Can automated item generation be used to develop high quality MCQs that assess application of knowledge?
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
Abstract The purpose of this study was to compare the quality of multiple choice questions (MCQs) developed using automated item generation (AIG) versus traditional methods, as judged by a panel of experts. The quality of MCQs developed using two methods (i.e., AIG or traditional) was evaluated by a panel of content experts in a blinded study. Participants rated a total of 102 MCQs using six quality metrics and made a judgment regarding whether or not each item tested recall or application of knowledge. A Wilcoxon two-sample test evaluated differences in each of the six quality metrics rating scales as well as an overall cognitive domain judgment. No significant differences were found in terms of item quality or cognitive domain assessed when comparing the two item development methods. The vast majority of items (> 90%) developed using both methods were deemed to be assessing higher-order skills. When compared to traditionally developed items, MCQs developed using AIG demonstrated comparable quality. Both modalities can produce items that assess higher-order cognitive skills.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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