The Theoretical Foundation of Socratic Tests: Dynamic, Multimodal, Conversational Examinations
- URL: http://arxiv.org/abs/2607.29624v1
- Date: Fri, 31 Jul 2026 16:57:49 GMT
- Title: The Theoretical Foundation of Socratic Tests: Dynamic, Multimodal, Conversational Examinations
- Abstract summary: "Socratic Test" is an automated, computer-mediated conversational assessment.<n>This paper formalizes the use of graduated scaffolding to quantify the Zone of Proximal Development (ZPD)<n>Details a non-compensatory, additive grading architecture that prioritizes mastery over penalty and human-AI alignment.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- INSPIRE: An Internalize-Then-Improve Approach for Example-Driven Mathematical Reasoning [61.60016491769943]
In human mathematics education, example-based reasoning such as constructing counterexamples to test theorem boundaries reflects deep conceptual understanding.<n>We propose INSPIRE, an Internalize-Then-Improve approach combining Reference-Guided Student Internalization (RGSI) with a stage-wise preference training strategy.<n>Experiments across multiple model scales and families demonstrate consistent improvements, even surpassing larger open-source models.
arXiv Detail & Related papers (2026-08-27T03:24:03Z) - Multi-Dimensional Assessment for AI Cognition (MAAC): A Theoretical Framework for Process-Oriented Cognitive Evaluation of Text-Based AI Systems [1.8262547855491456]
This paper introduces the Multi-Dimensional Assessment for AI Cognition (MAAC), a framework for shifting evaluation from what text-based AI systems produce to how they think.<n>MAAC defines nine cognitively motivated dimensions: Cognitive Load, Tool Execution, Content Quality, Memory Integration, Complexity Handling, Hallucination Control, Knowledge Transfer, Processing Efficiency, and Process-Outcome Alignment.<n>Five theoretical analyses provide initial support for the framework's coherence and empirical testability.
arXiv Detail & Related papers (2026-08-01T14:01:17Z) - Rethinking Entropy Minimization in Test-Time Adaptation for Autoregressive Models [52.18157855348584]
Test-Time Adaptation (TTA) via entropy minimization (EM) has proven effective for classification tasks, yet its application remains theoretically fragmented.<n>In this work, we deriving a rigorous formulation of EM tailored to autoregressive models.<n>We show that the exact objective naturally decomposes into a token-level policy gradient loss and a token-level entropy loss, and we reinterpret prior methods as partial realizations of this unified formulation.<n>Using Whisper ASR as a testbed, we demonstrate that our approach consistently improves performance across more than 20 diverse domains, including acoustic noise, accents, and multilingual settings.
arXiv Detail & Related papers (2026-05-05T12:00:06Z) - General Machine Learning: Theory for Learning Under Variable Regimes [2.538209532048866]
We study learning under regime variation, where the learner, its memory state, and the evaluative conditions may evolve over time.<n>The paper develops a regime-varying framework centered on admissible transport, protected-core preservation, and evaluator-aware learning evolution.
arXiv Detail & Related papers (2026-03-24T13:58:31Z) - InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem [87.30601926271864]
InnoEval is a deep innovation evaluation framework designed to emulate human-level idea assessment.<n>We apply a heterogeneous deep knowledge search engine that retrieves and grounds dynamic evidence from diverse online sources.<n>We construct comprehensive datasets derived from authoritative peer-reviewed submissions to benchmark InnoEval.
arXiv Detail & Related papers (2026-02-16T00:40:31Z) - PRISM-Physics: Causal DAG-Based Process Evaluation for Physics Reasoning [57.868248683256574]
PRISM-Physics is a process-level evaluation framework and benchmark for complex physics reasoning problems.<n> Solutions are represented as directed acyclic graphs (DAGs) of formulas.<n>Results show that our evaluation framework is aligned with human experts' scoring.
arXiv Detail & Related papers (2025-10-03T17:09:03Z) - Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment [0.0]
This paper presents a theoretical framework for addressing the challenges posed by generative artificial intelligence (AI) in higher education assessment.<n>Large language models like GPT-4, Claude, and Llama increasingly demonstrate the ability to produce sophisticated academic content.<n>Surveys indicate 74-92% of students experimenting with these tools for academic purposes.
arXiv Detail & Related papers (2025-05-31T22:29:43Z) - Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
arXiv Detail & Related papers (2025-02-07T17:01:37Z) - Position: AI Evaluation Should Learn from How We Test Humans [65.36614996495983]
We argue that psychometrics, a theory originating in the 20th century for human assessment, could be a powerful solution to the challenges in today's AI evaluations.
arXiv Detail & Related papers (2023-06-18T09:54:33Z) - Independent Ethical Assessment of Text Classification Models: A Hate
Speech Detection Case Study [0.5541644538483947]
An independent ethical assessment of an artificial intelligence system is an impartial examination of the system's development, deployment, and use in alignment with ethical values.
This study bridges this gap and designs a holistic independent ethical assessment process for a text classification model with a special focus on the task of hate speech detection.
arXiv Detail & Related papers (2021-07-19T23:03:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.