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Listening with Language Models: Using LLMs to Collect and Interpret Classroom Feedback

Authors
Sai Siddartha Maram,
Ulia Zaman,
Magy Seif El-Nasr
Date
Publisher
arXiv
Link
http://arxiv.org/pdf/2508.11707v1
Traditional end-of-quarter surveys often fail to provide instructors with timely, detailed, and actionable feedback about their teaching. In this paper, we explore how Large Language Model (LLM)-powered chatbots can reimagine the classroom feedback process by engaging students in reflective, conversational dialogues. Through the design and deployment of a three-part system-PromptDesigner, FeedbackCollector, and FeedbackAnalyzer-we conducted a pilot study across two graduate courses at UC Santa Cruz. Our findings suggest that LLM-based feedback systems offer richer insights, greater contextual relevance, and higher engagement compared to standard survey tools. Instructors valued the system's adaptability, specificity, and ability to support mid-course adjustments, while students appreciated the conversational format and opportunity for elaboration. We conclude by discussing the design implications of using AI to facilitate more meaningful and responsive feedback in higher education.
Study at a glance
  • Question: How can AI chatbots be used to collect and interpret student feedback on classroom teaching?
  • Setting and data: Two graduate courses at UC Santa Cruz.
  • Method: Pilot study testing the design and deployment of a three-part conversational feedback system.
Why it matters

Traditional end-of-quarter surveys often fail to provide instructors with timely, detailed, and actionable feedback about their teaching.

Key findings
  • In two university graduate courses, results suggest that AI feedback systems offer richer insights, greater contextual relevance, and higher engagement than standard survey tools.
  • Instructors valued the AI system's adaptability, specificity, and ability to help them make mid-course teaching adjustments.
  • Students appreciated the conversational format of the AI feedback tool and the opportunity to elaborate on their responses.

Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.

What is the application?
Teaching – Assessment and Feedback
Who is the user?
Student,
Educator
Who age?
Post-Secondary
Why use AI?
Efficiency,
Outcomes – Other Academic,
Reimagined Schooling
Study design
Descriptive – Implementation and Use,
Descriptive – Product Development
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