Artificial Intelligence in Personalized Learning: Enhancing Student Engagement through Adaptive Learning Systems - Arief Hidayat; Maryana Maryana; Rustiyana Rustiyana; Triyugo Winarko (Yayasan Adra Karima Hubbi, 2025)
Background. Advancements in artificial intelligence (AI) have transformed educational practices by enabling personalized learning experiences that adapt to individual student needs. Traditional instructional methods often fail to accommodate diverse learning paces, preferences, and competencies, leading to disengagement and suboptimal learning outcomes. Purpose. This study investigates the effectiveness of AI-based adaptive learning systems in promoting personalized learning and increasing student engagement across multiple educational contexts. Method. A mixed-methods research design was employed, combining quantitative analysis of engagement metrics and academic performance with qualitative exploration through student interviews and teacher observations. Results. Results indicated significant improvements in engagement, motivation, and learning outcomes, with adaptive feedback and personalized content contributing to sustained participation and deeper comprehension. Students reported higher satisfaction and perceived control over their learning processes, while educators noted more efficient monitoring and instructional planning. Conclusion. The study concludes that integrating AI into personalized learning systems can substantially enhance engagement and academic performance.