The Effect of The Use of Artificial Intelligence and Blended Learning on Student Learning Motivation
DOI:
https://doi.org/10.31851/jmksp.v10i2.21155Keywords:
Artificial Intelligence, blended learning, learning motivation, university student, higher educationAbstract
This research is conducted to comprehensively examine how the application of artificial intelligence together with blended learning contributes to variations in student learning motivation among university students located in Surakarta, utilizing a quantitative methodological framework supported by a correlational survey model that engaged 76 undergraduate participants, with empirical data gathered through a structured five level Likert scale instrument and statistically processed using multiple linear regression techniques supported by SPSS software version 26. The empirical findings demonstrate that artificial intelligence and blended learning significantly enhance student learning motivation when evaluated individually as well as collectively, with artificial intelligence emerging as the factor exerting a stronger statistical contribution to motivational improvement. These findings suggest that the integration of adaptive learning technology and well-planned blended learning can support more flexible and engaging learning environments, thereby enhancing student learning motivation in higher education.
References
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2023). Artificial intelligence and learning motivation in higher education. Education and Information Technologies, 28(4), 5301–5320. https://doi.org/10.1007/s10639-022-11431-4
Amelia, P., & Suranto, S. (2025). Transformasi pendidikan akuntansi melalui platform e-learning: Peran LMS dalam meningkatkan efektivitas belajar mahasiswa. Cetta: Jurnal Ilmu Pendidikan, 8(1), 236–247. https://doi.org/10.37329/cetta.v8i1.3947
Bernard, R. M. (2020). Effects of blended learning on student engagement and motivation. Journal of Educational Psychology, 112(4). https://doi.org/10.1037/edu0000380
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic review. Frontiers in Education, 5. https://doi.org/10.1186/s41239-020-00194-8
Broadbent, J. (2021). Comparing online and blended learner’s self-regulated learning strategies. The Internet and Higher Education, 48, 100790. https://doi.org/10.1016/j.iheduc.2020.100790
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Graham, C. R. (2020). Current research in blended learning. British Journal of Educational Technology, 51(2), 355–358. https://doi.org/10.1111/bjet.12829
Hendrilia, Y., Salamah, S., Judijanto, L., Nuryenda, E. Y., & Fauzi, M. S. (2025). Learning Motivation as a Predictor of Academic Success: A Literature Review in Educational Psychology. TOFEDU: The Future of Education Journal, 4(6), 1841–1846. https://doi.org/10.61445/tofedu.v4i6.697
Howard, J. L., Bureau, J. S., Chong, J. X. Y., & Ryan, R. M. (2021). Student motivation and associated outcomes: A meta-analysis. Educational Psychology Review, 33, 1. https://doi.org/10.1007/s10648-020-09581-9
Hrastinski, S. (2021). What do we mean by blended learning? TechTrends, 65(5), 564–569. https://doi.org/10.1007/s11528-021-00575-5
Istenic, A. (2024). Blended learning in higher education: the integrated and distributed model and a thematic analysis. Education and Information Technologies, 3(1), 165. https://doi.org/10.1007/s44217-024-00239-y
Kasneci, E., Sessler, K., & K{"u}bler, T. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kintu, M. J., Zhu, C., & Kagambe, E. (2021). Blended learning effectiveness. International Journal of Educational Technology in Higher Education, 18(1), 1–20. https://doi.org/10.1186/s41239-021-00254-3
Lodge, J. M., Kennedy, G., Lockyer, L., Arguel, A., & Pachman, M. (2021). Understanding difficulties and resulting confusion in blended learning environments: Implications for student motivation. The Internet and Higher Education, 49. https://doi.org/10.1016/j.iheduc.2021.100794
Mariyanti, P., & Wahyudi, T. N. (2025). Student’ perception of the use of artificial intelligence (AI) technology in student creativity completing academic tasks. Didaktika: Jurnal Kependidikan, 14(1), 1–10. https://doi.org/10.58230/27454312.1442
Means, B. (2020). Digital learning in higher education. Review of Educational Research, 90(3). https://doi.org/10.3102/0034654320919090
Mohamed, A. M., Tahany, S. S., Bakry, S. H., Guillén-Gámez, F., & Strzelecki, A. (2024). Empowering the Faculty of Education Students: Applying AI’s Potential for Motivating and Enhancing Learning. Innovative Higher Education, 50, 587–609. https://doi.org/10.1007/s10755-024-09747-z
Purba, S., Lubis, D. B., Purba, G. B. S., & Simarmata, J. (2025). Pengaruh Penggunaan Teknologi AI terhadap Motivasi Belajar Mahasiswa. Jurnal Pengabdian Masyarakat Dan Riset Pendidikan (JERKIN), 3(3). https://doi.org/10.31004/jerkin.v3i3.402
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Sari, D. E., & Yudhanto, R. (2025). Analisis dampak penggunaan ChatGPT terhadap produktivitas belajar siswa di Kota Surakarta. Pedagogia: Jurnal Pendidikan, 14(1). https://doi.org/10.21070/pedagogia.v14i1.1843
Schmid, R. F. (2023). Effectiveness of blended and flipped learning. Computers & Education, 190, 104600. https://doi.org/10.1016/j.compedu.2022.104600
Syah, M. F. J., Suranto, S., Karima, A. K., & Widyasari, D. A. (2023). Model pembelajaran moving peer tutoring untuk meningkatkan literasi informasi mahasiswa. Jurnal Pendidikan Ilmu Sosial, 31(2), 95–107. https://doi.org/10.23917/jpis.v31i2.15011
Viberg, O., Khalil, M., & Baars, M. (2020). Self-regulated learning and learning analytics: A systematic review. Computers & Education, 159, 104000. https://doi.org/10.1016/j.compedu.2020.104000
Wang, Z., & Hannafin, M. J. (2021). Designing blended learning environments to support motivation and self-regulated learning. Educational Technology Research and Development, 69(2), 1039–1063. https://doi.org/10.1007/s11423-020-09848-4
Yu, Q., Yu, K., Li, B., & Wang, Q. (2023). Effectiveness of blended learning on students’ learning performance: a meta-analysis. Journal of Research on Technology in Education. https://doi.org/10.1080/15391523.2023.2264984
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2020). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 17(1), 39. https://doi.org/10.1186/s41239-019-0171-0
Zhai, X. (2021). Artificial intelligence education and student motivation. Computers & Education, 168, 104192. https://doi.org/10.1016/j.compedu.2021.104192
Zhao, Y. (2021). Blended learning and student motivation: A meta-analysis. Educational Research Review, 33, 100387. https://doi.org/10.1016/j.edurev.2021.100387
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Siti Shofiyah, Agus Susilo
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
JMKSP (Jurnal Manajemen, Kepemimpinan, dan Supervisi Pendidikan) by http://www.univpgri-palembang.ac.id/e_jurnal/index.php/JMKSP is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
1.png){kind=logo}
