Bibliometric Analysis of Artificial Intelligence for Digital Literacy
Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis of Artificial Intelligence (AI) for digital literacy research from 2015 to 2024, utilizing citation analysis on 223 articles from the Dimensions database. The research examines publication trends, author collaborations, and thematic focuses in the field. Findings reveal a significant publication growth, with a peak in 2020 followed by fluctuations in subsequent years. The analysis highlights the field’s interdisciplinary nature, with computer science and education emerging as dominant areas. Key research themes identified through co-word analysis include educational applications, technological integration, and broader societal impacts. The emergence of “AI literacy” as a significant keyword underscores the evolving nature of digital competence in the AI era. Author collaboration networks reveal established experts and researchers with strong collaborative ties, indicating a dynamic research community. The study identifies challenges and opportunities in AI for digital literacy, including ethical considerations, teacher preparation, and the potential for personalized learning. This analysis provides valuable insights into AI’s current state and future directions for digital literacy research, emphasizing the need for continued interdisciplinary collaboration and the development of inclusive AI literacy programs. The findings suggest a growing recognition of AI’s role in shaping digital literacy practices and the importance of preparing learners for an AI-integrated future.
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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 | 0.008 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.166 | 0.215 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".