematic synthesis of 25 eligible articles structured around an AI-feature × SRL-phase × conceptual-understanding extraction matrix.

    Main Findings: AI research in science education has grown sharply since 2022. Intelligent tutoring systems, learning analytics, adaptive simulations, and generative AI predominantly scaffold the forethought and performance phases of SRL, enhance conceptual understanding, reduce misconceptions, and raise motivation through personalised interaction. Support for the self-reflection phase remains comparatively weak, evidence is concentrated in higher education, and longitudinal and developing-country studies are scarce.

    Novelty/Originality: The review advances an integrative framework that explicitly links specific AI affordances to individual SRL phases and to conceptual change in science, repositioning AI as a cognitive and metacognitive mediator rather than a content-delivery aid, and derives an evidence-based agenda that foregrounds the reflection phase, K–12 science, and Global South contexts.

  • How to cite

    [1]
    R. Fitriani, R. Ayshar, A. Asrial, and S. Syaiful, “Artificial Intelligence for Science Learning: A Systematic Review of Self-Regulation and Conceptual Understanding”, Ind. Jou. Edu. Rsc, vol. 7, no. 3, pp. 415–427, Jun. 2026, doi: 10.37251/ijoer.v7i3.3482.

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    1. OECD, PISA 2022 Results (Volume I). in PISA. OECD Publishing, 2023, doi: 10.1787/53f23881-en.
    2. E. Panadero, “A review of self-regulated learning: Six models and four directions for research,” Front. Psychol., vol. 8, no. APR, pp. 1–28, 2017, doi: 10.3389/fpsyg.2017.00422.
    3. D. Bransen and M. J. B. Govaerts, “How to conceptualise self‐regulated learning: Implications for measurement,” Med. Educ., vol. 54, no. 8, pp. 684–686, Aug. 2020, doi: 10.1111/medu.14183.
    4. J.-W. Fang, L.-Y. He, G.-J. Hwang, X.-W. Zhu, C.-N. Bian, and Q.-K. Fu, “A concept mapping-based self-regulated learning approach to promoting students’ learning achievement and self-regulation in STEM activities,” Interact. Learn. Environ., vol. 31, no. 10, pp. 7159–7181, Dec. 2023, doi: 10.1080/10494820.2022.2061013.
    5. M. M. Zhu, K. M. Alberts, W. N. Bork, and D. Wong, “Self-regulated learning and intercultural competence: examining the role of self-regulation in supporting preservice teachers’ intercultural learning outcomes,” Intercult. Educ., vol. 34, no. 5, pp. 494–515, Sep. 2023, doi: 10.1080/14675986.2023.2213655.
    6. L. S. Nadelson, B. C. Heddy, S. Jones, G. Taasoobshirazi, and M. Johnson, “Conceptual Change in Science Teaching and Learning: Introducing the Dynamic Model of Conceptual Change,” Int. J. Educ. Psychol., vol. 7, no. 2, pp. 151–166, Jun. 2018, doi: 10.17583/ijep.2018.3349.
    7. O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education – where are the educators?,” Int. J. Educ. Technol. High. Educ., vol. 16, no. 1, pp. 1–18, Dec. 2019, doi: 10.1186/s41239-019-0171-0.
    8. L. Chen, P. Chen, and Z. Lin, “Artificial Intelligence in Education: A Review,” IEEE Access, vol. 8, pp. 75264–75278, 2020, doi: 10.1109/ACCESS.2020.2988510.
    9. X. Chen, H. Xie, D. Zou, and G.-J. Hwang, “Application and theory gaps during the rise of artificial intelligence in education,” Comput. Educ. Artif. Intell., vol. 1, p. 100002, 2020, doi: 10.1016/j.caeai.2020.100002.
    10. G.-J. Hwang, H. Xie, B. W. Wah, and D. Gašević, “Vision, challenges, roles and research issues of Artificial Intelligence in Education,” Comput. Educ. Artif. Intell., vol. 1, p. 100001, 2020, doi: 10.1016/j.caeai.2020.100001.
    11. F. Ouyang and P. Jiao, “Artificial intelligence in education: The three paradigms,” Comput. Educ. Artif. Intell., vol. 2, p. 100020, 2021, doi: 10.1016/j.caeai.2021.100020.
    12. G. Cooper, “Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence,” J. Sci. Educ. Technol., vol. 32, no. 3, pp. 444–452, Jun. 2023, doi: 10.1007/s10956-023-10039-y.
    13. A. Yusuf, N. Pervin, M. Román‐González, and N. M. Noor, “Generative in education and research: A systematic mapping review,” Rev. Educ., vol. 12, no. 2, Aug. 2024, doi: 10.1002/rev3.3489.
    14. X. Ning, Y. Zhuo, X. Wang, C.-I. D. Sio, and L.-H. Lee, “When Generative Artificial Intelligence Meets Extended Reality: A Systematic Review,” Int. J. Human–Computer Interact., vol. 42, no. 10, pp. 6870–6890, May 2026, doi: 10.1080/10447318.2025.2565392.
    15. B. Liu, W. Liu, W. Zeng, and Y. Peng, “Exploring the application potential of generative artificial intelligence in high school geography teaching: Scenarios, limitations, and improvement strategies,” J. Educ. Res., vol. 118, no. 6, pp. 674–687, Nov. 2025, doi: 10.1080/00220671.2025.2510396.
    16. M. Dam, F. J. J. M. Janssen, and J. H. van Driel, “Making sense of student data in teacher professional development,” Prof. Dev. Educ., vol. 46, no. 2, pp. 256–273, Mar. 2020, doi: 10.1080/19415257.2018.1550104.
    17. E. D. Loepp, “Beyond Polls: Using Science and Student Data to Stimulate Learning,” J. Polit. Sci. Educ., vol. 14, no. 1, pp. 17–41, Jan. 2018, doi: 10.1080/15512169.2017.1359094.
    18. K.-Y. Tang, C.-Y. Chang, and G.-J. Hwang, “Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019),” Interact. Learn. Environ., vol. 31, no. 4, pp. 2134–2152, May 2023, doi: 10.1080/10494820.2021.1875001.
    19. A. Hadyaoui and L. Cheniti-Belcadhi, “AI-driven adaptive stealth assessment for socially regulated learning in collaborative environments,” J. Res. Technol. Educ., vol. 58, no. 1, pp. 130–150, Jan. 2026, doi: 10.1080/15391523.2025.2586501.
    20. X. Wang et al., “The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis,” J. Educ. Comput. Res., vol. 62, no. 6, pp. 1348–1383, Oct. 2024, doi: 10.1177/07356331241240459.
    21. J. A. Kulik and J. D. Fletcher, “Effectiveness of Intelligent Tutoring Systems,” Rev. Educ. Res., vol. 86, no. 1, pp. 42–78, Mar. 2016, doi: 10.3102/0034654315581420.
    22. L. Zheng, L. Zhong, and J. Niu, “Effects of personalised feedback approach on knowledge building, emotions, co-regulated behavioural patterns and cognitive load in online collaborative learning,” Assess. Eval. High. Educ., vol. 47, no. 1, pp. 109–125, Jan. 2022, doi: 10.1080/02602938.2021.1883549.
    23. A. Mavroudi, M. Giannakos, and J. Krogstie, “Supporting adaptive learning pathways through the use of learning analytics: developments, challenges and future opportunities,” Interact. Learn. Environ., vol. 26, no. 2, pp. 206–220, Feb. 2018, doi: 10.1080/10494820.2017.1292531.
    24. M. Lan and X. Zhou, “A qualitative systematic review on AI empowered self-regulated learning in higher education,” npj Sci. Learn., vol. 10, no. 1, pp. 21–35, May 2025, doi: 10.1038/s41539-025-00319-0.
    25. D. Ifenthaler and J. Y.-K. Yau, “Utilising learning analytics to support study success in higher education: a systematic review,” Educ. Technol. Res. Dev., vol. 68, no. 4, pp. 1961–1990, Aug. 2020, doi: 10.1007/s11423-020-09788-z.
    26. J. C. K. Lee, Z. H. Wan, S. K. F. Hui, and P. Y. Ko, “More student trust, more self-regulation strategy? Exploring the effects of self-regulatory climate on self-regulated learning,” J. Educ. Res., vol. 112, no. 4, pp. 463–472, Jul. 2019, doi: 10.1080/00220671.2018.1553840.
    27. M. Zeidner and H. Stoeger, “Self-Regulated Learning (SRL): A guide for the perplexed,” High Abil. Stud., vol. 30, no. 1–2, pp. 9–51, Jul. 2019, doi: 10.1080/13598139.2019.1589369.
    28. O. Chernikova, N. Heitzmann, M. Stadler, D. Holzberger, T. Seidel, and F. Fischer, “Simulation-Based Learning in Higher Education: A Meta-Analysis,” Rev. Educ. Res., vol. 90, no. 4, pp. 499–541, Aug. 2020, doi: 10.3102/0034654320933544.
    29. A. Jenney, K. Scott, O. Cullen, M. Bittman, and C. Smith, “Exploring the Effectiveness of Virtual Simulation-Based Learning in Enhancing Clinical Skills in Social Work Education,” Stud. Clin. Soc. Work Transform. Pract. Educ. Res., vol. 95, no. 3–4, pp. 498–519, Oct. 2025, doi: 10.1080/28376811.2025.2524621.
    30. H. Zhu et al., “The status of virtual simulation experiments in medical education in China: Based on the national virtual simulation experiment teaching Center (iLAB-X),” Med. Educ. Online, vol. 28, no. 1, pp. 1–19, Dec. 2023, doi: 10.1080/10872981.2023.2272387.
    31. B. Sichterman, O. Noroozi, J. Boetje, S. van Ginkel, H. Khosravi, and J. Versendaal, “Supporting peer learning with artificial intelligence: A systematic literature review,” Innov. Educ. Teach. Int., vol. 62, no. 5, pp. 1648–1664, Sep. 2025, doi: 10.1080/14703297.2025.2530118.
    32. J. Riley, E. S. Harvey, J. S. Hain, P. Green, and R. VanMeter, “Integrating Artificial Intelligence in Marketing Education: Preparing Students for an AI-Driven Future,” Mark. Educ. Rev., pp. 1–12, May 2026, doi: 10.1080/10528008.2026.2676121.
    33. S. Saini, F. Rabby, R. Bansal, A. L. Aziz, and A. Propheto, “Mapping the intersection of artificial intelligence and neuroticism: A bibliometric analysis,” Crit. Public Health, vol. 36, no. 1, pp. 1–15, Dec. 2026, doi: 10.1080/09581596.2025.2598715.
    34. H. Crompton and D. Burke, “Artificial intelligence in higher education: the state of the field,” Int. J. Educ. Technol. High. Educ., vol. 20, no. 1, pp. 22–34, Apr. 2023, doi: 10.1186/s41239-023-00392-8.
    35. H. Crompton, M. V. Jones, and D. Burke, “Affordances and challenges of artificial intelligence in K-12 education: a systematic review,” J. Res. Technol. Educ., vol. 56, no. 3, pp. 248–268, May 2024, doi: 10.1080/15391523.2022.2121344.
    36. B. J. Zimmerman, “Chapter 2 - Attaining Self-Regulation: A Social Cognitive Perspective,” in Handbook of Self-Regulation, 2000, doi: 10.1016/B978-012109890-2/50031-7.
    37. G. Makransky and G. B. Petersen, “The Cognitive Affective Model of Immersive Learning (CAMIL): a Theoretical Research-Based Model of Learning in Immersive Virtual Reality,” Educ. Psychol. Rev., vol. 33, no. 3, pp. 937–958, Sep. 2021, doi: 10.1007/s10648-020-09586-2.
    38. C. Konnemann, R. Asshoff, and M. Hammann, “Insights Into the Diversity of Attitudes Concerning Evolution and Creation: A Multidimensional Approach,” Sci. Educ., vol. 100, no. 4, pp. 673–705, Jul. 2016, doi: 10.1002/sce.21226.
    39. A. Iqbal and G. K. Singh, “Teaching synchronization of synchronous generator using virtual laboratory,” IETE J. Educ., vol. 64, no. 1, pp. 5–14, Jan. 2023, doi: 10.1080/09747338.2022.2051615.
    40. H. J. Banda and J. Nzabahimana, “Effect of integrating physics education technology simulations on students’ conceptual understanding in physics: A review of literature,” Phys. Rev. Phys. Educ. Res., vol. 17, no. 2, Dec. 2021, doi: 10.1103/PhysRevPhysEducRes.17.023108.