Using AI to boost evidence-based teaching and learning: A collaborative approach across a network of schools
Bibliographic record
Abstract
The job of delivering curriculum through engaging and effective learning for every student makes teaching a challenging and rewarding profession. This article presents a case study of how collaborative professional development sessions in the Australian state of New South Wales (NSW) have upskilled teachers in the use of generative artificial intelligence (GenAI) to enhance teaching practice. The case study shows that when teachers are properly trained in the effective use of GenAI tools like ChatGPT, they can be supported in spending more time delivering best-practice teaching, backed up by the growing evidence-base behind the science of learning. The case study also provides a blueprint for how other education systems can support teachers to develop these skills, enabling them to adapt to and navigate future technological changes with confidence. The article concludes with an overview of the NSW Department of Education’s recently released GenAI tool, NSWEduChat.
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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 | 0.096 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".