EduGrader: The AI-powered grading assistant

The rapid development of artificial intelligence (AI) and large language models (LLMs) is reshaping assessment practices in higher education, particularly in grading open-ended questions. Traditional grading is time-consuming, prone to inconsistency, and difficult to scale for large classes. LLMs offer an opportunity to improve efficiency, consistency, and feedback frequency by leveraging their ability to understand and reason about natural language. This paper introduces EduGrader, an open-source, multi-model grading platform integrating several LLMs, including GPT-4o, GPT-4o-mini, DeepSeek-Chat, GPT-5.1, and DeepSeek-Reasoner. EduGrader supports three grading strictness levels (lenient, neutral, strict) and generates numerical scores with concise explanations highlighting correct reasoning, missing concepts, and errors. The system operates in two modes: a provided reference-answer grading mode guided by instructor solutions and a generated reference-answer grading mode that automatically creates reference answers from course materials. All experiments are conducted in Serbian, a comparatively low resource language underrepresented in mainstream AI training data, extending AI assisted grading research beyond the dominant English-language context. EduGrader is evaluated on 686 real student responses from six university courses across two institutions, including free-text explanations and code-based answers. In its best performing configuration, the system achieves a Pearson correlation coefficient of 0.90 with human graders while reducing grading workload and improving reliability, though it is intended to assist rather than replace instructors.