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.

使用开源生成型 AI 合成学生反馈的事实性、可操作性和恰当性总结，为教师提供成本效益的支持，以增强教学实践的可行性。

利用大型语言模型进行可行课程评估 —— 学生对讲师的反馈

Leveraging Large Language Models for Actionable Course Evaluation  Student Feedback to Lecturers

The emergence of large language models has led to the development of powerful
tools such as ChatGPT that can produce text indistinguishable from
human-generated work. With the increasing accessibility of such technology,
students across the globe may utilize it to help with their school work -- a
possibility that has sparked discussions on the integrity of student
evaluations in the age of artificial intelligence (AI). To date, it is unclear
how such tools perform compared to students on university-level courses.
Further, students' perspectives regarding the use of such tools, and educators'
perspectives on treating their use as plagiarism, remain unknown. Here, we
compare the performance of ChatGPT against students on 32 university-level
courses. We also assess the degree to which its use can be detected by two
classifiers designed specifically for this purpose. Additionally, we conduct a
survey across five countries, as well as a more in-depth survey at the authors'
institution, to discern students' and educators' perceptions of ChatGPT's use.
We find that ChatGPT's performance is comparable, if not superior, to that of
students in many courses. Moreover, current AI-text classifiers cannot reliably
detect ChatGPT's use in school work, due to their propensity to classify
human-written answers as AI-generated, as well as the ease with which
AI-generated text can be edited to evade detection. Finally, we find an
emerging consensus among students to use the tool, and among educators to treat
this as plagiarism. Our findings offer insights that could guide policy
discussions addressing the integration of AI into educational frameworks.

该研究比较了 ChatGPT 和 32 门大学课程学生的表现，发现 ChatGPT 在许多课程中的表现相当，甚至优于许多学生。此外，其使用也难以被 AI 文本分类器可靠地检测出来，并且出现了学生使用该工具和教育者将其视为抄袭的共识，这些发现为 AI 融入教育框架的政策讨论提供了指导。