在STEM评估中定位生成式人工智能：何时要求、搭建支架或限制其使用
Positioning Generative Artificial Intelligence in STEM Assessment: When to Require, Scaffold, or Restrict Its Use
- University of Georgia（佐治亚大学）
- University of Central Florida（中佛罗里达大学）
- University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文基于证据中心设计（ECD）提出以学生为中心的框架，明确STEM评估中GenAI的限制、支架或要求场景，结合入门物理示例设计任务，平衡学习完整性与AI赋能环境准备需求。
AI中文摘要：
生成式人工智能（GenAI）对STEM评估提出了治理挑战。不受限制的访问可能导致任务外包，破坏传统评估的有效性；而全面禁止则难以执行，可能导致使用转入地下，且对学生为日益普遍的GenAI支持型工作流程做准备帮助不大。本文提出了一个以学生为中心的框架，基于证据中心设计（ECD），该框架明确了在STEM评估中何时限制、搭建支架或要求GenAI使用。该框架通过提供将目标构念、证据要求和任务特征与治理机制关联的决策规则，扩展了现有的AI使用分类法。当GenAI威胁到与未辅助熟练度相关的构念证据，尤其是基础知识和常规技能时，限制是合理的；当有限的GenAI支持减少外围需求同时保持可解释性时，搭建支架是合适的；当目标构念涉及人机协作和AI素养时，要求GenAI使用是合适的。我们利用入门物理学的例子，说明了如何在不同的GenAI使用政策下设计任务。该框架为在维护学习完整性的同时，支持学生为AI赋能环境做准备提供了指导。
英文摘要：
Generative Artificial Intelligence (GenAI) presents a governance challenge for STEM assessment. Unrestricted access can enable task outsourcing that undermines the validity of traditional assessments, while blanket prohibitions are difficult to enforce, may drive use underground, and do little to prepare students for workplaces where GenAI supported workflows are increasingly common. This paper proposes a student focused framework grounded in Evidence Centered Design (ECD) that specifies when to restrict, scaffold, or require GenAI use in STEM assessment. The framework extends existing AI use taxonomies by providing decision rules that link target constructs, evidence requirements, and task characteristics to governance regimes. Restriction is warranted when GenAI threatens construct relevant evidence for unaided proficiency, particularly for foundational knowledge and routine skills. Scaffolding is appropriate when bounded GenAI support reduces peripheral demands while maintaining interpretability. Requiring GenAI is appropriate when the target construct involves human AI collaboration and AI literacy. Using examples from introductory physics, we illustrate how tasks can be designed under different GenAI use policies. The framework provides guidance for preserving learning integrity while supporting preparation for AI enabled environments.
