Systematic Insights and Trends in AI-Based Student Engagement Detection

A Systematic Review and Bibliometric Analysis

Abstract

This systematic review critically examines the growing field of artificial intelligence (AI) applications in tracking student engagement and disengagement in educational settings. We synthesize current literature, employing bibliometric analysis to understand the complexities of technology-integrated teaching methods and their effectiveness in creating engaging learning environments. This study employs a rigorous methodological framework, incorporating the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model and the population, intervention, comparison, outcomes, and study design (PICOS) criteria to ensure a structured and comprehensive review. A systematic search strategy was implemented to identify relevant studies from authoritative academic databases. The research findings indicate a significant use of new datasets and virtual learning environments, particularly emphasizing higher education. Despite the promising advancements in AI-driven engagement detection, our analysis reveals critical research gaps, such as the lack of detailed demographic information, especially the age factor that greatly influences engagement behaviors. This absence highlights the need for more specific engagement detection tools suitable for different educational levels. Another key observation is the limited research on early education, a critical area where engagement is crucial yet subtly indicated. Considering these points, we offer recommendations for future research, calling for a comprehensive approach that includes detailed demographics, integration of various learning settings, ensuring broad technology access, improving multimodal techniques, and maintaining privacy and ethical standards. The study’s practical implications underscore the need for more adaptable, inclusive, and ethically responsible technological contributions to education, benefiting educators, policymakers, and AI developers.