A bibliometric analysis of pedagogical approaches and AI-integrated interactive tools for supporting students under academic risk: Toward sustainable educational practices - Jayashree, Rajendran; Shanmugam, Ms. Kavitha; Alsiyabi, Ms. Hanaa Said Nasser (Edelweiss Applied Science and Technology, 2026)
This study aims to map pedagogical strategies integrated with Artificial Intelligence (AI) that support students at academic risk by enhancing academic performance through interactive and personalized learning experiences, while identifying influential authors, journals, and emerging research themes. A bibliometric analysis of 500 peer-reviewed articles was conducted using R programming software. The analysis employed country-level productivity assessment, citation trend analysis, keyword co-occurrence, word cloud visualization, and thematic evolution mapping to ensure robust and reliable insights. The results reveal a rapidly growing research domain, with a notable surge in publications in 2024 and high average citation impact for studies published in 2023. Leading journals include Heliyon, Frontiers in Psychology, and BMC Medical Education. Influential contributions are led by Zhang et al., while China emerges as the most productive country. Predominant themes emphasize human-centered and educational perspectives, with frequent keywords such as “students,” “learning,” and “humans,” alongside emerging empirical and application-oriented trends.This study provides the first comprehensive bibliometric overview of AI-integrated pedagogical learning approaches, highlighting the field’s intellectual structure and evolution. The findings offer guidance for educators, researchers, and policymakers in designing AI-driven, sustainable pedagogical strategies tailored to support academically at-risk students.