ChatGPT Revolution in Education: Trends, Opportunities, and Research Hot Spots—A Bibliometric Perspective
Bibliographic record
Abstract
Since its release in late 2022, ChatGPT has quickly become central to debates in educational technology, drawing widespread attention from academia and media. This study conducts a bibliometric analysis of ChatGPT in education using 447 publications sourced from the Web of Science. Tools such as VOSviewer, Biblioshiny, and CiteSpace were applied to examine citation patterns, author collaborations, journal contributions, and global research distribution. Results show an extraordinary surge in scholarship, with publications increasing by 467.16% between 2023 and 2024, involving over 1,200 authors worldwide. Prominent themes include academic integrity, personalized learning, and the integration of AI into teaching practices. Keyword analysis highlights “students,” “artificial intelligence,” and “education” as focal points of discussion. Leading institutions from Hong Kong, China, and the United States dominate contributions, underscoring the global nature of this trend. The study concludes by calling for ethical frameworks and inclusive strategies to ensure responsible and effective adoption.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.075 | 0.128 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".