Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2025

Abstract

Student feedback is critical for improving teaching, yet instructors often avoid reading evaluations due to emotional burden and information overload. We present a systematic exploration of how language models can distill and transform student evaluations into adaptive, actionable insights. Through a systematic design space exploration combining 4 feedback strategies (removing harmful content, paraphrasing criticism, sandwiching negatives, adding constructive suggestions) with 4 presentation formats (themes, cards, letters, chatbots), we created six AI-augmented prototypes of teaching evaluations. Interviews with 16 post-secondary instructors revealed that effective use of AI in feedback processing should: (1) support action formation through focused views and divergent thinking, (2) reduce emotional costs while enabling celebration and sharing, (3) facilitate longitudinal engagement and re-contextualization across terms, and (4) maintain transparency and preserve access to original context to build trust. Our work provides design guidelines for AI-augmented feedback systems and demonstrates how language models can adaptively process and present information based on feedback receivers' specific needs and contexts.

Keywords

educational technology, human-AI interaction, interface design, language models, student evaluations of teaching

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the ACM on Human-Computer Interaction

Volume

9

Issue

7

First Page

1

Last Page

40

ISSN

2573-0142

Identifier

10.1145/3757501

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://dl.acm.org/doi/10.1145/3757501

Download

Share

COinS