A Bibliometric Analysis of the Papers Published in the Journal of Artificial Intelligence in Education from 2015-2019
Bibliographic record
Abstract
To analyze the current research status and trends of the artificial intelligence in education field, we applied bibliometric methods to examine the articles published in one of the representative journals of the field, <em>International Journal of Artificial Intelligence in Education, </em>from 2015 to 2019. We analyzed 135 articles retrieved from the Web of Science database and examined prolific countries, collaboration networks, prolific authors, keywords, and the citations the articles received. Through examining keywords, we found that the authors largely focused on students and learning. Through examining prolific authors and countries, we found active publication of corresponding authors from United States, United Kingdom, Canada, and Germany. We found international collaboration among some researchers and institutions, such as strong collaboration network between United States and Canada. We suggest reinforcement in building more widespread international partnership and expanding collaboration network by including diverse institutions. International collaboration and expanded institutional network can improve research by incorporating various perspectives and expertise.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.197 | 0.218 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".