AI and Transformative Learning in Higher Education: A Systematic Literature Review and Bibliometric Insights
Bibliographic record
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.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.133 | 0.121 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".