A new ℓ1-regularized mixture asymmetric IRT framework jointly recovers latent classes for impact and selects DIF items without group labels or anchors, as shown in simulations and two educational datasets.
Marginal maximum likelihood estimation of item parameters: Application of an
2 Pith papers cite this work. Polarity classification is still indexing.
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A persona-induced latent variable model with LLM-generated priors enables scalable adaptive item selection with closed-form Bayesian updates for accurate user-specific predictions.
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Latent Impact and Differential Item Functioning Analysis for Asymmetric IRT Models
A new ℓ1-regularized mixture asymmetric IRT framework jointly recovers latent classes for impact and selects DIF items without group labels or anchors, as shown in simulations and two educational datasets.
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Adaptive Querying with AI Persona Priors
A persona-induced latent variable model with LLM-generated priors enables scalable adaptive item selection with closed-form Bayesian updates for accurate user-specific predictions.