Applying Large Language Models to Enhance the Assessment of Parallel Functional Programming Assignments

Courses in computer science (CS) often assess student programming assignments manually, with the intent of providing in-depth feedback to each student regarding correctness, style, efficiency, and other quality attributes. As class sizes increase, however, it is hard to provide detailed feedback consistently, especially when multiple assessors are required to handle a larger number of assignment submissions. Large language models (LLMs), such as ChatGPT, offer a promising alternative to help automate this process in a consistent, scalable, and minimally-biased manner.