  Problem solving is an integral part of any physics curriculum, and most
physics instructors would likely agree that the associated learner competencies
are best assessed by considering the solution path: not only the final solution
matters, but also how the learner arrived there. Unfortunately, providing
meaningful feedback on written derivations is much more labor and resource
intensive than only grading the outcome: currently, the latter can be done by
computer, while the former involves handwritten solutions that need to be
graded by humans. This exploratory study proposes an AI-assisted workflow for
grading written physics-problem solutions, and it evaluates the viability of
the actual grading step using GPT-4. It is found that the AI-tool is capable of
providing feedback that can be helpful in formative assessment scenarios, but
that for summative scenarios, particularly those that are high-stakes, it
should only be used for an initial round of grading that sorts and flags
solution approaches.
