Generative AI-Powered Personalized Learning: A Systematic Investigation of Student Engagement and Academic Achievement in Higher Education
DOI:
https://doi.org/10.63876/jets.v1i2.44Keywords:
Generative AI, Personalized Learning, Student Engagement, Academic Achievement, Higher Education, Adaptive Learning SystemsAbstract
The rapid advancement of Generative Artificial Intelligence (GenAI) has introduced transformative opportunities in higher education, particularly in enabling personalized learning experiences. This study presents a systematic investigation into the impact of GenAI-powered personalized learning systems on student engagement and academic achievement. Using a mixed-methods approach, the research integrates quantitative data collected from student performance metrics and engagement analytics with qualitative insights derived from surveys and interviews across multiple higher education institutions. The proposed framework leverages adaptive content generation, real-time feedback, and learner-specific recommendations to tailor instructional materials according to individual learning needs. The findings indicate a significant improvement in student engagement levels, as reflected in increased participation, time-on-task, and interaction frequency. Furthermore, students exposed to GenAI-driven personalized learning demonstrated measurable gains in academic achievement compared to those in traditional learning environments. The study also highlights key challenges, including ethical considerations, data privacy concerns, and the need for pedagogical alignment. Overall, this research underscores the potential of Generative AI as a catalyst for enhancing learning effectiveness and provides practical implications for educators and policymakers in designing future-ready educational systems.
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