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@article{171325,
        author = {Dr. Arul R and Jesma Michael X and Hari Priya K and Parvathi C},
        title = {Exploring the Effectiveness of AI-Based Personalized Learning Systems in Improving Student Engagement and Performance Across Diverse Learning Styles},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3850-3858},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171325},
        abstract = {AI-based personalized learning systems have revolutionized education by offering tailored learning experiences to address the unique needs of diverse learners. These systems utilize advanced algorithms to customize content, pacing, and strategies based on individual students’ strengths, weaknesses, and preferences. This study investigates the effectiveness of AI-driven personalized learning platforms in enhancing student engagement and academic performance across various learning styles, with data collected from 478 respondents. By integrating machine learning and data analytics, these systems optimize content delivery, provide real-time feedback, and create an adaptive and inclusive learning environment. The research examines their impact in K-12 and higher education settings, focusing on improvements in student outcomes and their ability to support auditory, visual, and kinesthetic learners. Additionally, the study addresses the challenges of implementing AI in education, including teacher adoption, student motivation, and data privacy concerns. This research aims to offer valuable insights into leveraging AI to create more personalized, engaging, and effective learning experiences for students with diverse educational needs.},
        keywords = {AI-based personalized learning systems - Student engagement - Learning styles - Machine learning - Academic performance},
        month = {December},
        }