AI and Transformative Learning in Higher Education: A Systematic Literature Review and Bibliometric Insights

This study comprehensively maps the development and trends in AI and transformative learning research in higher education from 2019-2025. Using a systematic literature review and bibliometric analysis, it answers six key questions to explore the evolution of AI integration in transformative learning. Analyzing 181 Scopus-indexed articles, the study utilizes R-studio and VOSviewer software, following the PRISMA method to assess author collaborations, theme evolution, and publication distribution. The results show a significant increase in publications on AI and transformative learning. Initially focused on general AI applications in education, research has shifted toward more specific themes like generative AI, personalized learning, and ChatGPT. Despite technological innovation, pedagogical studies on transformative learning, such as active and personalised learning, remain underexplored in AI contexts. The research also reveals that countries like India and Indonesia dominate the field, indicating regional research concentration. While AI shows potential to improve student motivation, writing skills, and personalized learning, challenges such as ethical concerns, digital literacy, and socio-cultural sensitivity persist, especially regarding academic integrity and AI dependence, which may reduce critical thinking and metacognitive reflection essential for transformative learning. This study affirms that AI must be integrated with a human-centered approach to support both learning effectiveness and critical reflection. Thus, the development of ethical frameworks, educator training, and international collaboration is crucial for the sustainable and inclusive implementation of AI in higher education. In conclusion, while AI offers significant potential for enhancing transformative learning, its successful integration into higher education requires careful consideration of ethical, pedagogical, and socio-cultural dimensions to ensure its responsible and impactful application.

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Publication Details

Journal
Journal of Teaching and Learning
Published
2025-10-22
DOI
https://doi.org/10.22329/jtl.v19i4.10096
Citations
2
Primary Topic
Engineering Education and Technology
Type
article
Field-Weighted Citation Impact
6.49
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article

AI and Transformative Learning in Higher Education: A Systematic Literature Review and Bibliometric Insights

Anderias Henukh, Asep Irvan Irvani, Agus Setiawan, Naveed Iqbal Raja et al.
2 citations
Journal of Teaching and Learning
Engineering Education and Technology
6.49
article

AI and Transformative Learning in Higher Education: A Systematic Literature Review and Bibliometric Insights

Anderias Henukh, Asep Irvan Irvani, Agus Setiawan, Naveed Iqbal Raja, Emsi Magdalena Seme
article en
2 citations

Abstract

This study comprehensively maps the development and trends in AI and transformative learning research in higher education from 2019-2025. Using a systematic literature review and bibliometric analysis, it answers six key questions to explore the evolution of AI integration in transformative learning. Analyzing 181 Scopus-indexed articles, the study utilizes R-studio and VOSviewer software, following the PRISMA method to assess author collaborations, theme evolution, and publication distribution. The results show a significant increase in publications on AI and transformative learning. Initially focused on general AI applications in education, research has shifted toward more specific themes like generative AI, personalized learning, and ChatGPT. Despite technological innovation, pedagogical studies on transformative learning, such as active and personalised learning, remain underexplored in AI contexts. The research also reveals that countries like India and Indonesia dominate the field, indicating regional research concentration. While AI shows potential to improve student motivation, writing skills, and personalized learning, challenges such as ethical concerns, digital literacy, and socio-cultural sensitivity persist, especially regarding academic integrity and AI dependence, which may reduce critical thinking and metacognitive reflection essential for transformative learning. This study affirms that AI must be integrated with a human-centered approach to support both learning effectiveness and critical reflection. Thus, the development of ethical frameworks, educator training, and international collaboration is crucial for the sustainable and inclusive implementation of AI in higher education. In conclusion, while AI offers significant potential for enhancing transformative learning, its successful integration into higher education requires careful consideration of ethical, pedagogical, and socio-cultural dimensions to ensure its responsible and impactful application.

Journal of Teaching and LearningVol. 19(4)
Indonesia University of Education (ID), Universitas Garut (ID)
Openalex Percentile: Top 3%
Engineering Education and Technology
6.49
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