How can AI not only score student answers accurately, but also explain why it assigned a particular score?
In our latest paper, we introduce AGRAA (Aspect-Grounded Rubric–Answer Alignment), a new framework for rubric-based assessment that represents rubric criteria as semantic subspaces. Instead of treating scoring as a standard classification problem, AGRAA measures how strongly a student’s response aligns with the latent semantic aspects defined by each rubric level.
We evaluate the approach on both short-answer and essay scoring benchmarks, where it consistently achieves performance highly competitive with strong transformer-based baselines. More importantly, the model naturally produces interpretable explanations by showing which rubric-defined aspects contributed to each scoring decision.
We believe this work is a step toward automated assessment systems that are not only accurate, but also transparent, trustworthy, and aligned with the way human assessors use rubrics.