International Journal of Science, Engineering and TechnologyAuthors: Tavhare Pranjal Pravin, Prof. S. S. Bhor, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Student engagement is closely linked to achievement, retention and the quality of learning, yet teachers in large physical classrooms and online courses cannot easily monitor every learner in real time. This paper reviews AI-driven student engagement systems for classroom and online settings. It begins with the educational view of engagement as behavioural, emotional and cognitive, and then examines automatic engagement recognition from facial expressions and head pose, datasets of learners recorded in the wild, the dynamics of affective states such as confusion and boredom during learning, and learning analytics from activity logs, attendance and quiz performance, including studies of disengagement patterns in massive open online courses and knowledge tracing. Machine learning and deep learning methods such as CNNs, random forests and SVMs for classifying engagement as high, moderate or low are compared using accuracy, precision and recall. An adaptive recommendation module that suggests content and interventions and a teacher dashboard are discussed. A five-layer architecture comprising multimodal data, feature extraction, engagement classification, adaptive intervention and teacher analytics layers is proposed, together with research gaps, objectives, a comparison of data modalities and challenges such as privacy, consent, bias and scalability. The review concludes that ethical, teacher-centred AI can support more inclusive and student-centred education.
DOI: https://doi.org/10.5281/zenodo.24216799
