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Artificial Intelligence-Driven Teaching Methods for Enhancing Higher Quality Education: A Bibliometric Analysis

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Title: Artificial Intelligence-Driven Teaching Methods for Enhancing Higher Quality Education: A Bibliometric Analysis
Language: English
Authors: Laurence C. Espino (ORCID 0000-0002-1069-0223), Ronilo P. Antonio (ORCID 0000-0002-2832-7203), R. S. Wilson Constantino (ORCID 0000-0001-6493-1902), Camille L. Espino (ORCID 0009-0005-7208-5108), Walton Wider (ORCID 0000-0002-0369-4082)
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
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  Data: Artificial Intelligence-Driven Teaching Methods for Enhancing Higher Quality Education: A Bibliometric Analysis
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  Data: <searchLink fieldCode="AR" term="%22Laurence+C%2E+Espino%22">Laurence C. Espino</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1069-0223">0000-0002-1069-0223</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ronilo+P%2E+Antonio%22">Ronilo P. Antonio</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2832-7203">0000-0002-2832-7203</externalLink>)<br /><searchLink fieldCode="AR" term="%22R%2E+S%2E+Wilson+Constantino%22">R. S. Wilson Constantino</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6493-1902">0000-0001-6493-1902</externalLink>)<br /><searchLink fieldCode="AR" term="%22Camille+L%2E+Espino%22">Camille L. Espino</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-7208-5108">0009-0005-7208-5108</externalLink>)<br /><searchLink fieldCode="AR" term="%22Walton+Wider%22">Walton Wider</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0369-4082">0000-0002-0369-4082</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Technology+in+Education%22"><i>International Journal of Technology in Education</i></searchLink>. 2026 9(1):153-167.
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  Data: 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
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  Data: 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.
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      – Text: English
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        PageCount: 15
        StartPage: 153
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      – SubjectFull: Literature Reviews
        Type: general
      – SubjectFull: Bibliometrics
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Teaching Methods
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      – SubjectFull: Higher Education
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      – SubjectFull: Trend Analysis
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      – SubjectFull: Decision Making
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      – SubjectFull: Educational Innovation
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