Peran AI-Generated Feedback dalam Meningkatkan Self-Regulated Learning Mahasiswa
DOI:
https://doi.org/10.51903/4gj86971Keywords:
AI-generated feedback, self-regulated learning, higher educationAbstract
The integration of Artificial Intelligence (AI) into higher education has opened new opportunities to enhance Self-Regulated Learning (SRL) through AI-generated feedback. This study investigates the effectiveness of automated, real-time feedback in supporting students’ metacognitive processes of planning, monitoring, and reflection. Using a quasi-experimental mixed-method design, 124 undergraduate students were divided into experimental and control groups, with only the experimental group receiving AI-generated feedback through a university Learning management system. Data were collected via pre- and post-tests using the adapted Indonesian version of the MSLQ questionnaire, system log analytics, and semi-structured interviews. The results revealed a significant increase in SRL scores for the experimental group (average improvement of 0.67 points, p < 0.01, Cohen’s d = 0.62) and higher weekly engagement (rising from 65 to 91) compared to the control group. The AI feedback system achieved 87.4% accuracy in classifying Learning errors. Interview findings highlighted three key themes: students perceived the feedback as accurate and motivating, valued its immediacy, but also expressed concerns about its impersonal nature. These findings demonstrate that AI-generated feedback can effectively scaffold learner autonomy while complementing human facilitation. This study contributes both theoretically by linking SRL frameworks to AI-supported Learning and practically by offering insights into designing feedback systems that balance technological efficiency with pedagogical sensitivity.
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. https://doi.org/10.1007/s10639-024-13113-z
Afzaal, M., Zia, A., Nouri, J., & Fors, U. (2024). Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation. Technology, Knowledge and Learning, 29(1), 331–354. https://doi.org/10.1007/s10758-023-09650-0
Al-Abdullatif, A. M. (2024). Modeling Teachers’ Acceptance of Generative Artificial Intelligence Use in Higher Education: The Role of AI Literacy, Intelligent TPACK, and Perceived Trust. Education Sciences, 14(11), 1209. https://doi.org/10.3390/educsci14111209
Al Hakim, V. G., Yang, S. H., Liyanawatta, M., Wang, J. H., & Chen, G. D. (2022). Robots in Situated Learning Classrooms With Immediate Feedback Mechanisms to Improve Students’ Learning Performance. Computers and Education, 182, 104483. https://doi.org/10.1016/j.compedu.2022.104483
Almegren, A., Mahdi, H. S., Hazaea, A. N., Ali, J. K., & Almegren, R. M. (2024). Evaluating the Quality of AI Feedback: A Comparative Study of AI and Human Essay Grading. Innovations in Education and Teaching International, 61(6), 1858–1873. https://doi.org/10.1080/14703297.2024.2437122
Almuhanna, M. A. (2025). Teachers’ Perspectives of Integrating AI-Powered Technologies in K-12 Education for Creating Customized Learning Materials and Resources. Education and Information Technologies, 30(8), 10343–10371. https://doi.org/10.1007/s10639-024-13257-y
Ariely, M., Nazaretsky, T., & Alexandron, G. (2023). Machine Learning and Hebrew NLP for Automated Assessment of Open-Ended Questions in Biology. International Journal of Artificial Intelligence in Education, 33(1), 1–34. https://doi.org/10.1007/s40593-021-00283-x
Azzahra, W. A., Alfiana, W., & Setiabudi, D. I. (2022). Pengaruh Bahan Ajar Berbasis Audio Visual terhadap Hasil Belajar Peserta Didik Sekolah Dasar. Education: Jurnal Sosial Humaniora Dan Pendidikan, 2(1), 60–66. https://doi.org/10.51903/education.v2i1.150
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Vighio, M. S., Alblehai, F., Soomro, R. B., & Shutaleva, A. (2024). Investigating AI-Based Academic Support Acceptance and Its Impact on Students’ Performance in Malaysian and Pakistani Higher Education Institutions. Education and Information Technologies, 29(14), 18695–18744. https://doi.org/10.1007/s10639-024-12599-x
Er, E., Akçapınar, G., Bayazıt, A., Noroozi, O., & Banihashem, S. K. (2025). Assessing Student Perceptions and Use of Instructor Versus AI-Generated Feedback. British Journal of Educational Technology, 56(3), 1074–1091. https://doi.org/10.1111/bjet.13558
Gan, Z., He, J., Zhang, L. J., & Schumacker, R. (2023). Examining the Relationships Between Feedback Practices and Learning Motivation. Measurement, 21(1), 38–50. https://doi.org/10.1080/15366367.2022.2061236
Gürdür Broo, D., Kaynak, O., & Sait, S. M. (2022). Rethinking Engineering Education at the Age of Industry 5.0. Journal of Industrial Information Integration, 25, 100311. https://doi.org/10.1016/j.jii.2021.100311
Halabieh, H., Hawkins, S., Bernstein, A. E., Lewkowict, S., Unaldi Kamel, B., Fleming, L., & Levitin, D. (2022). The Future of Higher Education: Identifying Current Educational Problems and Proposed Solutions. Education Sciences, 12(12), 888. https://doi.org/10.3390/educsci12120888
Hawkins, B., Taylor-Griffiths, D., & Lodge, J. M. (2025). Summarise, Elaborate, Try Again: Exploring the Effect of Feedback Literacy on AI-Enhanced Essay Writing. Assessment and Evaluation in Higher Education, 1–13. https://doi.org/10.1080/02602938.2025.2492070
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451
Kim, P. W. (2025). Fog Computing for Artificial Intelligence Digital Textbooks: Educational Scaffolding and Security and Privacy Challenges. Expert Systems, 42(2), e13801. https://doi.org/10.1111/exsy.13801
Li, M., & Wilson, J. (2025). AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review. Information, 16(7), 519. https://doi.org/10.3390/info16070519
Luo, R. Z., & Zhou, Y. L. (2024). The Effectiveness of Self-Regulated Learning Strategies in Higher Education Blended Learning: A Five Years Systematic Review. Journal of Computer Assisted Learning, 40(6), 3005–3029. https://doi.org/10.1111/jcal.13052
Mekheimer, M. (2025). Generative AI-Assisted Feedback and EFL Writing: A Study on Proficiency, Revision Frequency and Writing Quality. Discover Education, 4(1), 170. https://doi.org/10.1007/s44217-025-00602-7
Mohebbi, A. (2025). Enabling Learner Independence and Self-Regulation in Language Education Using AI Tools: A Systematic Review. Cogent Education, 12(1), 2433814. https://doi.org/10.1080/2331186x.2024.2433814
Morales-Chan, M., Amado-Salvatierra, H. R., Medina, J. A., Barchino, R., Hernández-Rizzardini, R., & Teixeira, A. M. (2024). Personalized Feedback in Massive Open Online Courses: Harnessing the Power of LangChain and OpenAI API. Electronics, 13(10), 1960. https://doi.org/10.3390/electronics13101960
Okoye, K., Hussein, H., Arrona-Palacios, A., Quintero, H. N., Ortega, L. O. P., Sanchez, A. L., Ortiz, E. A., Escamilla, J., & Hosseini, S. (2023). Impact of Digital Technologies Upon Teaching and Learning in Higher Education in Latin America: An Outlook on the Reach, Barriers, and Bottlenecks. Education and Information Technologies, 28(2), 2291–2360. https://doi.org/10.1007/s10639-022-11214-1
Onah, D. F. O., Pang, E. L. L., & Sinclair, J. E. (2022). Investigating Self-Regulation in the Context of A Blended Learning Computing Course. International Journal of Information and Learning Technology, 39(1), 50–69. https://doi.org/10.1108/ijilt-04-2021-0059
Rensfeldt, A. B., & Rahm, L. (2023). Automating Teacher Work? A History of the Politics of Automation and Artificial Intelligence in Education. Postdigital Science and Education, 5(1), 25–43. https://doi.org/10.1007/s42438-022-00344-x
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
Theobald, M., & Bellhäuser, H. (2022). How Am I Going and Where to Next? Elaborated Online Feedback Improves University Students’ Self-Regulated Learning and Performance. Internet and Higher Education, 55, 100872. https://doi.org/10.1016/j.iheduc.2022.100872
Venter, J., Coetzee, S. A., & Schmulian, A. (2025). Exploring the Use of Artificial Intelligence (AI) in the Delivery of Effective Feedback. Assessment and Evaluation in Higher Education, 50(4), 516–536. https://doi.org/10.1080/02602938.2024.2415649
Wang, C., Chen, X., Yu, T., Liu, Y., & Jing, Y. (2024). Education Reform and Change Driven by Digital Technology: A Bibliometric Study From a Global Perspective. Humanities and Social Sciences Communications, 11(1), 1-17. https://doi.org/10.1057/s41599-024-02717-y
Zainuddin, M. A., Wulandari, R., & Putri, R. E. (2025). Exploration of Opportunities and Challenges in Implementing AI-Driven Tutoring System in 3T Area Schools: An Empirical Study in Eastern Indonesia Regions. Education: Jurnal Sosial Humaniora Dan Pendidikan, 5(1), 10–26. https://doi.org/10.51903/jt678p34
Zhang, R., & Zou, D. (2024). Self-Regulated Second Language Learning: A Review of Types and Benefits of Strategies, Modes of Teacher Support, and Pedagogical Implications. Computer Assisted Language Learning, 37(4), 720–765. https://doi.org/10.1080/09588221.2022.2055081
Zhang, Z., Xu, Q., Koehler, A. A., & Newby, T. (2023). Comparing Blended and Online Learners’ Self-Efficacy, Self-Regulation, and Actual Learning in the Context of Educational Technology. Online Learning Journal, 27(4), 295–314. https://doi.org/10.24059/olj.v27i4.4039
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