Artificial Intelligence-Driven Teaching Methods for Enhancing Higher Quality Education: A Bibliometric Analysis
Saved in:
| Title: | Artificial Intelligence-Driven Teaching Methods for Enhancing Higher Quality Education: A Bibliometric Analysis |
|---|---|
| Language: | English |
| Authors: | Laurence C. Espino (ORCID |
| Source: | International Journal of Technology in Education. 2026 9(1):153-167. |
| Availability: | International Society for Technology, Education, and Science. ISTES Organization, Monument, CO 80132. e-mail: istesorganization@gmail.com; e-mail: ijteoffice@gmail.com; Web site: https://www.ijte.net/index.php/ijte/about |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Information Analyses |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Literature Reviews, Bibliometrics, Artificial Intelligence, Teaching Methods, Higher Education, Trend Analysis, Educational Research, Educational Trends, Citation Analysis, Intelligent Tutoring Systems, Technology Integration, Learning Management Systems, Decision Making, Educational Innovation |
| ISSN: | 2689-2758 |
| Abstract: | 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 cocitation 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1494475 |
| Database: | ERIC |
| Abstract: | 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 cocitation 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. |
|---|---|
| ISSN: | 2689-2758 |
