Leveraging Generative AI to Elevate Curriculum Design and Pedagogy in Public Health and Health Promotion
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
Despite increased recognition of the importance and need for pedagogical training for public health and health promotion instructors in best-practices and inclusivity, formal training is often overlooked. This disregard for pedagogical training necessitates exploration of alternative and innovative approaches to enhance teaching and learning such as generative AI. This paper describes applied uses of generative AI, specifically ChatGPT, to enhance pedagogy in public health and health promotion education in the areas of curriculum design, instructional strategies, assessment and feedback, and diversity, equity, and inclusion. Generative AI as a supplemental tool shows immense promise for improving teaching and learning, however, inherent limitations and ethical considerations require caution and continued scrutiny.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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