🤖 AI Summary
This study addresses the limitations of traditional static assessments, which overemphasize penalty for errors and offer weak diagnostic insight, as well as oral examinations, whose validity is compromised by performance anxiety and power imbalances. To overcome these issues, the paper proposes an automated, human–AI dialogic “Socratic test” that integrates dynamic assessment, a multimodal interaction space, real-time scaffolding informed by Bloom’s taxonomy, and structured scoring based on the SOLO taxonomy within a non-compensatory cumulative architecture to support mastery-oriented evaluation. The approach formally operationalizes the Zone of Proximal Development (ZPD) through progressive scaffolding and ensures measurement reliability via human–AI alignment. This novel assessment framework significantly enhances the precision of dynamically probing students’ cognitive boundaries, effectively balancing diagnostic utility with fairness.
📝 Abstract
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.