AI integration prototype in Learning Management System (LMS) for personalization of student learning

Kuntum An Nisa Imania; Yuniar Purwanti; Siti Husnul Bariah; Dian Mardiani; Siti Zulva

doi:10.64014/jik.v23i4.762

Abstract

The need for an online learning system that can adapt materials, activities, and learning recommendations to individual characteristics, learning styles, and mastery levels has driven the development of a prototype that integrates Artificial Intelligence (AI) into a Learning Management System (LMS) to support personalized student learning. This study aims to design and test an adaptive AI-based LMS prototype that can improve student effectiveness, efficiency, and engagement in the learning process. This study used the Research and Development (R&D) method, following the Lee and Owens model, and was limited to the implementation stage. The trial results indicate that the prototype is feasible to develop and falls into the very feasible category. The study deemed the purpose, content structure, scope, functionality, and scalability very adequate. The highest-rated aspects were technical risks and tangible user benefits, indicating confidence that the prototype can make a significant contribution to learning effectiveness. The prototype meets design and implementation feasibility standards. However, it needs improvements in security, privacy, and technical performance to support personalized learning more effectively and sustainably, in accordance with education regulations and data protection.

Abstrak

Kebutuhan akan sistem pembelajaran daring yang mampu menyesuaikan materi, aktivitas, dan rekomendasi belajar sesuai karakteristik, gaya belajar, serta tingkat penguasaan individu, sehingga mendorong pengembangan prototipe integrasi Artificial Intelligence (AI) dalam Learning Management System (LMS) untuk mendukung personalisasi pembelajaran mahasiswa. Penelitian ini bertujuan untuk merancang dan menguji prototipe LMS berbasis AI yang adaptif, sehingga dapat meningkatkan efektivitas, efisiensi, dan keterlibatan mahasiswa dalam proses belajar. Penelitian ini menggunakan metode Research and Development (R&D) dengan model Lee dan Owens, serta dibatasi hingga tahap implementasi terbatas. Hasil uji coba menunjukkan bahwa prototipe layak dikembangkan dalam kategori sangat layak. Aspek tujuan, struktur konten, ruang lingkup, fungsionalitas, dan skalabilitas dinilai sangat memadai. Aspek dengan penilaian tertinggi adalah risiko teknis yang dipertimbangkan dan manfaat nyata bagi pengguna, yang menunjukkan keyakinan bahwa prototipe dapat memberikan kontribusi signifikan terhadap efektivitas pembelajaran. Prototipe telah memenuhi standar kelayakan desain dan implementasi, namun perlu penyempurnaan pada aspek keamanan, privasi, dan performa teknis agar dapat mendukung personalisasi pembelajaran secara lebih optimal, berkelanjutan, serta sesuai dengan regulasi pendidikan dan perlindungan data.

Kata Kunci: kecerdasan buatan; learning management system; personalisasi pembelajaran

Keywords:

artificial Intellegence learning management system personalization of learning

References

Abrar, M., Aboraya, W., Khaliq, R. A., Subramanian, K. P., Al Husaini, Y., & Al Hussaini, M. (2025). AI-powered learning pathways: Personalized learning and dynamic assessments. International Journal of Advanced Computer Science and Applications, 16(1), 454-462.

Afolabi, I. Y., Ugah, J. O., Igwe, J. S., & Nwali, M. E. (2025). Development of an ai-driven adaptive learning management system using data analytics. International Journal of Scientific Research in Modern Science and Technology, 4(7), 52-63.

Al Fadillah, Y., & Akbar, A. R. (2024). Strategi desain pembelajaran adaptif untuk meningkatkan pengalaman belajar di era digital. Jurnal Pendidikan Sains dan Teknologi Terapan, 1(4), 354-362.

Alotaibi, N. S. (2024). The impact of AI and LMS integration on the future of higher education: Opportunities, challenges, and strategies for transformation. Sustainability, 16(23), 1-21.

Alstete, J. W., & Meyer, J. P. (2026). Personalized, experiential, and future-centric: An adaptive business education framework. Journal of International Education in Business, 19(2), 200-216.

Amelia, R., Izzah, S. N. R., Hikmah, M. A., & Bakar, M. Y. A. (2025). Memahami gaya belajar siswa: Kunci keberhasilan personalisasi pembelajaran. Jurnal Ilmiah Nusantara, 2(1), 287-300.

Asrida, Y. N., Amanda, F., & Fadilah, J. U. (2024). Effectiveness and limitations on Learning Management Systems (LMS) in learning and teaching: A systematic review. Icoeress, 1(1), 141-152.

Azhar, K., Ali, M., Ropo, E., Wahyudin, D., & Hadiapurwa, A. (2024). Balancing control and freedom: Conditional autonomy in curriculum management in an Islamic Private School in Indonesia. Inovasi Kurikulum, 21(4), 2021-2036.

Bariah, S. H., Rahadian, D., Imanis, K. A. N., Purwanti, Y., & Nasruloh, I. (2021, March). Prototyping online learning media using SCORM. IOP Conference Series: Materials Science and Engineering, 1098(3), 1-8.

Beirat, M. A., Tashtoush, D. M., Khasawneh, M. A., Az-Zo’bi, E. A., & Tashtoush, M. A. (2025). The effect of artificial intelligence on enhancing education quality and reduce the levels of future anxiety among Jordanian teachers. Appl. Math, 19(2), 279-290.

Bernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021). A systematic review of research on personalized learning: Personalized by whom, to what, how, and for what purpose(s)?. Educational Psychology Review, 33(4), 1675-1715.

Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3(1), 1-18.

Budiman, R., & Syafrony, A. I. (2023). The digital literacy of first-year students and its function in an online method of delivery. Asian Association of Open Universities Journal, 18(2), 176-186.

Bukhari, I. A., Mukaromah, M., & Puspitasari, N. (2024). Implementasi kecerdasan buatan untuk personalisasi pembelajaran di perguruan tinggi. Prosiding seminar nasional AMIKOM Surakarta, 2(1), 1520-1530.

Chen, S. Y., & Wang, J. H. (2021). Individual differences and personalized learning: a review and appraisal. Universal Access in the Information Society, 20(4), 833-849.

Fitrian, H. P., Munawar, A. H., Fajar, B. N., & Aima, F. N. (2025). Analisis keamanan dan ancaman pada jaringan komputer di era Internet of Things (IoT) dalam bidang teknologi sistem pendidikan. Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), 8(1), 1-10.

Hujah, I., Yusuf, F. A., Mutoharoh, M., & Maarif, M. (2025). Digital transformation in learning: A systematic review of digital technology utilization to enhance student-centered learning effectiveness. Southeast Asian Journal of Science and Technology, 9(2), 56-64.

Ikhsan, I., Husnaini, A. N., & Lopo, F. L. (2025). Strategies for implementing ai in LMS to improve the effectiveness and personalization of digital learning. The Journal of Academic Science, 2(3), 944-953.

Indriati, P., Salim, M. F. S., & Sihite, M. (2023). Kinerja perguruan tinggi dalam perspektif kinerja layanan, strategi pemanfataan teknologi dan kompetensi sumber daya manusia. JIMP: Jurnal Ilmiah Manajemen Pancasila, 3(1), 12-30.

Ingkavara, T., Panjaburee, P., Wongkia, W., & Yasri, P. (2026). Effects of a self-regulated personalised ubiquitous learning system on students' Mathematics learning achievement and behavioural patterns. International Journal of Mobile Learning and Organisation, 20(4), 376-401.

Insan, K., Huda, A., Irfan, D., & Hendriyani, Y. (2024). Study literature review penggunaan teknologi kecerdasan buatan dalam personalisasi pembelajaran online. Jurnal Teknik Komputer dan Informatika, 4(3), 1-7.

Maghfiroh, N. (2026). Digi-Equity MI: Rancangan ekosistem LMS adaptif berbasis AI untuk penguatan literasi digital guru madrasah ibtidaiyah. Jurnal Perspektif Ilmu Pendidikan dan Keguruan, 1(1), 1-7.

Mahabu, F. F., Subhan, M., Pramadita, O. I., Fahriza, A., & Ekabudi, A. (2025). Pemanfaatan Learning Management System (LMS) untuk meningkatkan efektifitas pembelajaran. Jurnal Teknologi Pendidikan dan Pembelajaran, 3(1), 27-34.

Maola, P. S., Handak, I. S. K., & Herlambang, Y. T. (2024). Penerapan artificial intelligence dalam pendidikan di era revolusi industri 4.0. Educatio, 19(1), 61-72.

Masmur, M. (2025). Transformasi manajemen dosen pendidikan tinggi melalui integrasi artificial intelligence dalam pembelajaran. Jurnal Ilmiah Edukatif, 11(2), 415-434.

Mishra, N. (2025). Cognitive-inspired adaptive learning models for personalized digital education. Advances in Cognitive and Neural Studies, 1(3), 46-53.

Omar, A. S., Mgala, M., & Mwakondo, F. (2025). AI-driven visual scaffolding in education: A comprehensive literature review. International Journal of Research and Scientific Innovation, 12(3), 740-750.

Peliza, R. (2025). Implementasi artificial intelligence dalam sistem pembelajaran adaptif berbasis web pada pendidikan tinggi. Mutiara: Jurnal Penelitian dan Karya Ilmiah, 3(6), 24-37.

Putra, A. A., & Nuryadi, N. (2019). Pengembangan media pembelajaran interaktif berbasis LMS Moodle ditinjau dari cognitive loads theory. Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika, 6(2), 54-60.

Rahadian, D., Ilham, S., Imania, K. A. N., & Nasrulloh, I. (2024). Development of google sites-based e-learning with time learning model. 4th International Conference on Education and Technology (ICETECH 2023), 1(1), 657-678.

Rahayaan, J. A., Porayou, A. I., Patanduk, A., & Yusupa, A. (2026). AI-driven learning personalization in lms platforms: A systematic review of mechanisms, effectiveness, and computational challenges. Jurnal Ilmiah Informatika dan Komputer, 3(1), 26-37.

Rostiana, N., & Wahyudin, D. (2025). The influence of the STAD learning model towards improving Mathematics learning outcomes. Inovasi Kurikulum, 22(2), 699-710.

Ruba, N., & Priyadharshini, P. (2025). AI-powered personalized learning pathways in online education. International Journal of Computer Science (IJCS Journal), 13(1), 42-52.

Saifullah, S., Yawan, H., Syafitri, N., & Nurhaliza, S. (2024). Integrating AI chatbot into learning management system: enhancing student engagement and learning outcomes. Dharmas Education Journal (DE_Journal), 5(2), 1346-1359.

Santoso, J. (2023). Mengatasi tantangan keterlibatan mahasiswa: Strategi efektif untuk menciptakan lingkungan belajar yang menarik. Jurnal Ilmiah Kanderang Tingang, 14(2), 469-478.

Sari, H. E., Tumanggor, B., & Efron, D. (2024). Improving educational outcomes through adaptive learning systems using AI. International Transactions on Artificial Intelligence, 3(1), 21-31.

Sengupta, S., Bardhan, S., Das, R., & Chakrabarti, S. (2025). LearnAlytics: The smart way to analyze, adapt, and achieve in e-learning. SN Computer Science, 6(1), 1-10.

Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI‐driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921-1947.

Tegeh, I. M., Suarjana, I. M., & Rukmana, G. W. (2025). Mindful learning-based cognitive load management model in elementary school mathematics learning. Indones. J. Instr, 6(1), 214-221.

Tetzlaff, L., Schmiedek, F., & Brod, G. (2021). Developing personalized education: A dynamic framework. Educational Psychology Review, 33(3), 863-882.

Veluvali, P., & Surisetti, J. (2022). Learning management system for greater learner engagement in higher education—A review. Higher Education for the Future, 9(1), 107-121.

Wahyudanti, N. R., Rahmadanti, S. N., & Khimaya, N. (2023). Pengintegrasian Learning Management System (LSM) dengan artificial intelligence untuk memfasilitasi ragam kebutuhan peserta didik. Literasi: Jurnal Kajian Keislaman Multi-Perspektif, 3(2), 29-39.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education-where are the educators?. International Journal of Educational Technology in Higher Education, 16(1), 1-27.