Graduate Research Assistant (Mixed-methods and Multimodal Clinical Reasoning Analysis)
About The Position
This position will support the research activities of the AI Learning Lab, a collaborative initiative with Dell Medical School focused on understanding how clinicians and trainees use large language models during clinical reasoning. The study uses mixed-methods to examine clinical reasoning quality, cognitive engagement, and human–AI interaction in simulated clinical environments. The GRA will work across quantitative and qualitative analyses, integrating data from multiple sources, including LLM chat transcripts, structured interaction logs, video and audio recordings from standardized patient encounters, transcripts, rubrics, surveys, and interviews. This role is designed for a student comfortable working across methods, helping translate multimodal data into empirically grounded insights about clinical reasoning and AI use.
Requirements
- Experience in qualitative research methods
- Experience conducting statistical analysis of multi-modal data, including text-based data (e.g., chat logs), rubrics, and/or surveys
- Ability to move between computational summaries and interpretive analysis
- Familiarity with R or Python and qualitative tools (e.g., Dedoose/NVivo) preferred
- Relevant education and experience may be substituted as appropriate.
Responsibilities
- Conduct statistical and descriptive analyses of LLM interaction data
- Code and interpret qualitative data from transcripts and interviews
- Align observed behaviors, written reasoning artifacts, and rubric scores
- Develop mixed-methods analytic memos synthesizing findings
- Collaborate closely with clinical faculty and the broader research team
Benefits
- UTSaver voluntary retirement programs
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What This Job Offers
Job Type
Part-time
Career Level
Entry Level
Education Level
No Education Listed
