Interpretable AI for Education: Unlocking Trustworthy Automated Scoring
By Yunsung Kim, Mike Hardy, Joseph Tey, Candace Thille, Chris Piech
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CL updates on arXiv.org.
Summary
This paper addresses the critical need for interpretable AI in large-scale automated educational scoring. It introduces a principled approach, defining four key interpretability principles (Faithfulness, Groundedness, Traceability, and Interchangeability - FGTI) tailored to various assessment stakeholders. To demonstrate feasibility, the authors developed the AnalyticScore framework for short answer scoring. AnalyticScore leverages Large Language Models (LLMs) to extract and featurize explicitly identifiable, human-interpretable elements from responses, subsequently applying an intuitive ordinal logistic regression model for scoring. Crucially, AnalyticScore not only outperforms many uninterpretable scoring methods and is competitive with state-of-the-art uninterpretable solutions but also demonstrates strong alignment with human annotators in its featurization behavior.
Why It Matters
This research is highly significant for AI professionals, particularly those working on high-stakes applications and ethical AI. The push for interpretable AI in educational assessment directly tackles the "black box" problem in a domain where decisions profoundly impact individuals' lives and futures. Achieving transparency and explainability here is not just an academic exercise; it's a fundamental requirement for building trust and facilitating wider adoption of AI systems. For professionals, this highlights a critical trend: the shift from prioritizing solely predictive accuracy to integrating interpretability and fairness as core design principles, especially as regulatory pressures around AI ethics intensify. Furthermore, the innovative use of Large Language Models (LLMs) for extracting human-interpretable features demonstrates a powerful paradigm beyond mere content generation, showcasing their potential for robust, explainable analytical tasks. This framework could serve as a blueprint for developing trustworthy AI solutions in other sensitive sectors, such as HR, legal, or healthcare, where explainable decision-making is paramount, ultimately advancing the broader goal of responsible and impactful AI deployment.