Can AI tools assess coding assignments?

Credit: Creative Images Lab/Getty One evening, my partner Boyan Li sat at the kitchen table marking student submissions for a coding course he was teaching as part of his PhD at Harvard Medical School in Boston, Massachusetts. The assignment required students to implement a computational-biology algorithm on a given data set. Each submission demanded more than a quick check. He ran the code, examined the output and traced the logic line by line. Some submissions were clearly correct; others were clearly wrong. But many fell into a grey zone: they were partly right, but uneven in their execution or reasoning. These were the hardest to assess, and the most time-consuming. As a higher-education researcher, I watched this process with professional interest. What seemed to be a purely technical task — running code and checking outputs — was revealed to be deeply interpretative. Assessing coding assignments involves deciding what counts as understanding, what counts as error and how much variation is acceptable. This resonated with my own research on student learning and development, which views educational activities as inherently relational: even something as seemingly mechanical as marking becomes a dialogue between the examiner and the learner. …