Trends and Challenges for AI in Higher Education

This article explores the integration of AI in higher education and personalized learning experiences through a systematic review of literature from 2020 to 2024, with a focus on its role in personalizing learning experiences. Utilizing PRISMA guidelines, the study initially identified 647 articles, of which 186 were included after systematic screening and quality assessment. From these, 24 studies were selected for in-depth analysis based on methodological rigor and relevance. The review highlights reported improvements in adaptive learning systems, predictive analytics, and intelligent tutoring, enhancing student engagement and success. Despite these advancements, challenges persist in digital infrastructure, data privacy, and algorithm bias. Overcoming these barriers can make higher education more inclusive and efficient. For instance, some of the analyzed studies highlight performance improvements of up to 20% with adaptive systems, and predictive models reaching up to 93% accuracy in identifying at-risk students. A key insight regarding intelligent tutoring systems is their potential to enhance knowledge retention and provide real-time feedback to support student progress.