Tutorial Overview
Focus: Using AI to improve access to, understanding of, and readiness for working with data.
This session would focus on helping researchers navigate the early stages of working with data, especially when datasets are large, complex, poorly documented, or unfamiliar. AI can assist with interpreting data dictionaries, summarizing metadata, identifying missing documentation, and making datasets more approachable for new users.
Possible hands-on activities:
- Summarize a dataset description or codebook into plain language
- Generate a checklist for evaluating whether a dataset is usable for a project
- Identify likely data limitations or missing documentation
- Draft questions to ask a data provider or collaborator
- Use AI to map dataset fields to possible research questions
About the Series
This tutorial series introduces practical ways researchers can use AI to support common stages of the research workflow. Designed as a hands-on learning experience, the series focuses on approachable, real-world applications rather than abstract theory. Each session will combine brief framing, live demonstrations, and guided practice so participants can explore how AI tools may help with tasks such as refining research questions, working with data, conducting early-stage analysis, checking outputs, and communicating findings responsibly. The goal is to help researchers develop useful habits for integrating AI into their work in thoughtful, transparent, and effective ways.