A Bibliometric Analysis of Artificial Intelligence for Multimedia in Education by Dimensions AI
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Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis of Artificial Intelligence (AI) research for Multimedia in Education from 2020 to 2024. Using the Dimensions AI database, VOSviewer software and Scimago Graphica, we examined 45 publications to identify key trends, influential contributors, and emerging directions in this rapidly evolving field. The analysis reveals a significant publication surge from 2020 to 2021, followed by stabilization in subsequent years. China is the dominant contributor, with 19 publications and 214 citations, highlighting its leadership in AI and educational technology research. Co-authorship network analysis shows a tightly interconnected research community lacking distinct clusters. The most cited papers focus on student engagement and specific AI applications in education, indicating the field's emphasis on practical implementations. Keyword analysis reveals a consistent focus on core concepts such as artificial intelligence, education, technology, and learning, with a recent shift towards more user-centered research. The study also identifies challenges in implementing AI for multimedia in education, including data privacy concerns, ethical considerations, and the need for educator training. These findings provide valuable insights for researchers, educators, and policymakers, highlighting the need to balance technological advancements with pedagogical needs and ethical considerations. Future research directions include investigating the long-term impact of AI-enhanced multimedia education, developing ethical frameworks, conducting cross-cultural studies, and enhancing AI's capability to provide personalized learning experiences through multimedia content.
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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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.039 | 0.106 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it