Bibliometric Mapping of Teachers’ Roles in Artificial Intelligence: A Visualization Study
Bibliographic record
Abstract
This bibliometric study examines the evolving research landscape on teachers’ roles in AI-enhanced education from 2020 to 2024. Using the PRISMA 2020 guidelines, we analyzed 54 relevant publications using VOSviewer and Scimago Graphica tools. The study reveals a significant publication surge, peaking at 25 in 2023, indicating growing academic interest in this field. China, the United States, and South Korea emerge as leading contributors, highlighting the global nature of this research area. Keyword analysis emphasizes the centrality of “artificial intelligence” and “teacher,” reflecting the focus on AI’s impact on educators’ roles. The prominence of “integration” and “transformation” suggests a shift in perceiving AI as a supplementary tool to a transformative force in education. Citation network analysis reveals influential works shaping the field, with recent publications garnering significant attention. The study also highlights strong international collaborations, particularly among institutions in China and the United States. Key themes identified include the integration of AI in teaching practices, transforming teachers’ roles, and AI’s potential for personalized learning. This comprehensive analysis provides valuable insights for educators, policymakers, and researchers navigating the future of AI-enhanced education, emphasizing the need for ongoing research, international collaboration, and interdisciplinary approaches to optimize the role of teachers in this rapidly evolving field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.079 | 0.144 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".