Many of us feel like using AI in our work is a "dance with the devil"—powerful, but unpredictable and a little scary. After years of trial and error, I've found the key isn't to follow, but to lead the dance. In this guide, I share my CRAFTe framework for writing better prompts and the ethical principles we need to keep our soul in the process.
The future of UX Research with AI: utopian dream or dystopian nightmare? 😬👉🏻🤯
As posited on the panel with Sam Ladner, PhD, and Jared Forney at Dovetail’s Insight Out conference last month, Generative AI is forcing us to confront a critical question: Will we, as Researchers, create and define our collective future, or risk it being defined for us?
I've applied the "best/worst/base case" foresight framework to explore three potential paths forward for the Product & Design Research industry, from the "Ambling Along Status Quo" to the "Empowered Research Engine."
It's not about predicting, but preparing. Let's discuss our direction and how we can get there intentionally.
Dive into the article to explore these futures and share your thoughts! 👇🏻
Last month, I utilized generative AI to analyze 20 hours of interviews in just 14 hours (instead of over 80). The efficiency is mind-blowing... but at what cost and what opportunity? According to a recent HBR article, 77% of researchers are concerned about AI bias, and only 31% rating AI-generated data as "great," we're facing a critical inflection point in our field.
In the article, I note:
• 3 game-changing opportunities AI brings to UX research
• 3 serious challenges we can't ignore
• Thoughts on the impact and future of research
As researchers, what's our responsibility in this AI revolution?
What's your take: Will AI fundamentally enhance or erode the quality of UX research?
So I did a little experiment with ChatGTP...name me 100 of the most influential BIPOC women in the past 100 years? It's answer highlighted the need to intentionally and consciously train AI with a diverse and representative dataset.
