The Impact of Artificial Intelligence Implementation on Student Learning Patterns: Technology and Methods

The integration of Artificial Intelligence (AI) in higher education has resulted in significant shifts in how students learn, and institutions deliver instruction. This research conducts a Systematic Literature Review (SLR) to investigate the impact of AI implementation on the effectiveness and efficiency of student learning patterns. This research identified six key factors that support the effectiveness and efficiency of AI implementation in higher education based on recent empirical and theoretical works published between 2014 and 2025: AI Technology and Methods in Education, Personalization and Learning Experience, Automation and Efficiency in Teaching, Curriculum and Education Development, Teacher and Student Interaction in AI, and Ethical Challenges and Issues in AI. From a total of 1.460 records initially identified, after screening and eligibility assessment, 24 studies were finally included following the PRISMA guidelines. The proposed research model views AI Technology and Methods in Education as the core enabler that influence these key factors, collectively shaping student learning experiences. The findings indicate that AI enhances instructional quality via adaptive systems, personalized learning paths, and automated administrative tasks, while also fostering inclusive education and real-time feedback mechanisms. Nevertheless, the review also emphasizes the continuous problems with transparency, data privacy, and algorithmic fairness. This study provides an in-depth understanding of the benefits and drawbacks of Artificial Intelligence application in higher education, thus preparing ground for next AI-enhanced learning environments and policy development.