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            <namePart type="given">Joshua</namePart>
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    <abstract>Test assembly, the process of constructing a complete test form from an item pool subject to blueprint constraints, has traditionally been treated as a static optimization problem. In AI-enabled assessment environments, however, item pools evolve continuously as newly generated items enter with uncertain psychometric parameters, and delivery is on demand. These conditions make test assembly a sequential decision-making problem under uncertainty: which form should be deployed now, given current but incomplete knowledge of item quality, to simultaneously maximize measurement precision, satisfy content-blueprint constraints, maintain pool sustainability, and accelerate calibration of uncertain new items? This paper proposes the Stochastic Constrained Hybrid (SCH) framework as a principled answer to this question. SCH recasts form-level assembly as a multi-armed bandit (MAB) problem with Fisher information as the reward, extending recent item-level approaches in computerized adaptive testing (CAT) to the form-level setting. A simulation study comparing six test assembly methods is also presented. The main contribution of this paper is a framework for incorporating items with uncertain parameters into the automatic test assembly process for linear test forms.</abstract>
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%0 Conference Proceedings
%T Stochastic Constrained Test Assembly for AI-Enabled Assessment Systems
%A von Davier, Alina A.
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F von-davier-2026-stochastic
%X Test assembly, the process of constructing a complete test form from an item pool subject to blueprint constraints, has traditionally been treated as a static optimization problem. In AI-enabled assessment environments, however, item pools evolve continuously as newly generated items enter with uncertain psychometric parameters, and delivery is on demand. These conditions make test assembly a sequential decision-making problem under uncertainty: which form should be deployed now, given current but incomplete knowledge of item quality, to simultaneously maximize measurement precision, satisfy content-blueprint constraints, maintain pool sustainability, and accelerate calibration of uncertain new items? This paper proposes the Stochastic Constrained Hybrid (SCH) framework as a principled answer to this question. SCH recasts form-level assembly as a multi-armed bandit (MAB) problem with Fisher information as the reward, extending recent item-level approaches in computerized adaptive testing (CAT) to the form-level setting. A simulation study comparing six test assembly methods is also presented. The main contribution of this paper is a framework for incorporating items with uncertain parameters into the automatic test assembly process for linear test forms.
%U https://aclanthology.org/2026.aimecon-main.1/
%P 1-6

Markdown (Informal)

[Stochastic Constrained Test Assembly for AI-Enabled Assessment Systems](https://aclanthology.org/2026.aimecon-main.1/) (von Davier, AIME-Con 2026)

• Stochastic Constrained Test Assembly for AI-Enabled Assessment Systems (von Davier, AIME-Con 2026)

ACL

• Alina A. von Davier. 2026. Stochastic Constrained Test Assembly for AI-Enabled Assessment Systems. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 1–6, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).