Using AI to boost evidence-based teaching and learning: A collaborative approach across a network of schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it