Automatic Short Answer Scoring (ASAS) is central to NLP for Education. However, openly available benchmarks remain scarce, and existing datasets largely address how well students answer a question directly rather than how well they master underlying concepts (knowledge elements) such as thermal energy or epistemic activities (skills) such as reasoning or claim.

To address this gap, we introduce Alice, a large-scale, rubric-based German ASAS dataset that is pedagogically aligned and comprises three subtasks: (i) learning performance (Alice-LP), (ii) knowledge elements (Alice-KE), and (iii) skills (Alice-SK).

We further formulate rubric-based ASAS as a rubric-retrieval task and benchmark the dataset with a range of language models, from encoder-only models to lightweight LLMs. We also benchmark the dataset with zero-shot prompting via LLMs and a standard classification baseline. The experiments show that LLMs, in particular, struggle to score knowledge elements and skills in the zero-shot setting. They also indicate that rubric text is often useful, especially for Alice-KE and Alice-SK, while on Alice-LP gains over sample-solution-focused inputs are more modest and vary by model and input format.

We present the BEA 2026 shared task on rubric-based short answer scoring for German. Rubric-based short answer scoring is a case of automatic short answer scoring (ASAS) that requires models to apply textual scoring rubrics to student answers as a basis for assigning scores. For the shared task, we introduced a novel German-language dataset from multiple STEM domains to provide a comprehensive benchmark for this problem. The dataset was designed to evaluate both performance and generalization (the latter, by distinguishing between seen and unseen questions), as well as coarse- and fine-grained scoring (2-way vs. 3-way). The systems submitted to the shared task cover a wide range of approaches, including fine-tuned large language models, prompt-based methods, human-AI collaboration strategies, or a combination of these. The results show that structured, task-adapted LLM systems achieved the strongest performance across all tracks. The winning system, IWM-DKM, combined LoRA fine-tuning of Qwen models with rubric-aware input structuring, including checklist-style reasoning, rubric reframing as decision trees, background knowledge injection, and ensemble voting. Other systems similarly relied on fine-tuned LLMs, retrieval-augmented prompting, encoder–LLM ensembles, or weighted aggregation strategies. Overall, the shared task results show that rubric-based scoring benefits most from systems that explicitly operationalise rubric semantics, while generalisation to unseen questions remains a central challenge.