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Instructor's feedback plays a critical role in students' development ofconceptual understanding and reasoning skills. However, grading student writtenresponses and providing personalized feedback can take a substantial amount oftime. In this study, we explore using GPT-3.5 to write feedback to studentwritten responses to conceptual questions with prompt engineering and few-shotlearning techniques. In stage one, we used a small portion (n=20) of thestudent responses on one conceptual question to iteratively train GPT. Four ofthe responses paired with human-written feedback were included in the prompt asexamples 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 fourstudent researchers the 16 responses as well as two versions of feedback, onewritten by the authors and the other by GPT. Students were asked to rate thecorrectness and usefulness of each feedback, and to indicate which one wasgenerated by GPT. The results showed that students tended to rate the feedbackby human and GPT equally on correctness, but they all rated the feedback by GPTas more useful. Additionally, the successful rates of identifying GPT'sfeedback were low, ranging from 0.1 to 0.6. In stage three, we tasked GPT togenerate feedback to the rest of the student responses (n=65). The feedback wasrated by four instructors based on the extent of modification needed if theywere to give the feedback to students. All the instructors rated approximately70% of the feedback statements needing only minor or no modification. Thisstudy demonstrated the feasibility of using Generative AI as an assistant togenerating feedback for student written responses with only a relatively smallnumber of examples. An AI assistance can be one of the solutions tosubstantially reduce time spent on grading student written responses.

Exploring Generative AI assisted feedback writing for students' written  responses to a physics conceptual question with prompt engineering and  few-shot learning