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

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.