AI 中文总结
研究人工智能赋能评估系统中测试组装问题，提出随机约束混合（SCH）框架，将形式级组装转化为多臂老虎机问题，以Fisher信息为奖励，扩展相关方法到形式级，纳入参数不确定项目，通过模拟研究比较六种方法。
AI 中文摘要
测试组装是根据蓝图约束从题库构建完整测试形式的过程，传统上被视为静态优化问题。在人工智能赋能的评估环境中，随着新生成项目以不确定心理测量参数进入且按需交付，题库不断演变。这使测试组装成为不确定性下的顺序决策问题。本文提出随机约束混合（SCH）框架，将形式级组装重塑为以Fisher信息为奖励的多臂老虎机问题，扩展了计算机自适应测试中项目级方法到形式级设置。还进行了比较六种测试组装方法的模拟研究。本文主要贡献是将参数不确定的项目纳入线性测试形式自动测试组装过程的框架。
英文摘要
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.
