  End of semester student evaluations of teaching are the dominant mechanism
for providing feedback to academics on their teaching practice. For large
classes, however, the volume of feedback makes these tools impractical for this
purpose. This paper explores the use of open-source generative AI to synthesise
factual, actionable and appropriate summaries of student feedback from these
survey responses. In our setup, we have 742 student responses ranging over 75
courses in a Computer Science department. For each course, we synthesise a
summary of the course evaluations and actionable items for the instructor. Our
results reveal a promising avenue for enhancing teaching practices in the
classroom setting. Our contribution lies in demonstrating the feasibility of
using generative AI to produce insightful feedback for teachers, thus providing
a cost-effective means to support educators' development. Overall, our work
highlights the possibility of using generative AI to produce factual,
actionable, and appropriate feedback for teachers in the classroom setting.
