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Grading of exams is an important, labor intensive, subjective, repetitive andfrequently challenging task. The feasibility of autograding textual responseshas greatly increased thanks to the availability of large language models(LLMs) such as ChatGPT and because of the substantial influx of data broughtabout by digitalization. However, entrusting AI models with decision-makingroles raises ethical considerations, mainly stemming from potential biases andissues related to generating false information. Thus, in this manuscript weprovide an evaluation of a large language model for the purpose of autograding,while also highlighting how LLMs can support educators in validating theirgrading procedures. Our evaluation is targeted towards automatic short textualanswers grading (ASAG), spanning various languages and examinations from twodistinct courses. Our findings suggest that while "out-of-the-box" LLMs providea valuable tool to provide a complementary perspective, their readiness forindependent automated grading remains a work in progress, necessitating humanoversight.

Towards LLM-based Autograding for Short Textual Answers