Graduate Research Assistant (LLM Interaction and Statistical Analysis)
About The Position
Under the direction of the Learning Lab leadership team, the employee will contribute to the design and implementation of research involving medical learners’ interactions with generative AI tools. The appointment is on a semester-by-semester basis. This role provides hands-on experience at the intersection of data science, clinical reasoning research, and human–AI interaction, contributing to empirical analyses of how LLMs are used in real clinical reasoning tasks. The GRA will work extensively with LLM-related data, including chat transcripts, structured interaction logs, usage metadata, and quantitative evaluation measures (e.g., rubrics and surveys). The primary emphasis of this role is statistical and computational analysis of clinician–LLM interactions and their relationship to clinical reasoning outcomes.
Requirements
- Bachelor’s degree in data science, cognitive science, psychology, informatics, communication, social science, or related field.
- Experience working with quantitative data and computational analysis.
- Experience conducting statistical or computational analysis
- Experience working with text-based or interaction data (e.g., chat logs)
- Strong organizational skills and attention to detail.
- Ability to manage secure data and respect confidentiality protocols.
- Ability to work independently and collaboratively.
- Relevant education and experience may be substituted as appropriate.
Nice To Haves
- Minimum of Master’s level coursework in data science, informatics, psychology, or related fields.
- Experience with Python or R for text analysis, NLP, or data wrangling.
- Experience with human–AI interaction or clinical/medical education settings.
- Experience contributing to IRB submissions.
Responsibilities
- Analyze LLM chat logs and interaction metadata (e.g., turn taking, prompt types, revision behavior)
- Conduct statistical analyses combining LLM data with structured measures (rubrics, surveys)
- Support mixed methods synthesis in collaboration with qualitative researchers
- Prepare internal analytic memos documenting methods, findings, and implications
- Contribute figures, tables, and methods text for academic papers and white papers
Benefits
- Tuition Reduction Benefit (TRB)
- UTSaver voluntary retirement programs
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What This Job Offers
Job Type
Part-time
Career Level
Entry Level
