Integrating AI-Based Learning Management Systems to Improve Science Concept Understanding among Junior High School Students - Rahman, Dedi Irawadi; Bahri, Syamsul (Journal of Education and Social Science, 2026)
The rapid advancement of artificial intelligence (AI) has significantly influenced educational practices, particularly through the integration of AI-based Learning Management Systems (LMS). This study aims to examine the effectiveness of integrating AI-based LMS in improving science concept understanding among junior high school students. Employing a quasi-experimental research design, the study involved two groups: an experimental group that utilized an AI-based LMS and a control group that received conventional instruction. Data were collected through pre-tests and post-tests designed to measure students’ conceptual understanding of science topics, complemented by observations and student feedback questionnaires. The findings indicate a significant improvement in science concept understanding among students who engaged with the AI-based LMS compared to those in the control group. Features such as adaptive learning pathways, real-time feedback, and personalized content delivery were found to enhance student engagement and facilitate deeper conceptual comprehension. Furthermore, students demonstrated positive attitudes toward the use of AI-driven learning platforms, highlighting increased motivation and learning autonomy. The results suggest that integrating AI-based LMS can serve as an effective instructional strategy to support science learning at the junior high school level. This study contributes to the growing body of research on educational technology by providing empirical evidence on the pedagogical benefits of AI integration in secondary education. Future research is recommended to explore long-term impacts and implementation challenges across diverse educational contexts.