  Instructor's feedback plays a critical role in students' development of
conceptual understanding and reasoning skills. However, grading student written
responses and providing personalized feedback can take a substantial amount of
time. In this study, we explore using GPT-3.5 to write feedback to student
written responses to conceptual questions with prompt engineering and few-shot
learning techniques. In stage one, we used a small portion (n=20) of the
student responses on one conceptual question to iteratively train GPT. Four of
the responses paired with human-written feedback were included in the prompt as
examples for GPT. We tasked GPT to generate feedback to the other 16 responses,
and we refined the prompt after several iterations. In stage two, we gave four
student researchers the 16 responses as well as two versions of feedback, one
written by the authors and the other by GPT. Students were asked to rate the
correctness and usefulness of each feedback, and to indicate which one was
generated by GPT. The results showed that students tended to rate the feedback
by human and GPT equally on correctness, but they all rated the feedback by GPT
as more useful. Additionally, the successful rates of identifying GPT's
feedback were low, ranging from 0.1 to 0.6. In stage three, we tasked GPT to
generate feedback to the rest of the student responses (n=65). The feedback was
rated by four instructors based on the extent of modification needed if they
were to give the feedback to students. All the instructors rated approximately
70% of the feedback statements needing only minor or no modification. This
study demonstrated the feasibility of using Generative AI as an assistant to
generating feedback for student written responses with only a relatively small
number of examples. An AI assistance can be one of the solutions to
substantially reduce time spent on grading student written responses.
