AI and Transformative Learning in Higher Education: A Systematic Literature Review and Bibliometric Insights
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| Title: | AI and Transformative Learning in Higher Education: A Systematic Literature Review and Bibliometric Insights |
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| Language: | English |
| Authors: | Anderias Henukh, Asep Irvan Irvani, Agnus Setiawan, Emsi Magdalena Seme, Noca Riama Lumban Raja |
| Source: | Journal of Teaching and Learning. 2025 19(4):233-261. |
| Availability: | Journal of Teaching and Learning. 401 Sunset Ave. Faculty of Education, University of Windsor, Windsor, Ontario, Canada N9B 3P4. Tel: 519-253-3000 Ext. 4068; e-mail: jtl@uwindsor.ca; Web site: https://ojs.uwindsor.ca/index.php/JTL |
| Peer Reviewed: | Y |
| Page Count: | 29 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Information Analyses |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Literature Reviews, Meta Analysis, Bibliometrics, Artificial Intelligence, Transformative Learning, Higher Education, Educational Trends, Trend Analysis, Foreign Countries |
| ISSN: | 1492-1154 1911-8279 |
| Abstract: | This study comprehensively maps the development and trends in AI and transformative learning research in higher education from 2019 to 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 personalized 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. [Note: The page range (233-260) shown on the PDF is incorrect. The correct page range is 233-261.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1487550 |
| Database: | ERIC |
| Abstract: | This study comprehensively maps the development and trends in AI and transformative learning research in higher education from 2019 to 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 personalized 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. [Note: The page range (233-260) shown on the PDF is incorrect. The correct page range is 233-261.] |
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| ISSN: | 1492-1154 1911-8279 |
