用AI对话式考试动态评估学生认知边界，避免焦虑与评分偏差。
The Theoretical Foundation of Socratic Tests: Dynamic, Multimodal, Conversational Examinations
- 通过对话逐步引导，动态测量学生最近发展区
- 采用非补偿性加分制，精准反映真实掌握程度
- 适合需要公平、深入评估的教育场景
传统静态测评采用减分式评价，常惩罚尝试并掩盖诊断信息；面对面口试则因表现焦虑和学术权力失衡引入额外干扰。本文提出「苏格拉底测试」——一种由计算机驱动的对话式评估体系。融合动态评估原理、多模态工作空间、布卢姆分类法实现实时监考，以及SOLO分类法进行结构化评价，该系统能主动定位学生的认知边界。论文形式化了分级支持策略以量化最近发展区（ZPD），并设计了一种非补偿性、累加式评分架构，强调掌握而非惩罚，通过人机对齐确保测量可靠性。
原文摘要 · Abstract (English)
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.
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