Artificial Intelligence Use to Empower the Implementation of OER and the UNESCO OER Recommendation
Bibliographic record
Abstract
Artificial intelligence (AI) has recently been gaining ground, particularly since November 2022, with the introduction of generative tools based on natural language processing and neural network algorithms. These kinds of tools have great potential for creators and users of Open Educational Resources (OER) and the Open Movement itself but they also represent risks. The International Council for Open and Distance Education OER Advocacy Committee (OERAC) developed two workshops to present the role of AI in OER at two international conferences in the fall of 2023. The workshops presented the features, benefits, key challenges, and practical issues related to using AI technologies from professional, ethical, sustainable, and equitable perspectives, while also focusing on the five areas of the UNESCO OER Recommendation. Participants were dynamically engaged in discussions, and documented their ideas in formats that could be used as OER in themselves. The OERAC noted and categorized the results, and developed short summaries and drafts for further work. Finally, drawing on the findings from the workshops, we asked ChatPDF for a second opinion on further suggestions for AI in connection with OER, which in turn related to the five areas of the recommendation. We conclude that, while there is great potential for the use of AI in the context of the Open Movement, there is also a need for professional ethics, equity, and sustainable capacity building, access, inclusion, policy, models, and international collaboration.
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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 | 0.037 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".