GRAPPA 2024: Innovations in Education Workshop
Bibliographic record
Abstract
At the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting and trainee symposium, an Innovations in Education workshop aimed to explore the skills necessary to design learning objectives, optimize artificial intelligence (AI) use in education, and employ interactive teaching methods. Learning objectives are crucial for structuring educational programs and enhancing learner engagement and performance. Although incorporating AI into education promises untapped potential, it requires careful implementation to avoid introducing inaccuracies and biases. Effective teaching techniques, such as the Know-Feel-Do principle, emphasize the importance of understanding audience needs, creating engaging content, and fostering interactive learning environments. With the use of real-world examples in the rheumatology field, the workshop illustrated the diversity and impact of innovative educational practices that can help educators deliver an outstanding learning experience. This paper will cover the key points from this workshop.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".