Rethinking Teaching Evaluation Reports: Designing AI-transformed Student Feedback for Instructor Engagement

Ruoxi Shang, Keri Mallari, Wei Bin Au Yeong, Ken Yasuhara, Anthony Tang, and Gary Hsieh. (2025). Rethinking Teaching Evaluation Reports: Designing AI-transformed Student Feedback for Instructor Engagement. Proc. ACM Hum.-Comput. Interact. 9, 7.

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.

Materials

URL (https://doi.org/10.1145/3757501)
DOI (10.1145/3757501)

Keywords

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

BibTeX

@article{shang2025rethinking,
  author = {Shang, Ruoxi and Mallari, Keri and Au Yeong, Wei Bin and Yasuhara, Ken and Tang, Anthony and Hsieh, Gary},
  title = {Rethinking Teaching Evaluation Reports: Designing AI-transformed Student Feedback for Instructor Engagement},
  year = {2025},
  issue_date = {November 2025},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {9},
  number = {7},
  url = {https://doi.org/10.1145/3757501},
  doi = {10.1145/3757501},
  journal = {Proc. ACM Hum.-Comput. Interact.},
  month = oct,
  articleno = {CSCW320},
  numpages = {40},
  keywords = {educational technology, human-AI interaction, interface design, language models, student evaluations of teaching},
  type = {journal}
}