Rubrics are everywhere in education — but surprisingly, most AI systems for grading short answers barely use them. In our recent paper, Are Rubrics All You Need?, presented by Sebastian Gombert at the LAK 2026, we introduce rubric-based automatic short-answer scoring, a new approach where AI models explicitly align student answers with rubric criteria instead of treating grading as a black-box classification problem. We propose two novel architectures, GRAASP and ToLeGRAA, which use transformer-based alignment mechanisms to compare learner responses directly against rubric descriptions. Across German and English benchmark datasets, the models achieved highly competitive performance and transferred better to unseen questions than traditional instance-based classifiers. Particularly exciting is ToLeGRAA’s ability to generate token-level alignment maps, making it possible to visualize which parts of a student answer correspond to specific rubric criteria. This opens the door for more interpretable AI-supported assessment and potentially more targeted automated feedback. The work also introduces ALICE-LP, a new German dataset for rubric-based short-answer scoring collected in authentic school contexts. Overall, the paper argues that rubrics are not just supplementary metadata — they can become the central reasoning anchor for educational AI systems.
