#193 - AI won't make you a better UX researcher unless you do the hard work

**Producer's Note:** After eight incredible years of conversations, insights, and yes, a few awkward silences, we're bringing the Awkward Silences podcast to a close after episode 194 (scheduled for Oct. 27th featuring a roundtable discussion on synthetic users). We're incredibly grateful to our guests, listeners and hosts who've been part of this journey. But the conversations don't have to end. We invite you to check out Insights Unlocked, a podcast from Auros, where we explore the people, ideas, and innovations shaping the future of AI, research, digital products, and customer experiences. Find *Insights Unlocked* wherever you listen to podcasts. And from everyone at Awkward Silences, past and present, thank you for listening, learning, and getting curious with us. **Episode summary** In this episode of *Awkward Silences*, Auros' Lija Hogan chats with Sal Criscione, senior UX researcher and research leader, about how he's using AI to sharpen his quantitative research skills through hands-on practice, simulated research scenarios, and continuous learning. Sal shares how he turned AI into an interactive practice tool that generates realistic UX research scenarios and datasets, challenges him to select appropriate statistical methods, and provides feedback on his analysis. By separating learning from practicing, he's developed a repeatable approach to building confidence in quantitative research while making the process engaging and fun. The conversation also explores the risks of relying too heavily on AI, including hallucinations, inaccurate calculations, and its tendency to agree with users rather than challenge them. Sal explains why trusted source materials, independent verification, and critical thinking remain essential—and how teaching others can reveal gaps in your own understanding. You'll learn: - How to use AI to build quantitative UX research skills through hands-on practice - Why separating learning from practicing helps develop deeper understanding - How to create AI-generated research scenarios and datasets to practice statistical analysis - Ways to recognize AI biases, challenge its responses, and verify results - Why trusted sources and independent calculations are essential when learning with AI - How teaching others can strengthen your expertise and expose knowledge gaps Resources & links - Sal Criscione on LinkedIn (https://www.linkedin.com/in/salvatore-criscione/) - Lija Hogan on LinkedIn (https://www.linkedin.com/in/lija-hogan-894769/) - Nathan Isaacs on LinkedIn (https://www.linkedin.com/in/nathanisaacs/) - *Quantifying the User Experience* (book discussed in the episode; https://www.amazon.com/dp/0128023082) - Insights Unlocked podcast (https://www.usertesting.com/resources/podcast) - Auros website (https://aurosglobal.com/)