Artificial Intelligence in Online Education: A Systematic Review of Its Impact on Learner Engagement and Satisfaction

Synthesis: This systematic literature review (30 peer-reviewed studies, 2020–2025) examines how AI technologies—chatbots, intelligent tutoring systems (ITS), sentiment analysis, gaze tracking, and predictive analytics—support learner engagement and satisfaction in higher education online courses across cognitive, emotional, behavioral, and social dimensions. Guided by three research questions, the review finds that adaptive learning, real-time Feedback, and emotion-aware systems contribute positively to personalized learning and Motivation, while persistent concerns around data privacy, algorithmic bias, over-reliance on automation, and system usability temper these gains. Experimental and quasi-experimental designs, machine learning, mixed methods, and surveys dominate the methodological landscape.

Key Findings

Personalization drives engagement: AI-driven adaptive pathways and personalized feedback (e.g., ChatGPT, ITS with fuzzy rule-based personalization, ALEKS, Squirrel AI, AISAR) tailor content and pacing to individual needs, increasing satisfaction and intrinsic Motivation across learner populations.
Emotional and cognitive support: Emotion-aware systems, gaze tracking with AI-generated summaries, and predictive analytics enable real-time attention monitoring and early intervention for at-risk learners, supporting motivation and emotional well-being.
Chatbots and AI companions: 24/7 conversational support via chatbots, intelligent tutoring, and AI-as-mentor/companion improves accessibility, reduces frustration, and strengthens learner–instructor/community connection.
Key challenges persist: Data privacy and Ethics, algorithmic bias and fairness, over-reliance on automation, AI accuracy and hallucination, and usability/interface limitations (e.g., ChatGPT response times) can undermine satisfaction and trust.
Institutional and cost barriers: Financing, cost of deployment, and learner motivation/learning-impact concerns temper widespread adoption of AI-enhanced systems in higher education.
Methodological dominance: Experimental/quasi-experimental designs, machine learning approaches, mixed-methods, and survey-based research are the most common ways the field assesses AI's impact.

Study Design & Method

This PRISMA-based systematic literature review searched Scopus and Web of Science (May 2025) using a structured keyword protocol combining online/distance learning, higher education, AI, and learner satisfaction/engagement terms. Following four phases (identification, screening, eligibility, inclusion), 30 peer-reviewed studies (2020–2025) were retained after strict inclusion/exclusion criteria and full-text review. Data extraction coded positive factors (RQ1), challenges (RQ2), and research methods (RQ3), with thematic categorization conducted iteratively by all three authors. Quality was appraised using the Mixed Methods Appraisal Tool (MMAT) 2018, operationalized into context-specific indicators scored 0–5.

Implications for AI in Education

The review grounds a foundation for designing future AI-enhanced learning management systems aimed at boosting engagement across cognitive, emotional, behavioral, and social domains. Designers and policymakers should pair personalization, real-time feedback, and emotion-aware features with robust privacy protections, bias mitigation, and intuitive user interfaces. The persistent challenge set—ethics, over-reliance, bias, accuracy, and cost—signals that AI's benefits are only realized when technological affordances are matched by careful pedagogical integration and institutional support.

Connected Concepts

Connected Articles

Citation

Katalinic, A., Slavuj, V., & Jaksic, D. (2026). Artificial Intelligence in Online Education: A Systematic Review of Its Impact on Learner Engagement and Satisfaction. Education Sciences, 16(3), 389.