Expanding possibilities for generative AI in qualitative analysis: Fostering student feedback literacy through the application of a feedback quality rubric

Background

Courses in engineering often use peer evaluation to monitor teamwork behaviors and team dynamics. The qualitative peer comments written for peer evaluations hold potential as a valuable source of formative feedback for students, yet little is known about their content and quality.

Purpose

This study uses a large language model (LLM) to apply a previously tested feedback quality rubric to peer feedback comments. Our research questions interrogate the reliability of LLMs for qualitative analysis with a rubric and use Bandura's self-regulated learning theory to assess peer feedback quality of first-year engineering students' comments.

Method

An open-source, local LLM was used to score each comment according to four rubric criteria. Inter-rater reliability (IRR) with human raters using Cohen's quadratic weighted kappa was the primary metric of reliability. Our assessment of peer feedback quality utilized descriptive statistics.

Results

The LLM achieved lower IRR than human raters, but the model's challenges mimic those of human raters. The model did achieve an excellent quadratic weighted kappa of 0.80 for one rubric criterion, which shows promise for LLM capability. For feedback quality, students generally wrote low- to medium-quality comments that were infrequently grounded in specific teamwork behaviors. We identified five types of peer feedback that inform how students perceive the feedback process.

Conclusions

Our implementation of GAI suggests that LLMs can be helpful for rapid iteration of research designs, but consistent and reliable analysis with generative artificial intelligence (GAI) requires significant effort and testing. To develop feedback literacy, students must understand how to provide high-quality feedback.