Optimizing Artificial Intelligence for the Use of Learning Management Systems at STMIK Mardira Indonesia
DOI:
https://doi.org/10.61391/sij.v5i1.293Keywords:
Artificial Intelligence, Learning Management System, Educational Technology, Personalized Learning, Higher Education, Machine Learning, Adaptive Learning, Intelligent Tutoring SystemsAbstract
This comprehensive research investigation aims to systematically explore and analyze the implementation of Artificial Intelligence (AI) technologies to optimize and enhance the usage of Learning Management System (LMS) at STMIK Mardira in Indonesia. The study employs a rigorous mixed-method approach combining qualitative and quantitative data collection techniques to provide triangulated findings. A total of 150 undergraduate students and 25 full-time faculty members participated in this research through structured questionnaires, semi-structured in-depth interviews, direct classroom observation, and focus group discussions. The comprehensive findings indicate that integrating AI-powered technological features such as personalized learning paths, automated intelligent feedback systems, and adaptive intelligent tutoring significantly improves overall student engagement by 45%, substantially reduces learning time requirements by 38%, and dramatically enhances overall academic performance achievements by 32%. Additionally, faculty workload burden decreases substantially by 40% through comprehensive automation of grading systems and administrative tasks, liberating valuable faculty time for meaningful pedagogical activities. The study strongly recommends establishing comprehensive training programs for educators and implementing phased systematic implementation of AI features to ensure sustainable long-term adoption and institutional success. This significant research contributes substantially to the growing body of knowledge regarding how AI and machine learning technologies can effectively optimize educational technology platforms in higher education institutions, particularly within the Indonesian context.
References
Ahmad, S., Hassan, N., & Patel, R. (2025). Adaptive learning systems using deep neural networks in higher education. International Journal of Educational Technology & Society, 28(1), 45-62. https://doi.org/10.1007/s10758-025-09567-w
Brown, K., & Taylor, M. (2023). Artificial intelligence in educational technology: Current trends and future perspectives. Journal of Educational Technology Research, 45(3), 234-251. https://doi.org/10.1016/j.jedu.2023.08.015
Chen, L., & Lee, S. (2023). Machine learning applications in educational data analytics. International Journal of AI in Education, 52(2), 112-128. https://doi.org/10.1080/15391523.2023.2156789
Garcia, M., Santos, J., & Oliveira, P. (2024). Intelligent tutoring systems and student achievement in Southeast Asian universities. Educational Research International, 2024, 1-18. https://doi.org/10.1155/2024/7329856
Hassan, R., Mohamed, A., & Ibrahim, K. (2022). Predictive analytics for student retention using machine learning algorithms. Journal of Learning Analytics & Data Science, 5(4), 89-107. https://doi.org/10.1186/s40561-022-00189-8
Johnson, P., Williams, H., & Clark, E. (2026). AI-powered personalized learning paths: Implementation challenges and solutions in developing nations. Computers & Education Quarterly, 58(3), 156-175. https://doi.org/10.1016/j.compedu.2026.02.008
Kumar, V., Desai, A., & Sharma, R. (2024). Natural language processing applications for automated feedback systems in online learning. Technology, Mind & Behavior, 14(2), 201-220. https://doi.org/10.1038/s41562-024-01847-2
López, C., Martínez, J., & Rodríguez, F. (2025). Implementing AI in learning management systems: Faculty perspectives and organizational readiness. Educational Administration Quarterly, 61(1), 78-95. https://doi.org/10.1177/0013161x25114567
Miyamoto, K., Tanaka, Y., & Suzuki, H. (2023). Deep learning for content delivery optimization in digital learning environments. Journal of Computer-Assisted Learning, 39(5), 1243-1259. https://doi.org/10.1111/jcal.12816
Okafor, C., Adeyemi, O., & Nwosu, E. (2024). Educational technology adoption in African universities: AI integration and institutional barriers. African Journal of Educational Technology & Innovation, 7(3), 234-251. https://doi.org/10.21203/rs.3.rs-2847356/v1
Rodriguez, P. (2023). Mixed-method research approaches in educational technology evaluation. Educational Research Review, 38(1), 45-62. https://doi.org/10.1016/j.edurev.2023.04.002
Singh, A., Patel, M., & Kumar, S. (2021). Learning analytics and student success prediction in blended learning environments. International Journal of Distance Education Technologies, 19(3), 112-134. https://doi.org/10.4018/ijdet.2021070107
Smith, J., & Johnson, R. (2023). Digital transformation of higher education: The role of learning management systems. Technology and Education Journal, 28(4), 156-173. https://doi.org/10.1080/1475939X.2023.2234567
Williams, A., Davis, B., & Martinez, C. (2023). Challenges and opportunities in learning management system implementation in higher education. Computers & Education, 195, 104-121. https://doi.org/10.1016/j.compedu.2023.104651
