Tutorial Overview
Focus: Using AI to support data preparation and quality improvement.
This session would examine how AI can help researchers think through data cleaning tasks before formal analysis begins. The emphasis would be on using AI as a support tool to identify inconsistencies, plan, clean 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 or R
- Create a data cleaning log or documentation template
- Compare raw and cleaned data examples to assess impact
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.