Automated Grading
Overview
Automated grading studies how to assess student work consistently, from short answers and essays to mathematical reasoning, handwritten exams, and programming assignments. Approaches include fine-tuned language models and encoders, rubric-guided LLM judges, and vision-language systems that read handwritten work; many combine scores with explanations or route uncertain cases to human graders. Systems are commonly evaluated against expert marks using agreement and error measures, with benchmarks testing transfer across subjects, questions, and scoring rubrics. A central challenge is handling partial or ambiguous answers reliably while avoiding bias, prompt sensitivity, and overconfidence, so human oversight remains important, especially for high-stakes decisions.
Latest papers
89 papers6 papers in the last four weeks, down 14% on the four weeks before. 0.0% of all new papers.