EssayCBM: Rubric-Aligned Concept Bottleneck Models for Transparent Essay Grading

TL;DR

EssayCBM is a transparent, rubric-aligned concept bottleneck model for automated essay grading that enables interpretability, inspection, and manual adjustment of grading decisions at the rubric level.

Contribution

It introduces a novel framework that decomposes essay evaluation into interpretable concepts, improving transparency and controllability over neural-based grading systems.

Findings

EssayCBM matches neural AES performance benchmarks.

It provides an interpretable mapping from concepts to grades.

Instructors can inspect and modify rubric-level predictions in real time.

Abstract

Automated essay scoring (AES) has advanced significantly with neural language models, yet most systems remain opaque, offering little visibility into how grades are produced. In educational settings, instructors must be able to understand, trust, and occasionally override the automated grading decisions. We introduce EssayCBM, a rubric-aligned concept bottleneck framework that decomposes essay evaluation into eight interpretable writing concepts before computing the final score. Unlike direct LLM-based grading approaches, EssayCBM learns an explicit and auditable mapping from writing concepts to grades, allowing instructors to inspect and adjust rubric-level predictions during grading. EssayCBM matches neural AES baselines while making grading decisions transparent and directly editable at the rubric level. We further present an interactive system that demonstrates this capability by allowing instructors to inspect and modify concept predictions in real time.