TRENDS AND DEVELOPMENTS IN INTELLIGENT AGENT FOR SOCIALLY SHARED REGULATED LEARNING (SSRL): A BIBLIOMETRIC REVIEW
Bibliographic record
Abstract
The integration of intelligent agents in supporting Socially Shared Regulation of Learning (SSRL) has gained significant scholarly interest in recent years, aligning with the broader evolution of Artificial Intelligence (AI) in education. Despite its growing relevance, a comprehensive understanding of research trends, influential contributions, and thematic developments within this niche remains limited. This study aims to address that gap by conducting a bibliometric analysis titled “Trends and Developments in Intelligent Agent for Socially Shared Regulated Learning (SSRL),” using a dataset of 1951 publications retrieved from the Scopus database. Employing Scopus Analyzer, OpenRefine, and VOSviewer software, we systematically examined publication output, citation patterns, country contributions, author impact, keyword co-occurrences, and collaboration networks spanning from 1983 to 2025. The analysis revealed a sharp increase in publication volume post-2010, with peak activity occurring between 2010 and 2023, driven predominantly by contributions from the United States, the United Kingdom, China, and Canada. Keyword clustering highlighted dominant themes, including metacognitive support, agent-based learning systems, collaborative regulation, and AI-driven feedback mechanisms. Furthermore, the co-authorship and institutional analysis demonstrated an emerging but fragmented research community, suggesting opportunities for enhanced international collaboration. The findings map the intellectual landscape of intelligent agents in SSRL and provide actionable insights for future research. It highlights areas with high potential for interdisciplinary integration and technological innovation. This study provides a foundational perspective for researchers, policymakers, and developers seeking to leverage intelligent agents in fostering effective collaborative learning environments.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.117 | 0.162 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".