In this tutorial you will learn:

• The promises, limitations, and professional implications of AI research tools
• Cognitive and social dynamics of automations and human–AI interaction
• The core drivers of value and creative adaptation in ethnographic work
• A practical framework for deciding when and how your team should integrate AI research tools

As AI research tools and the pressure to use them proliferate, it’s time to evolve our tech stacks. Even more urgently, it’s time to evolve our ‘thinking stacks’ so we can ensure the quality, value, and future of our work.

This tutorial provides knowledge and frameworks to lead strategic conversations and make decisions about how and when to use AI tools – rather than just reacting to technological trends. This adaptable ‘thinking stack’ is based on the capabilities of the technologies, the core drivers of value in ethnographic practice, and a creative and culturally engaged approach to experimentation. We will cover:

Mapping the AI Landscape

• The promise and limitations of AI for research and insights
• New findings about the cognitive and sociocultural dynamics of human-AI interaction
• Professional pressures and the evolving market for research and ethnographic work

Mapping the Core Drivers of Ethnographic Value

• Framing problem spaces and research questions
• Using participation, relationality, and multimodal data to create and activate deeper insights
• Learning from diverse cultures and strategies for creating, adapting, resisting socio-technologies

Using and Customizing a Framework for AI Integration

• Learn a framework that identifies the core layers of our ‘thinking stack’
• Apply and customize the framework to specific use cases, teams, and organizations
• Use the framework to lead strategic conversations and equip teams to make good decisions

Instructor

Lindsey DeWitt Prat is a Director at Bold Insight, where she leads global UX research initiatives to help teams understand how cultural and linguistic dynamics shape technology use. A humanities-trained ethnographer, author, and translator with more than 15 years of research experience bridging academia and industry, she brings cultural insights to projects spanning 25+ countries, with particular depth in Japan and East Asia. Her recent research focuses on pathways to making AI more inclusive and culturally resonant through deep, contextual understanding of people and society. Most of her published work explores gender exclusion, cultural heritage, and religion in Japan through a combined ethnographic and historical approach. Lindsey holds a PhD in Asian Languages & Cultures from UCLA and an MA in International Studies and Comparative Religion from the University of Washington. She is a 2025–2026 AI for Developing Countries Forum (AIFOD) Senior Fellow.