Investigating AI's Contribution to Improving Quality and Mitigating Challenges in Higher Education

Artificial Intelligence (AI) is transforming many sectors, and higher education is no exception. This study aims to explore how AI impacts the quality of higher education by reviewing existing literature and analysing data using various machine learning algorithms. The integration of AI in education raises important ethical concerns, including data privacy, algorithmic bias, and the digital divide. Addressing these challenges is crucial to ensuring that AI applications in education are fair, transparent, and equitable. This study will explore these ethical considerations and propose strategies to mitigate potential risks. This study explores the impact of Artificial Intelligence (AI) on the quality of higher education through a mixed-methods approach, combining a comprehensive literature review, quantitative data analysis, and qualitative insights. The research aims to evaluate how AI-driven technologies, including personalized learning systems, administrative automation, intelligent tutoring systems, and predictive analytics, enhance educational outcomes and address the diverse needs of students. Using the "Students Performance in Exams" dataset from Kaggle, machine learning models such as Linear Regression and Random Forest were applied to predict student performance, revealing significant predictors and demonstrating the efficacy of AI in educational contexts. The Random Forest model outperformed Linear Regression, underscoring the importance of non-linear approaches in capturing complex educational dynamics. Additionally, thematic analysis of case studies and expert interviews provided qualitative insights into the practical applications and challenges of AI in higher education. The findings indicate that AI technologies significantly contribute to personalized learning, administrative efficiency, and improved student engagement while highlighting ethical concerns related to data privacy and algorithmic bias. This research offers valuable implications for educators, administrators, policymakers, and technologists, advocating for the responsible and equitable integration of AI in higher education to enhance the overall quality of educational experiences and outcomes.