Eight AI-driven factors positively influence learner satisfaction and engagement in online higher education¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-19 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? The review identifies eight major AI-driven factors central to learner satisfaction and engagement, including personalized learning, engagement tracking, feedback systems, chatbot support, predictive analytics, teamwork support, AI tutoring, and ease of use. → Ana Katalinic 2026

Evidence¶

Ana Katalinic 2026¶

Ana Katalinic, Vanja Slavuj, Danijela Jaksic. (2026). Artificial Intelligence in Online Education: A Systematic Review of Its Impact on Learner Engagement and Satisfaction. Educ. Sci. 2026, 16, 389. https://doi.org/10.3390/educsci16030389

q2 · i? · review · r2

Systematic review of 30 studies (2020–2025) identified via PRISMA screening of Scopus and WoS. The review's thematic coding of included studies found that "eight major AI-driven factors" shape satisfaction and engagement, listed as personalized pathways, engagement tracking, feedback and assessment, chatbot support, predictive analytics, teamwork, tutoring, and intuitive interfaces.

"Based on the analysis of the selected publications, eight major AI-driven factors can be identified as central to having effect on learner satisfaction and engagement in digital learning environments."

Discussion¶

Related Claims¶

• AI-driven personalized and adaptive learning pathways support learner engagement and satisfaction in online courses — related
• Chatbot support systems in online learning are associated with higher learner satisfaction and engagement when perceived as useful — a narrower finding that bears on this claim
• AI integration is associated with improvements in teaching, learning, and administrative efficiency in higher education when supported by adequate infrastructure — related
• AI in online learning offers personalization, predictive analytics, and potential reduction of instructor time on tasks — a narrower finding that bears on this claim
• Interest in AI for feedback in higher education is driven by workload reduction and feedback timeliness amid massification — related
• Pedagogical considerations and workload reduction potential are additional factors influencing teachers' acceptance of AI-EdTech, beyond trust — related
• BERTopic analysis of 1,048 AIOL publications identifies research themes including adaptive learning systems, sentiment analysis, and predictive analytics — related
• Learners in fully online courses reported positive perceived usefulness and perceived ease of use of both a goal-setting chatbot and a learning buddy chatbot — a narrower finding that bears on this claim
• Adaptive learning systems report academic performance gains of 15–25% and engagement increases of up to 40% — related
• Pilot studies in computing-related majors report measurable improvements in student engagement, self-efficacy, and learning outcomes — related
• AI tools such as chatbots, intelligent tutoring systems, and early alert systems are identified as having potential to serve marginalized students, contingent on ethical governance and infrastructure — related
• Teachers have far more power to shape what happens to students than investors or scouts, partly through their opinions of each child — related
• Chatbot support for goal setting and social presence in fully online activities was evaluated through behavioral engagement from conversation records plus perceptions and interviews — a narrower finding that bears on this claim