Open-ended questions offer deep insights into students’ reasoning, but present persistent challenges in terms of grading consistency and bias. In this paper, we introduce Criterium, a teacher-assistant system designed to support fair, explainable, and scalable evaluation of open-ended student responses, particularly in the humanities. Unlike traditional automated grading solutions, Criterium places the teacher at the center of the evaluation loop, offering structured rubrics, prompt-based LLM scoring, and optional content retrieval to anchor judgments in curricular material. We present the system architecture, the underlying scoring model – which distinguishes minimum from advanced criteria – and results from a real-world pilot involving multiple classes and assignments. Criterium aims not to replace teacher judgment, but to enhance its objectivity and traceability in both formative and summative contexts.