发表机构
Northwestern University（西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出苏格拉底测试的理论基础，整合多类教育评估原则与分类学，量化最近发展区并设计非补偿性加法评分架构，以解决传统评估的缺陷并提升测量可靠性。
AI 中文摘要
传统静态评估采用减法式、基于缺陷的评分模型，常惩罚学生的进取性且模糊诊断反馈；传统面对面口试则因加剧表现焦虑和学术层级固有的社会学权力失衡，引入严重的非构念方差。本文提出自动化、计算机介导的对话式评估——“苏格拉底测试”的理论基础，其整合动态评估原则、多模态工作空间、用于实时监考的布鲁姆教育目标分类学、用于结构评估的SOLO分类学，主动映射学生的认知边界。本文形式化运用分级支架量化最近发展区（ZPD），详述非补偿性加法评分架构，该架构优先掌握知识而非惩罚，并结合人机协同以实现前所未有的测量可靠性。
英文摘要
Traditional static assessments rely on a subtractive, deficit-based grading model that often penalizes ambition and obscures diagnostic feedback. Conversely, traditional face-to-face oral examinations introduce severe construct-irrelevant variance by exacerbating performative anxiety and the sociological power imbalances inherent to academic hierarchies. This paper presents the theoretical foundation for the "Socratic Test," an automated, computer-mediated conversational assessment. By integrating Dynamic Assessment principles, multimodal workspaces, Bloom's Taxonomy for real-time proctoring, and the SOLO Taxonomy for structural evaluation, the Socratic Test actively maps a student's cognitive boundaries. This paper formalizes the use of graduated scaffolding to quantify the Zone of Proximal Development (ZPD) and details a non-compensatory, additive grading architecture that prioritizes mastery over penalty and human-AI alignment to ensure unprecedented measurement reliability.
Comments21 pages, 1 figure, submitted to Computers and Education: Artificial Intelligence
