Bibliometric Analysis of Artificial Intelligence in STEM Education
Bibliographic record
Abstract
This study conducts a bibliometric analysis of artificial intelligence (AI) in STEM education research from 2020 to 2024. The study uses citation analysis to examine publication trends, country contributions, top authors, cited journals, and influential articles in this field. Data was collected from the Dimensions database using the keywords "artificial intelligence" AND "stem education." The analysis reveals a significant increase in publications and citations in 2024 compared to previous years. The United States emerges as the leading country in the number of documents (9) and citations (103). China follows with five documents but no citations. The most cited authors include Nesra Yannier, Kenneth R. Koedinger, and Scott E. Hudson, each with 55 citations. The International Journal of Artificial Intelligence in Education is the most cited journal, with 55 citations. The most influential article, "Active Learning is About More Than Hands-On: A Mixed-Reality AI System to Support STEM Education," received 55 citations. Carnegie Mellon University stands out as the most cited institution, with 55 citations. The findings highlight the growing importance of AI in STEM education research, focusing on personalized learning, advanced analytics, and instructional automation, inspiring us all with the potential of AI to transform the future of education. This bibliometric analysis provides valuable insights for researchers, educators, and policymakers interested in the intersection of AI and STEM.
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.142 | 0.205 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".