When Can Text Embeddings Replace Item Calibration? A Geometric Diagnostic for Semantic Loadings in Multidimensional Adaptive Testing
Multidimensional item response theory relies on calibrated item parameters, such as discrimination and category threshold values, which are usually estimated from large samples of human test responses. This study investigates whether the directional loadings of these parameters can be recovered directly from item text using pre-trained sentence embeddings, avoiding the need for initial item calibration. Using the open-source IPIP Big-Five dataset ($n=19{,}719$; 50 items), we built a multidimensional computerized adaptive testing (CAT) simulation using D-optimal item selection. We compared three item loading sources: fitted graded response model parameters, semantic text embeddings, and a lexical baseline. In simulation, semantic embeddings recovered latent trait profiles almost as accurately as fitted parameters (correlation $0.825$ vs. $0.857$), performing noticeably better than simple word overlap ($0.752$). However, the embedding-based model produced inflated posterior variance, showing nearly four times higher measurement uncertainty despite accurate point estimates. We attribute this to collinearity across dimensions, as embedding-derived loadings pointed in similar directions across traits (condition number $137$ vs. $1.0$; mean trait cosine $0.90$). This outcome reflects the shared vocabulary common in personality items. We propose a simple diagnostic metric based on the loading matrix condition number to evaluate whether an item bank is suitable for text-derived loadings prior to testing.