Enhancing curriculum enactment: Leveraging <scp>ChatGPT</scp> to design a spelling programme based on the British Columbia standards
Bibliographic record
Abstract
Abstract Debate exists about the role and value of teaching spelling in the middle years of schooling. The increasing use of assistive technology in schools, has prompted questions about the time devoted to teaching spelling. Yet spelling and writing continue to be the means through which students are assessed as they move through school. In their study of teachers' approaches to teaching spelling in British schools, Esposito et al. (2022) found that many teachers lack confidence in this area, often developing their own resources or relying on commercially produced materials with limited evidence of their effectiveness. Teachers are increasingly exploring the use of generative Artificial Intelligence tools, such as ChatGPT, to support the creation of spelling resources that are tailored to their learners and grounded in established literacy principles. When used thoughtfully, these tools can help teachers design differentiated materials that integrate the core components of effective spelling instruction known to support broader literacy 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".