The Effect of AI (Artificial Intelligence) in Education on Student Motivation: A Systematic Literature Review

The lack of student learning motivation has become a critical issue in higher education, particularly in the digital era marked by rapid technological advancement. This study aims to analyze the contribution of artificial intelligence (AI) implementation in enhancing students’ learning motivation through a Systematic Literature Review (SLR) approach. The review was conducted by systematically screening scholarly literature in the Google Scholar database through several selection stages. The initial search using the keyword "Artificial Intelligence" yielded 4,320 documents, which were narrowed down to 749 with the addition of the keyword "Learning Motivation." Further refinement using the keyword "Students" reduced the results to 533 documents, and limiting the publication years to 2020–2024 resulted in 491 documents. The final selection produced 8 relevant articles for in-depth analysis. The data were analyzed qualitatively through thematic synthesis of findings across the selected studies. The results indicate that AI applications, such as educational chatbots and adaptive learning systems, significantly contribute to facilitating more personalized learning and fostering students’ intrinsic motivation. However, challenges such as limited technological access, resistance to change, and concerns regarding data privacy and ethics remain critical barriers. This study concludes that the development of AI in education must emphasize inclusivity, personalization, and the alignment with learners’ needs to ensure more effective and sustainable learning in the digital age.