Establishing Cognitive Item Models for Fair and Theory-Grounded Automatic Item Generation: A Large-Scale Assessment Study with Image-Based Math Items
Bibliographic record
Abstract
Mathematics is a core domain in large-scale assessments (LSA), yet item development remains resource-intensive, limiting scalability and innovation. Automatic Item Generation (AIG) offers a promising solution, but empirical validations remain rare. This study investigates the psychometric functioning and fairness of 48 cognitive item models designed to generate language-reduced, image-based math items for Grades 1, 3, and 5. Treating these models as proto-theories, we generated 612 item instances varying in cognitive demands and contextual features. Using data from Luxembourg’s school monitoring (N = 35,058), we found that item difficulty was mainly driven by predefined cognitive factors, with stronger contextual influences in early grades. We introduce Differential Radical Functioning to evaluate whether AIG-based items permit comparable score interpretations across subgroups. Results reveal meaningful differences by cultural background, regardless of language proficiency. These findings highlight the importance of contextual embedding and demonstrate the potential of cognitive modeling in AIG for scalable, valid, and equitable assessments.
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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 | 0.051 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".