ARTIFICIAL INTELLIGENCE IN MICRO-CREDENTIALS FOR OPEN AND DISTANCE LEARNING: A TECHNOLOGICALLY ENHANCED SYSTEMATIC REVIEW
Bibliographic record
Abstract
Micro-credentials are increasingly meeting the growing need for a flexible workforce and are particularly well suited to the open and distance learning (ODL) environment. While artificial intelligence (AI) is driving educational advancements, its integration into micro-credentials for ODL remains fragmented, hindering further growth. This research seeks to address this issue through a systematic review using specialised software to comprehensively analyse the relevant literature. Employing a hybrid approach that combines bibliometric and thematic analyses, this study examines 46 articles on AI’s role in micro-credentials within ODL contexts. This review identifies significant research gaps and offers suggestions for future directions in this emerging field. The findings provide valuable, updated insights with theoretical and practical implications. For industry practitioners, the review serves as a comprehensive resource, helping to bridge the gap between academic research and real-world applications. Ultimately, this study contributes to the existing body of knowledge and paves the way for further exploration of AI integration into micro-credentials in ODL, thus offering new opportunities for future research and development.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".