Attempts to Harness AI Model in Online Differentiated Learning on Students' Understanding Levels

Authors

  • Citra Kurniawan Departement of Educational Technology, Faculty of Education, Universitas Negeri Malang, Malang, Indonesia https://orcid.org/0000-0001-7927-3500
  • Ence Surahman Departement of Educational Technology, Faculty of Education, Universitas Negeri Malang, Malang, Indonesia
  • Deka Dyah Utami Departement of Educational Technology, Faculty of Education, Universitas Negeri Malang, Malang, Indonesia
  • Rumaizah Mohd Nordin Faculty of Built Environment, Universiti Teknologi MARA, Shah Alam, Selangor Darul Ehsan, Malaysia

DOI:

https://doi.org/10.21009/jtp.v27i2.54400

Keywords:

Online Differentiated Learning, Self-Regulated Learning, Artificial Intelligence Model, Level of Understanding, Path Analysis

Abstract

This study aims to examine the relationship between Online Differentiated Learning (ODL), Self-Regulated Learning (SRL), and an Artificial Intelligence Model (AIM) and the Level of Understanding (LU). This study employs an quasi experimental research method, utilizing path analysis as the primary data analysis technique. This study involved 125 respondents who volunteered to participate. The selection of respondents was based on their experience using generative AI applications such as ChatGPT. ChatGPT was used in the study because it is a popular generative AI application. This study found that Online Differentiated Learning (ODL) has an influence on the level of understanding of students. Furthermore, Self-Regulated Learning (SRL) does not affect learning outcomes. Different results are obtained if Artificial Intelligence Model (AIM) is involved to achieve learning outcomes in the form of students' level of understanding. The findings in the study are interesting because the role of AIM influences if integrated in the application of ODL and SRL to achieve learning outcomes. This research is expected to have an impact on the study of artificial intelligence, especially in its implementation in learning. Further research is expected to involve a larger number of respondents with diverse characteristics.

References

Abbasi, B. N., Wu, Y., & Luo, Z. (2025). Exploring the impact of artificial intelligence on curriculum development in global higher education institutions. Education and Information Technologies, 30(1), 547–581. Scopus. https://doi.org/10.1007/s10639-024-13113-z

Adamu, S., & Awwalu, J. (2019). The Role of Artificial Intelligence (AI) in Adaptive eLearning System (AES) Content Formation: Risks and Opportunities involved. arXiv Preprint arXiv:1903.00934. https://arxiv.org/abs/1903.00934

Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 1–10. https://doi.org/10.1007/s43681-021-00096-7

Allen, M., Webb, A. W., & Matthews, C. E. (2016). Adaptive teaching in STEM: Characteristics for effectiveness. Theory Into Practice. https://doi.org/10.1080/00405841.2016.1173994

Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Thabit, S., El-Qirem, F. A., Lutfi, A., Alrawad, M., Al Mulhem, A., Alkhdour, T., Awad, A. B., & Al-Maroof, R. S. (2022). Examining the Impact of Artificial Intelligence and Social and Computer Anxiety in E-Learning Settings: Students’ Perceptions at the University Level. Electronics, 11(22), 3662. https://doi.org/10.3390/electronics11223662

Ardiawan, I. K. N., Lasmawan, I. W., Dantes, N., & Dantes, G. R. (2024). The impact of differentiated learning materials on students’ understanding of nationalism and global diversity. Journal of Education and E-Learning Research, 11(1), 107–112. Scopus. https://doi.org/10.20448/jeelr.v11i1.5369

Beketov, V., Lebedeva, M., & Taranova, M. (2024a). The use of artificial intelligence in teaching medical students to increase motivation and reduce anxiety during academic practice. Current Psychology, 43(16), 14367–14377. https://doi.org/10.1007/s12144-023-05471-7

Beketov, V., Lebedeva, M., & Taranova, M. (2024b). The use of artificial intelligence in teaching medical students to increase motivation and reduce anxiety during academic practice. Current Psychology, 43(16), 14367–14377. https://doi.org/10.1007/s12144-023-05471-7

Borland, M. A. (2012). The relationships between personality characteristics and student achievement: What contributes to student satisfaction in online learning environments? search.proquest.com. https://search.proquest.com/openview/96bb7c29b9ddc3350df3ffa0d2faded0/1?pq-origsite=gscholar%5C&cbl=18750

Calamlam, J. M. (2022). Perception on research methods course’s online environment and self-regulated learning during the COVID-19 pandemic. E-Learning and Digital Media, 19(1), 93–119. https://doi.org/10.1177/20427530211027722

Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014

Chaudhry, M. A., & Kazim, E. (2022). Artificial Intelligence in Education (AIEd): A high-level academic and industry note 2021. AI and Ethics, 1–9. https://doi.org/10.1007/s43681-021-00074-z

Cheng, Y. P., Cheng, S. C., & Huang, Y. M. (2022). An Internet Articles Retrieval Agent Combined With Dynamic Associative Concept Maps to Implement Online Learning in an Artificial Intelligence Course. … in Open and Distributed Learning. http://www.irrodl.org/index.php/irrodl/article/view/5437

Delgado, H. O. K., de Azevedo Fay, A., Sebastiany, M. J., & Silva, A. D. C. (2020). Artificial intelligence adaptive learning tools: The teaching of English in focus. Brazilian English Language Teaching Journal, 11(2). Scopus. https://doi.org/10.15448/2178-3640.2020.2.38749

Erdogan, T., & Senemoglu, N. (2016). Development and validation of a scale on self-regulation in learning (SSRL). SpringerPlus, 5(1), 1686. https://doi.org/10.1186/s40064-016-3367-y

Foo, S. Y. (2024). Investigating gifted students’ higher-order thinking skills in a differentiated learning environment: A case study. Gifted Education International. Scopus. https://doi.org/10.1177/02614294241305766

Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-Regulated Learning strategies using e-learning tools for Computer Science. Computers & Education, 123, 150–163.

Haniya, S., & Roberts-Lieb, S. (2017). Differentiated Learning: Diversity Dimensions of e-Learning. In E-Learning Ecologies: Principles for New Learning and Assess. (pp. 183–206). Taylor and Francis; Scopus. https://doi.org/10.4324/9781315639215-8

Harati, H., Yen, C. J., Tu, C. H., Cruickshank, B. J., & ... (2020). Online Adaptive Learning: A Study of Score Validity of the Adaptive Self-Regulated Learning Model. … Web-Based Learning …. https://www.igi-global.com/article/online-adaptive-learning/261583

Jepkoech, F. (2023). Differentiated learning in a typical classroom. In Closing the Educ. Achiev. Gap for Stud. With Learn. Disabil. (pp. 228–245). IGI Global; Scopus. https://doi.org/10.4018/978-1-6684-8737-2.ch011

Jin, S.-H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20(1), 37. https://doi.org/10.1186/s41239-023-00406-5

Kurniawan, C., Surahman, E., Utami, D. D., Nordin, R. M., Hasanah, W., & Aniisah, A. (2024). Data Visualization of Online Differentiated Learning Implementation on Students’ Time Spend in Learning. 74–78. https://doi.org/10.1109/ICET64717.2024.10778467

Mills, M., Monk, S., Keddie, A., Renshaw, P., Christie, P., Geelan, D., & Gowlett, C. (2014). Differentiated learning: From policy to classroom. Oxford Review of Education, 40(3), 331–348. Scopus. https://doi.org/10.1080/03054985.2014.911725

Molenaar, I., Mooij, S. D., Azevedo, R., Bannert, M., Järvelä, S., & Gašević, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139, 107540. https://doi.org/10.1016/j.chb.2022.107540

Perkins, T., & Tolbert, J. (2021). Yours, Mine, Ours: Collaboration and Differentiated Learning in the Creative Writing Classroom. In Imaginative Teach. Through Creative Writing: A Guide for Secondary Classrooms (pp. 195–204). Bloomsbury Publishing Plc.; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190225093&partnerID=40&md5=3bec5289cfd0afc02291da0f25d0967f

Radi, E. T., & Kadem, R. A. (2019). The impact of the differentiated learning strategy on the way of learning stations in learning the skills of putting down and handling football. Indian Journal of Public Health Research and Development, 10(10), 2169–2174. Scopus. https://doi.org/10.5958/0976-5506.2019.03174.7

Rear, D. (2019). One size fits all? The limitations of standardised assessment in critical thinking. Assessment & Evaluation in Higher Education. https://srhe.tandfonline.com/doi/abs/10.1080/02602938.2018.1526255

Rijal, A., & Waluyo, B. (2025). Effectiveness of differentiated learning in mathematics: Insights from elementary school students. Journal of Education and Learning, 19(1), 241–248. Scopus. https://doi.org/10.11591/edulearn.v19i1.21806

Rintayati, P., Syawaludin, A., & Sunarno, W. (2024). Digital creativity-based professional learning communities’ model to encourage differentiated learning design skills in elementary school teacher. Edelweiss Applied Science and Technology, 8(5), 1083–1089. Scopus. https://doi.org/10.55214/25768484.v8i5.1808

Roy, A., Guay, F., & Valois, P. (2013). Teaching to address diverse learning needs: Development and validation of a Differentiated Instruction Scale. International Journal of Inclusive Education, 17(11), 1186–1204. https://doi.org/10.1080/13603116.2012.743604

Schunk, D. H., & Zimmerman, B. J. (2012). Motivation and self-regulated learning: Theory, research, and applications. Routledge.

Yang, S., Tian, H., Sun, L., & Yu, X. (2019). From One-size-fits-all Teaching to Adaptive Learning: The Crisis and Solution of Education in The Era of AI. Journal of Physics: Conference …. https://iopscience.iop.org/article/10.1088/1742-6596/1237/4/042039/meta

Yang, X., Dong, J., & Tan, D. (2022). Student conceptual level scale: Development and initial validation. Frontiers in Education, 7, 965643. https://doi.org/10.3389/feduc.2022.965643

Zhang, X., & Chen, L. (2021). College English Smart Classroom Teaching Model Based on Artificial Intelligence Technology in Mobile Information Systems. Mobile Information Systems, 2021, 1–12. https://doi.org/10.1155/2021/5644604

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Published

2025-08-31

How to Cite

Citra Kurniawan, Ence Surahman, Deka Dyah Utami, & Rumaizah Mohd Nordin. (2025). Attempts to Harness AI Model in Online Differentiated Learning on Students’ Understanding Levels. JTP - Jurnal Teknologi Pendidikan, 27(2), 587–597. https://doi.org/10.21009/jtp.v27i2.54400

Issue

Vol. 27 No. 2 (2025): Jurnal Teknologi Pendidikan

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Copyright (c) 2025 Citra Kurniawan, Ence Surahman, Deka Dyah Utami, Rumaizah Mohd Nordin

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