AI in Online Education.

Abstract

The integration of artificial intelligence (AI) into online education has changed the digital learning environment, providing new opportunities to improve learner satisfaction and engagement. This systematic literature review, covering a five-year period from 2020 to 2025, investigates how AI technologies, including chatbots, intelligent tutoring systems (ITS), sentiment analysis, gaze tracking, and predictive analytics, facilitate learner engagement across cognitive, emotional, behavioral, and social dimensions. Drawing from 30 peer-reviewed studies, this review addresses three main research questions: (1) Which aspects of AI positively affect learner satisfaction and engagement in online courses within higher education institutions; (2) What potential challenges may arise from using these technologies; and (3) Which research methods are most frequently employed to evaluate AI's impact in such learning contexts? The results emphasize that adaptive learning, real-time feedback, and emotion-aware systems positively contribute to personalized learning and motivation. Nevertheless, issues remain regarding data privacy, algorithmic bias, over-reliance on automation, and system usability. Experimental and quasi-experimental designs, along with machine learning, mixed methods, and survey-based approaches, are found to dominate the reviewed studies. Based on these insights, this work provides a foundation for future AI-enhanced learning management systems aimed primarily at improving learner engagement across cognitive, emotional, behavioral, and social domains.