Sessions For: UMOR MI Inst for Data Science
1 session on November 9, 2026
Focus: Using AI to support data preparation and quality improvement.
Description:
This session will examine how AI can help researchers think through data cleaning tasks before formal analysis begins. The emphasis will be on using AI as a support tool to identify inconsistencies, plan cleaning workflows, document decisions, and generate reusable code templates, while keeping human review central.
Possible hands-on activities:
Identify common data quality issues in a sample dataset
Draft a cleaning plan for missing values, duplicates, or formatting inconsistencies
Generate starter code for basic cleaning steps in Python
Create a data cleaning log or documentation template
Compare raw and cleaned data examples to assess impact
Reminder:
1. All attendees, please bring a laptop.
2. Sign in at genai.umich.edu to make sure your U-M GPT access is working.
Description:
This session will examine how AI can help researchers think through data cleaning tasks before formal analysis begins. The emphasis will be on using AI as a support tool to identify inconsistencies, plan cleaning workflows, document decisions, and generate reusable code templates, while keeping human review central.
Possible hands-on activities:
Identify common data quality issues in a sample dataset
Draft a cleaning plan for missing values, duplicates, or formatting inconsistencies
Generate starter code for basic cleaning steps in Python
Create a data cleaning log or documentation template
Compare raw and cleaned data examples to assess impact
Reminder:
1. All attendees, please bring a laptop.
2. Sign in at genai.umich.edu to make sure your U-M GPT access is working.
1 session on February 4, 2027
Description:
Explore how generative AI can support a rigorous, advanced approach to pre-registration. This session will demonstrate how researchers can use AI to develop analysis code and create simulated datasets before collecting real-world data—helping them clarify assumptions, test analytical workflows, identify potential problems, and strengthen research transparency and reproducibility.
Explore how generative AI can support a rigorous, advanced approach to pre-registration. This session will demonstrate how researchers can use AI to develop analysis code and create simulated datasets before collecting real-world data—helping them clarify assumptions, test analytical workflows, identify potential problems, and strengthen research transparency and reproducibility.
1 session on March 24, 2027
Focus: Using AI to critically assess outputs, check reasoning, and strengthen trust in findings.
Description:
This session would focus on using AI as a tool for review rather than solely for generation. Participants would practice checking results for plausibility, identifying possible errors or overclaims, and building habits for validation and reproducibility. This is especially important for avoiding misplaced confidence in AI-assisted work.
Possible hands-on activities:
Ask AI to critique an interpretation of findingsCross-check whether a result matches the stated method or assumptionsGenerate a validation checklist for an analysis workflowIdentify possible sources of bias, confounding, or errorReview examples of flawed outputs and discuss how to catch them
Description:
This session would focus on using AI as a tool for review rather than solely for generation. Participants would practice checking results for plausibility, identifying possible errors or overclaims, and building habits for validation and reproducibility. This is especially important for avoiding misplaced confidence in AI-assisted work.
Possible hands-on activities:
Ask AI to critique an interpretation of findingsCross-check whether a result matches the stated method or assumptionsGenerate a validation checklist for an analysis workflowIdentify possible sources of bias, confounding, or errorReview examples of flawed outputs and discuss how to catch them
1 session on April 8, 2027
Focus: Using AI to communicate research clearly to different audiences.
Description:
This session would explore how AI can help researchers translate technical work into accessible and audience-appropriate communication. Participants would practice adapting the same core findings for different settings, such as collaborators, community partners, students, leadership, or public-facing summaries.
Possible hands-on activities:
Turn a technical summary into plain-language prose
Draft a short abstract, presentation blurb, or project update
Reframe the same findings for academic vs. non-academic audiences
Generate slide outlines or talking points from key results
Create a first draft of documentation, FAQs, or knowledge-transfer notes
Description:
This session would explore how AI can help researchers translate technical work into accessible and audience-appropriate communication. Participants would practice adapting the same core findings for different settings, such as collaborators, community partners, students, leadership, or public-facing summaries.
Possible hands-on activities:
Turn a technical summary into plain-language prose
Draft a short abstract, presentation blurb, or project update
Reframe the same findings for academic vs. non-academic audiences
Generate slide outlines or talking points from key results
Create a first draft of documentation, FAQs, or knowledge-transfer notes