Open Education and Generative AI: Toward an Ethical and Inclusive Transformation of Pedagogical Strategies
Bibliographic record
Abstract
Recent studies show that Generative Artificial Intelligence (GenAI) is profoundly reshaping educational paradigms, steering teaching systems toward open, inclusive, and context-sensitive practices. This study advances an integrative model combining Open Educational Resources (OER), Open Educational Practices (OEP), and GenAI to support an ethical and sustainable pedagogical transformation. A mixed-methods design was employed with 453 education-science stakeholders across 45 secondary, college, and university institutions in Cameroon and Chad. Quantitative results indicate a significant association between OER and equitable accessibility (β = 0.61, p < 0.01), between OEP and cognitive justice (β = 0.54, p < 0.01), and a positive effect of GenAI on personalization (β = 0.49, p < 0.01). The combined effect of the three dimensions strengthens pedagogical resilience (β = 0.65, p < 0.01). Qualitative evidence underscores the role of human mediation, locally collaborative governance, and contextual appropriation. Policy implications include inclusive governance of innovation, techno-ethical teacher training, and place-based anchoring of open education policies. The proposed model emerges as a strategic and tactical lever for pedagogical sustainability suited to the challenges of the hypermodern era.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".