Members of the Bridges Lab

Graduate Research Assistant

Dr. Bridges is developing and evaluating an AI-based tool that generates competency-based clinical case studies and provides rubric-based feedback on student responses. This project, funded through the Center for Academic Innovation, will test whether AI can deliver feedback comparable in quality to an experienced instructor. If it works, it could give students rapid, actionable feedback on case studies at scale. Working closely with Dr. Bridges over the 2026–27 academic year, you would:

• Review AI-generated case studies and rate their difficulty, correctness, and clinical relevance
• Help compare cases and feedback generated by different AI models (Claude, OpenAI, UM-GPT)
• Support testing of the automatic grading function against instructor "gold-standard" grading
• Assist with concordance evaluation and iterating on the grading instructions
• Contribute to a pilot Canvas integration

Qualifications

• A current MS or MPH student in Nutritional Sciences, ideally a dietitian-in-training
• Solid grounding in clinical nutrition
• Careful, detail-oriented, and reliable with independent work
• Interest in AI in health professions education is a plus

Required Skills

An ability to apply rubrics and clinical judgment consistently to case study responses. Should have strong written communication and attention to detail. Comfort with data or light technical tools (e.g., spreadsheets, and openness to learning a bit of API/coding work) is welcome but not required.

Responsibilities

The person will review AI-generated case studies and rate their difficulty, correctness, and clinical relevance. They will help compare cases and feedback generated by different AI models (Claude, OpenAI, UM-GPT). You will support testing of the automatic feedback function against instructor "gold-standard" feedback. Finally, you will assist with evaluation and iterating on the grading instructions

Apply here

Date Posted: July 20, 2026