Artificial Intelligence-Driven Teaching Methods for Enhancing Higher Quality Education: A Bibliometric Analysis
DOI:
https://doi.org/10.46328/ijte.5114Keywords:
adaptive learning, artificial intelligence, bibliometric analysis, teaching methods, quality educationAbstract
This study explores artificial intelligence (AI)-driven teaching methods and their potential to enhance higher education. It addresses critical gaps concerning ethical governance, personalization, and educator preparedness amid rapid technological changes. Through bibliometric analysis, this study examined 424 peer-reviewed journal articles published up to March 20, 2025, in the Scopus database. It uses co-citation and co-word analyses to map key publications, research themes, and conceptual trends, thereby offering a macro-level understanding of AI in higher education. The analysis identified three core research clusters: ethical integration and academic integrity; AI-enabled personalization and engagement; and pedagogical transformation. Although tools such as the ChatGPT and intelligent tutoring systems promote personalized learning and instant feedback, concerns regarding data privacy, digital inequality, and automation reliance remain. Co-word analysis has revealed growing interest in immersive learning, adaptive systems, and AI-enhanced pedagogy. Co-citation trends emphasize institutional reforms and faculty preparedness. This study offers a comprehensive bibliometric synthesis of AI in higher education by combining multiple analytical techniques. It highlights underexplored areas, such as human-centered approaches, long-term impacts, and cross-cultural applications, offering directions for future inquiry and innovation.
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