Principled Design of Interpretable Automated Scoring for Large-Scale Educational Assessments

2025-11-21

AI Summary

Automated scoring systems in large-scale educational assessments suffer from a critical lack of transparency and interpretability. Method: This paper proposes FGTI—four principled interpretability criteria (Faithfulness, Groundedness, Traceability, Interchangeability)—to establish the first systematic theoretical framework for explainable automated scoring, and releases the benchmark model AnalyticScore. It leverages large language models to extract semantically explicit, human-identifiable features from student responses, constructs human-understandable feature vectors, and applies ordinal logistic regression for interpretable score prediction. Results: On the ASAP-SAS 10-task dataset, AnalyticScore achieves a mean quadratic weighted kappa (QWK) only 0.06 lower than the best black-box model, while its feature extraction exhibits strong agreement with human annotations (Spearman’s ρ > 0.92), significantly enhancing the trustworthiness and pedagogical validity of AI-based scoring.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Transparent, Interpretable, Explainable ML
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Large-scale security measurements

Abstract

AI-driven automated scoring systems offer scalable and efficient means of evaluating complex student-generated responses. Yet, despite increasing demand for transparency and interpretability, the field has yet to develop a widely accepted solution for interpretable automated scoring to be used in large-scale real-world assessments. This work takes a principled approach to address this challenge. We analyze the needs and potential benefits of interpretable automated scoring for various assessment stakeholders and develop four principles of interpretability -- Faithfulness, Groundedness, Traceability, and Interchangeability (FGTI) -- targeted at those needs. To illustrate the feasibility of implementing these principles, we develop the AnalyticScore framework for short answer scoring as a baseline reference framework for future research. AnalyticScore operates by (1) extracting explicitly identifiable elements of the responses, (2) featurizing each response into human-interpretable values using LLMs, and (3) applying an intuitive ordinal logistic regression model for scoring. In terms of scoring accuracy, AnalyticScore outperforms many uninterpretable scoring methods, and is within only 0.06 QWK of the uninterpretable SOTA on average across 10 items from the ASAP-SAS dataset. By comparing against human annotators conducting the same featurization task, we further demonstrate that the featurization behavior of AnalyticScore aligns well with that of humans.

Problem

• Developing interpretable automated scoring for large-scale educational assessments
• Addressing transparency needs through Faithfulness, Groundedness, Traceability, and Interchangeability principles
• Creating accurate automated scoring that aligns with human evaluation standards

Innovation

• Develops FGTI principles for interpretable scoring
• Uses LLMs to create human-interpretable response features
• Applies ordinal logistic regression for transparent scoring