An Artificial Intelligence Based Recommendation Model for Personalizing Students' Learning Interest Paths at Universities

Safrizal Safrizal (Universitas Pembangunan Jaya)
Chaerul Anwar (Universitas Pembangunan Jaya)
Augury El Rayeb (Universitas Pembangunan Jaya)

Proceeding of The International Conference on Electrical Engineering and Informatics
Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and

This study explores the integration of artificial intelligence (AI) in education, particularly in supporting personalized learning. AI presents new opportunities through adaptive learning platforms, virtual tutors, and intelligent assessment systems that have the potential to revolutionize teaching and learning methods. By conducting in-depth data analysis, AI can identify student performance patterns and provide tailored recommendations, enabling educators to deliver more targeted interventions. Furthermore, personalized learning plays a crucial role in enhancing student motivation and engagement by customizing learning experiences to meet individual needs and learning styles. This study aims to implement personalized learning strategies in educational settings and offers insights into best practices for their integration. It also examines their impact on student engagement and academic achievement. The findings highlight the importance of personalized learning in fostering an inclusive and effective educational environment. By leveraging AI, educators can optimize learning, empower students, and address achievement gaps. This study provides practical recommendations for educators and policymakers to implement AI-based learning strategies effectively.