Artificial Intelligence And Student Engagement In Online Learning : A Literature Review

Document Type

Article

Publication Date

1-1-2026

School

Leadership

Abstract

Student engagement is crucial for success in online learning but challenging to sustain due to the high demands for autonomy and self-regulation. Advances in artificial intelligence (AI) offer solutions through personalized learning, real-time feedback, and adaptive content. This study systematically reviews 24 studies from the Web of Science database to explore AI’s role in enhancing engagement. Six key applications emerged: 1) chatbots for course design, 2) emotion, facial, voice recognition, and eye tracking, 3) machine learning for data analysis, 4) teacher-student interaction support, 5) personalized feedback and recommendations, and 6) AI-powered bots in smart learning environments. Common data sources include video recordings, activity logs, standardized datasets, and surveys. Engagement is typically measured through multi-method approaches combining surveys, AI recognition, and coded activity data. Findings show that integrating diverse AI tools and data sources provides more accurate, real-time insights into cognitive, emotional, and behavioral engagement. Limitations include the focus on peer-reviewed studies from a single database, the lack of distinction between asynchronous and synchronous online learning, and the exclusion of broader AI applications beyond student engagement. Despite these limitations, this review offers strategies for designing effective AI systems, improving engagement measurement, and creating interactive, personalized learning experiences. These insights guide researchers, educators, and practitioners seeking to leverage AI to foster student engagement in online education.

Publication Title

American Journal of Distance Education

Volume

40

Issue

1

First Page

8

Last Page

30

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