The Effect of AI-Driven Personalized Learning with a Recommendation System on Students' Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning

Hariyanto S. Auna; Dedi Kuswandi; Henry Praherdhiono; Made Duananda Kartika Degeng; Saida Ulfa

doi:10.17275/per.26.83.13.6

The Effect of AI-Driven Personalized Learning with a Recommendation System on Students' Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning

Abstract

This study investigates the effect of AI-driven personalized learning supported by a recommendation system on students' conceptual understanding (C2) and analytical skills (C4), with self-regulated learning (SRL) as a moderating variable. Although AI-based personalized learning has attracted growing attention in education, empirical evidence regarding its effectiveness in controlled learning environments that consider differences in students' SRL levels remains limited. This study addresses this gap by implementing a structured AI-supported personalized learning approach through a recommendation system. A pretest–posttest control group experimental design was conducted with 60 senior high school students classified into high- and low-SRL groups using z-scores. The experimental group participated in AI-supported personalized learning through a learning management system (LMS) integrated with a recommendation system, whereas the control group received personalized learning without AI support. Data were analyzed using a 2 × 2 factorial MANOVA after meeting the assumptions of normality, homogeneity, and initial equivalence. The results showed that AI-driven personalized learning significantly improves students' C2 and C4. Although SRL does not have a significant direct effect on either outcome, it demonstrated a significant interaction effect on C4. These findings suggest that AI-based recommendation systems serve as an effective scaffolding mechanism for learners with different SRL levels. This study provides empirical evidence that integrating AI into controlled personalized learning environments enhances students' C2 and C4 in mathematics learning.

Keywords

AI-based learning, personalized learning, recommendation system, self-regulated learning, mathematical understanding, analytical skills, E-Learning, Artificial intelligence

References

Abu-Rasheed, H., Dornhöfer, M., Weber, C., Kismihók, G., Buchmann, U., & Fathi, M. (2023). Building Contextual Knowledge Graphs for Personalized Learning Recommendations Using Text Mining and Semantic Graph Completion. 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), 36–40. IEEE. https://doi.org/10.1109/ICALT58122.2023.00016
Adeleke, J. O., Balogun, H. A., & Ayanwale, M. A. (2025). Assessment of content and cognitive dimensions of learners’ mathematics performance. STEM Education, 5(3), 383–400. https://doi.org/10.3934/steme.2025019
Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives: complete edition. Addison Wesley Longman, Inc. Retrieved from https://drive.google.com/open?id=14sbVa8j4UCxgD94AY7QXM8jcnwJDJlhK&usp=drive_fs
Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347–364. https://doi.org/10.1007/BF00138871
Boussakssou, M., Hssina, B., & Erittali, M. (2020). Towards an Adaptive E-learning System Based on Q-Learning Algorithm. Procedia Computer Science, 170, 1198–1203. https://doi.org/10.1016/J.PROCS.2020.03.028
Cai, J., Ding, M., & Hwang, S. (2026). Fostering students’ mathematical understanding: Toward a theoretical framework. Asian Journal for Mathematics Education, 5(2), 117–137. https://doi.org/10.1177/27527263261442721
Carceles-Poveda, E., & Giannitsarou, C. (2007). Adaptive learning in practice. Journal of Economic Dynamics and Control, 31(8), 2659–2697. https://doi.org/10.1016/j.jedc.2006.09.004
Chen, C. M. (2009). Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance. Expert Systems with Applications, 36(5), 8816–8829. https://doi.org/10.1016/j.eswa.2008.11.026
Chen, Y., Li, X., Liu, J., & Ying, Z. (2018). Recommendation System for Adaptive Learning. Applied Psychological Measurement, 42(1), 24–41. https://doi.org/10.1177/0146621617697959
da Silva, F. L., Slodkowski, B. K., da Silva, K. K. A., & Cazella, S. C. (2023). A systematic literature review on educational recommender systems for teaching and learning: research trends, limitations and opportunities. Education and Information Technologies, 28(3), 3289–3328. https://doi.org/10.1007/s10639-022-11341-9

Eti, N., Mosia, M., & Egara, F. O. (2026). The role of AI-driven personalised learning in enhancing mathematics problem-solving skills: a systematic review. Frontiers in Computer Science, 8. https://doi.org/10.3389/fcomp.2026.1813431
Farkhunda Rasheed Choudhary, Azhar Mahmood, & Sidra Khushnood. (2020). Measuring Self-Regulation of Distance Learners: A Comparative Study of Science and Arts Students. International Journal of Distance Education and E-Learning, 5(2), 138–157. https://doi.org/10.36261/ijdeel.v5i2.1048
Feigerlova, E., Hani, H., & Hothersall-Davies, E. (2025). A systematic review of the impact of artificial intelligence on educational outcomes in health professions education. BMC Medical Education, 25(1), 129. https://doi.org/10.1186/s12909-025-06719-5
Firda, N. (2024). Artificial Intelligence’s Transformative Role in Mathematics Education: A Systematic Literature Review. Aip Conference Proceedings, 3220(1). https://doi.org/10.1063/5.0235478
Fitria, A., & Susanto, H. (2023). Cognitive Map: Diagnosing and Exploring Students’ Misconceptions in Algebra. Mathematics Teaching Research Journal, 15(5), 49–75.
Fortuna, A., Prasetya, F., Samala, A. D., Rawas, S., Criollo-C, S., Kaya, D., … Nabawi, R. A. (2025). Artificial intelligence in personalized learning: A global systematic review of current advancements and shaping future opportunities. Social Sciences & Humanities Open, 12, 102114. https://doi.org/10.1016/j.ssaho.2025.102114
Fuchs, K., Pösse, L., Bedenlier, S., Gläser-Zikuda, M., Kammerl, R., Kopp, B., … Händel, M. (2022). Preservice Teachers’ Online Self-Regulated Learning: Does Digital Readiness Matter? Education Sciences, 12(4), 272. https://doi.org/10.3390/educsci12040272
Funa, A. A., & Gabay, R. A. E. (2025). Policy guidelines and recommendations on AI use in teaching and learning: A meta-synthesis study. Social Sciences & Humanities Open, 11, 101221. https://doi.org/10.1016/J.SSAHO.2024.101221
Gebremeskel, A. A., Ayele, M. A., & Wondimuneh, T. E. (2025). Student engagement, conceptual-understanding, and problem-solving ability in learning plane geometry through an integrated instructional approach. Eurasia Journal of Mathematics, Science and Technology Education, 21(5), em2634. https://doi.org/10.29333/ejmste/16391
Huincahue, J., Borromeo-Ferri, R., Reyes-Santander, P., & Garrido-Véliz, V. (2021). Mathematical Thinking Styles—The Advantage of Analytic Thinkers When Learning Mathematics. Education Sciences, 11(6), 289. https://doi.org/10.3390/educsci11060289
Ingkavara, T., Panjaburee, P., Srisawasdi, N., & Sajjapanroj, S. (2022). The use of a personalized learning approach to implementing self-regulated online learning. Computers and Education: Artificial Intelligence, 3, 100086. https://doi.org/10.1016/J.CAEAI.2022.100086
Järvelä, S., Nguyen, A., & Molenaar, I. (2023). Advancing SRL research with artificial intelligence. Computers in Human Behavior, 147, 107847. https://doi.org/10.1016/j.chb.2023.107847
Kabassi, K. (2010). Personalizing recommendations for tourists. Telematics and Informatics, 27(1), 51–66. https://doi.org/10.1016/j.tele.2009.05.003
Kevin, A., Bakker, M., van Loon, A. M., Kral, M., & Camp, G. (2025). Young learners’ motivation, self-regulation and performance in personalized learning. Computers & Education, 226, 105208. https://doi.org/10.1016/J.COMPEDU.2024.105208
Lan, M., & Zhou, X. (2025a). A qualitative systematic review on AI empowered self-regulated learning in higher education. Npj Science of Learning, 10(1), 21. https://doi.org/10.1038/s41539-025-00319-0
Lan, M., & Zhou, X. (2025b). A qualitative systematic review on AI empowered self-regulated learning in higher education. Npj Science of Learning, 10(1), 21. https://doi.org/10.1038/s41539-025-00319-0
Lepore, M. (2024). A holistic framework to model student’s cognitive process in mathematics education through fuzzy cognitive maps. Heliyon, 10(16), e35863. https://doi.org/10.1016/j.heliyon.2024.e35863
Li, X., Li, T., Wang, M., Tao, S., Zhou, X., Wei, X., & Guan, N. (2025). Navigating the Textual Maze: Enhancing Textual Analytical Skills Through an Innovative GAI Prompt Framework. IEEE Transactions on Learning Technologies, 18, 206–215. https://doi.org/10.1109/TLT.2025.3539104
Makhno, K., Kireeva, N., & Shurygin, V. (2022). The impact of online learning technology on self-regulation and student success. Research in Learning Technology, 30. https://doi.org/10.25304/rlt.v30.2802
Martin, A. J., & Evans, P. (2018). Load reduction instruction: Exploring a framework that assesses explicit instruction through to independent learning. Teaching and Teacher Education, 73, 203–214. https://doi.org/10.1016/J.TATE.2018.03.018
Mohamed, R. A., Taref, N. N., Osman, N. E., Keshta, N. H. A., Alboghdady, M. A., Marzouk, M. M., … Fadel, E. A. (2024). Effect of web-based health education on nursing students’ knowledge, adaptive healthy measures and attitudes regarding polycystic ovary syndrome: a randomized controlled trial. BMC Nursing, 23(1), 479. https://doi.org/10.1186/s12912-024-02015-7
Molenaar, I. (2022). The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning. Computers and Education: Artificial Intelligence, 3, 100070. https://doi.org/10.1016/J.CAEAI.2022.100070
Nation, M., Habichler, J., Tadepalli, S., Ng, J., & ... (2007). Personalized learning recommendations. US Patent App. 10 …. Retrieved from https://patents.google.com/patent/US20070203711A1/en
Ncube, M., & Luneta, K. (2025). Concept-based instruction: Improving learner performance in mathematics through conceptual understanding. Pythagoras, 46(1). https://doi.org/10.4102/pythagoras.v46i1.815
O’Donoghue, J. (2009). Technology-Supported Environments for Personalized Learning: Methods and Case Studies. https://doi.org/10.4018/978-1-60566-884-0
Oubalahcen, H., Tamym, L., & Driss El Ouadghiri, M. lay. (2023). The Use of AI in E-Learning Recommender Systems: A Comprehensive Survey. Procedia Computer Science, 224, 437–442. https://doi.org/10.1016/j.procs.2023.09.061
Pachón-Basallo, M., de la Fuente, J., González-Torres, M. C., Martínez-Vicente, J. M., Peralta-Sánchez, F. J., & Vera-Martínez, M. M. (2022). Effects of factors of self-regulation vs. factors of external regulation of learning in self-regulated study. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.968733
Patrick, P. M., Yip, S. Y., & Campbell, C. (2025). Artificial Intelligence and Higher‐Order Thinking: A Systematic Review of Educator and Student Experiences and Perspectives in Higher Education. Higher Education Quarterly, 79(4). https://doi.org/10.1111/hequ.70069
Rahayu, N. W., Ferdiana, R., & Kusumawardani, S. S. (2022). A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence, 3, 100047. https://doi.org/10.1016/J.CAEAI.2022.100047
Rasheed, Z., Ghwanmeh, S., & Abualkishik, A. Z. (2023). Harnessing Artificial Intelligence for Personalized Learning: A Systematic Review. Data and Metadata, 2, 146. https://doi.org/10.56294/dm2023146
Reigeluth, C. (1999). Instructional-design theories and models, Vol. II: A new paradigm of instructional theory (92) (Vol. 2).
Reigeluth, C. M., & Moore, J. (2009). Cognitive Education and the Cognitive Domain. Instructional-Design Theories and Models, 2.
Scheiner, T., Buchholtz, N., & Kaiser, G. (2024). Mathematical knowledge for teaching and mathematics didactic knowledge: a comparative study. Journal of Mathematics Teacher Education, 27(6), 1083–1104. https://doi.org/10.1007/s10857-023-09598-z
Schwamborn, A., Thillmann, H., Opfermann, M., & Leutner, D. (2011). Cognitive load and instructionally supported learning with provided and learner-generated visualizations. Computers in Human Behavior, 27(1), 89–93. https://doi.org/10.1016/J.CHB.2010.05.028
Shi, Y., & Yang, H. (2025). Development, revision, and validation of a Self-Regulated Learning Questionnaire for Chinese undergraduate students. Acta Psychologica, 256, 104956. https://doi.org/10.1016/J.ACTPSY.2025.104956
Sibley, L., Lachner, A., Plicht, C., Fabian, A., Backfisch, I., Scheiter, K., & Bohl, T. (2024). Feasibility of adaptive teaching with technology: Which implementation conditions matter? Computers & Education, 219, 105108. https://doi.org/10.1016/J.COMPEDU.2024.105108
Song, X. (2024). Research on a personalised education recommendation system based on deep learning: from learning data to customised learning paths. 2024 5th International Conference on Information Science and Education (ICISE-IE), 147–152. IEEE. https://doi.org/10.1109/ICISE-IE64355.2024.11025504
Su, J. M. (2020). A rule‐based self‐regulated learning assistance scheme to facilitate personalized learning with adaptive scaffoldings: A case study for learning computer software. Computer Applications in Engineering Education. https://doi.org/10.1002/cae.22222
Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H. (2025). Artificial intelligence-enabled adaptive learning platforms: A review. Computers and Education: Artificial Intelligence, 9, 100429. https://doi.org/10.1016/J.CAEAI.2025.100429
Taylor, D. L., Yeung, M., & Bashet, A. Z. (2021). Personalized and Adaptive Learning. https://doi.org/10.1007/978-3-030-58948-6_2
Thanheiser, E., & Melhuish, K. (2023). Teaching routines and student-centered mathematics instruction: The essential role of conferring to understand student thinking and reasoning. The Journal of Mathematical Behavior, 70, 101032. https://doi.org/10.1016/j.jmathb.2023.101032
Trinovita, D., Nurchurifiani, E., Hastomo, T., Andewi, W., & Hasbi, M. (2025). Exploring the Influence of Generative AI on Self-Regulated Learning: A Mixed-Methods Study in the EFL Context. Jurnal Iqra’ : Kajian Ilmu Pendidikan, 10(2), 301–316. https://doi.org/10.25217/ji.v10i2.6389
Turmuzi, M., Azmi, S., & Kertiyani, N. M. I. (2026). ChatGPT in school mathematics education: A systematic review of opportunities, challenges, and pedagogical implications. Teaching and Teacher Education, 170, 105286. https://doi.org/10.1016/j.tate.2025.105286
Wakhata, R., Balimuttajjo, S., & Mutarutinya, V. (2023). Building on Students’ Prior Mathematical Thinking: Exploring Students’ Reasoning Interpretation of Preconceptions in Learning Mathematics. Mathematics Teaching Research Journal, 15(1), 127–151.
Wang, C.-Y., & Lin, J. J. H. (2023). Utilizing artificial intelligence to support analyzing self-regulated learning: A preliminary mixed-methods evaluation from a human-centered perspective. Computers in Human Behavior, 144, 107721. https://doi.org/10.1016/j.chb.2023.107721
Wang, M., Matore, M. E. M., & Rosli, R. (2025). Analytical Thinking in Mathematics: A Systematic Literature Review on Research Types and Content. International Journal of Instruction, 18(4), 153–176. https://doi.org/10.29333/iji.2025.1849a
Wang, M., Mohd Matore, M. E. E., & Rosli, R. (2025). A systematic literature review on analytical thinking development in mathematics education: trends across time and countries. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1523836
Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793–803. https://doi.org/10.1080/10494820.2020.1808794
Winne, P. H., & Perry, N. E. (2000). Measuring Self-Regulated Learning. In Handbook of Self-Regulation (pp. 531–566). Elsevier. https://doi.org/10.1016/B978-012109890-2/50045-7
Wu, T. T., Lee, H. Y., Li, P. H., Huang, C. N., & Huang, Y. M. (2023). Promoting Self-Regulation Progress and Knowledge Construction in Blended Learning via ChatGPT-Based Learning Aid. Journal of Educational Computing Research. https://doi.org/10.1177/07356331231191125
Zhou, J., & Abisado, M. B. (2025a). Innovative Negative Sampling and Natural Language Processing Approach for Optimizing Knowledge Point Recommendations in Personalized Learning. 2025 8th International Conference on Information and Computer Technologies (ICICT), 248–253. IEEE. https://doi.org/10.1109/ICICT64582.2025.00045
Zhou, J., & Abisado, M. B. (2025b). Innovative Negative Sampling and Natural Language Processing Approach for Optimizing Knowledge Point Recommendations in Personalized Learning. 2025 8th International Conference on Information and Computer Technologies (ICICT), 248–253. IEEE. https://doi.org/10.1109/ICICT64582.2025.00045
Zhuang, Y. (2026). Empowering students to critically validate AI-generated mathematical solutions through the rational questioning approach. Educational Studies in Mathematics. https://doi.org/10.1007/s10649-026-10528-y
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/S15430421TIP4102_2

Details

Primary Language

English

Subjects

Instructional Technologies, Curriculum and Instration (Other)

Journal Section

Research Article

Authors

Hariyanto S. Auna ^*
0009-0008-4052-8690
Indonesia

Dedi Kuswandi
0000-0003-1005-6641
Indonesia

Henry Praherdhiono
0000-0002-5922-7491
Indonesia

Made Duananda Kartika Degeng
0000-0002-6749-6237
Indonesia

Saida Ulfa
0000-0002-2302-7172
Indonesia

Publication Date

November 1, 2026

Submission Date

April 10, 2026

Acceptance Date

July 7, 2026

Published in Issue

Year 2026 Volume: 13 Number: 6

DOI

https://doi.org/10.17275/per.26.83.13.6

IZ

https://izlik.org/JA89ZY32HB

APA

S. Auna, H., Kuswandi, D., Praherdhiono, H., Degeng, M. D. K., & Ulfa, S. (2026). The Effect of AI-Driven Personalized Learning with a Recommendation System on Students’ Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning. Participatory Educational Research, 13(6), 168-191. https://doi.org/10.17275/per.26.83.13.6

AMA

1.S. Auna H, Kuswandi D, Praherdhiono H, Degeng MDK, Ulfa S. The Effect of AI-Driven Personalized Learning with a Recommendation System on Students’ Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning. PER. 2026;13(6):168-191. doi:10.17275/per.26.83.13.6

Chicago

S. Auna, Hariyanto, Dedi Kuswandi, Henry Praherdhiono, Made Duananda Kartika Degeng, and Saida Ulfa. 2026. “The Effect of AI-Driven Personalized Learning With a Recommendation System on Students’ Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning”. Participatory Educational Research 13 (6): 168-91. https://doi.org/10.17275/per.26.83.13.6.

EndNote

S. Auna H, Kuswandi D, Praherdhiono H, Degeng MDK, Ulfa S (November 1, 2026) The Effect of AI-Driven Personalized Learning with a Recommendation System on Students’ Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning. Participatory Educational Research 13 6 168–191.

IEEE

[1]H. S. Auna, D. Kuswandi, H. Praherdhiono, M. D. K. Degeng, and S. Ulfa, “The Effect of AI-Driven Personalized Learning with a Recommendation System on Students’ Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning”, PER, vol. 13, no. 6, pp. 168–191, Nov. 2026, doi: 10.17275/per.26.83.13.6.

ISNAD

S. Auna, Hariyanto - Kuswandi, Dedi - Praherdhiono, Henry - Degeng, Made Duananda Kartika - Ulfa, Saida. “The Effect of AI-Driven Personalized Learning With a Recommendation System on Students’ Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning”. Participatory Educational Research 13/6 (November 1, 2026): 168-191. https://doi.org/10.17275/per.26.83.13.6.

JAMA

1.S. Auna H, Kuswandi D, Praherdhiono H, Degeng MDK, Ulfa S. The Effect of AI-Driven Personalized Learning with a Recommendation System on Students’ Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning. PER. 2026;13:168–191.

MLA

S. Auna, Hariyanto, et al. “The Effect of AI-Driven Personalized Learning With a Recommendation System on Students’ Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning”. Participatory Educational Research, vol. 13, no. 6, Nov. 2026, pp. 168-91, doi:10.17275/per.26.83.13.6.

Vancouver

1.Hariyanto S. Auna, Dedi Kuswandi, Henry Praherdhiono, Made Duananda Kartika Degeng, Saida Ulfa. The Effect of AI-Driven Personalized Learning with a Recommendation System on Students’ Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning. PER. 2026 Nov. 1;13(6):168-91. doi:10.17275/per.26.83.13.6

27707

Creative Commons Atıf 4.0 International Lisance

download?token=eyJhdXRoX3JvbGVzIjpbXSwiZW5kcG9pbnQiOiJqb3VybmFsIiwib3JpZ2luYWxuYW1lIjoicGVyIHNjb3B1cyBpc3RhdHRpc3Rpay5wbmciLCJwYXRoIjoiOTg5Ni9mN2E1LzU3ZDcvNmEyMmIyZmFlMGJkYjYuMDUyNjIyMDMucG5nIiwiZXhwIjoxNzgwNjYyNTM5LCJub25jZSI6IjcxYzczMjhhOTdkOWYxM2ExZjE2NWExYjQyMzMyOWJhIn0.g4uvJoi6Oy8W0gRoUNW-d5FO9m4u30pEA0l1h8DwzSE

The Effect of AI-Driven Personalized Learning with a Recommendation System on Students' Conceptual Understanding and Analytical Skills: The Moderating Role of Self-Regulated Learning

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite