Enhancing Student Engagement and Academic Performance through Artificial Intelligence-Based Adaptive Learning Systems Muhammad Aryuda Silfa
Universitas Terbuka
Abstract
The integration of artificial intelligence (AI) in education has significantly transformed teaching and learning processes, particularly in improving student engagement and personalized learning experiences. This study aims to examine the effectiveness of AI-based adaptive learning systems in enhancing student engagement and academic performance in higher education. A quantitative research design was employed using a quasi-experimental approach involving 120 undergraduate students divided into control and experimental groups. The experimental group utilized an AI-driven adaptive learning platform, while the control group engaged in conventional learning methods. Data were collected through pre and post tests, as well as student engagement questionnaires, and analyzed using paired sample t-tests and regression analysis. The results indicate that students exposed to AI-based learning systems showed a statistically significant improvement in both engagement levels and academic achievement compared to the control group. Furthermore, AI personalization features contributed to better learning outcomes by adapting content to individual student needs. In conclusion, AI-based adaptive learning systems offer a promising approach to enhance learning effectiveness and support sustainable educational development.
