# Automated Assessment of Students' Code Comprehension using LLMs. (arXiv:2401.05399v1 [cs.CY])


Assessing student's answers and in particular natural language answers is a
crucial challenge in the field of education. Advances in machine learning,
including transformer-based models such as Large Language Models(LLMs), have led
to significant progress in various natural language tasks. Nevertheless, amidst
the growing trend of evaluating LLMs across diverse tasks, evaluating LLMs in
the realm of automated answer assesment has not received much attention. To
address this gap, we explore the potential of using LLMs for automated
assessment of student's short and open-ended answer. Particularly, we use LLMs
to compare students' explanations with expert explanations in the context of

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