A few weeks ago, my 82-year-old mom got an Apple Watch for health and safety reasons. My 11 year-old daughter, Que, and I wanted to surprise her with a more fashionable band, but we didn’t know the model—which mattered because different bands fit different models.
To keep it a surprise, Que FaceTimed my mom and casually chatted while trying to identify the model visually. When she couldn’t, she took a screenshot of the call, uploaded the image into ChatGPT on my laptop, then texted me the model details from her iPad. Done. No Googling. No asking me. No tutorials. No overthinking. Just a visual question, a workflow that made sense to her, and the right tool at the right moment.
She solved a real-world problem with AI, completely on her own, at age 11.
From “using AI” to “thinking with AI”
Que didn’t think, “I should try AI for this.” She thought, “I need to solve this.”
That subtle but powerful difference stayed with me—not just as a mom, but as a researcher.
As adults, we often evaluate whether or not a tool is appropriate for a task and, if so, which is the most appropriate. Que didn’t make that distinction. She just used what felt fastest and most intuitive. This is the defining trait of AI-Natives: they don’t adopt AI, they inhabit it. For them, AI isn’t a tool you turn on or off. It’s part of how they think and move through the world.
What we miss when we study AI like adults
Most of today’s AI adoption studies are built for pre-AI minds. We focus on awareness, onboarding, and perceived utility. That works fine for people easing into AI tools. But it fails to capture the intuitive fluency of AI-Natives like Que.
Here’s what we often miss:
- Invisible integration: They don’t think of AI as a separate tool or workflow. It’s baked into how they solve problems.
- Visual-first thinking: Text-based prompts aren’t the default. Screenshots, voice input, and multimodal questions are taking center stage.
- Workflow fluency: They naturally combine tools—FaceTime to screenshot to ChatGPT to text—without labeling it automation or a process. They don’t always follow the same order of operations—some steps stick, others fade depending on the ultimate goal.
- Unconstrained creativity: They haven’t spent decades adapting to legacy tools. They aren’t boxed in by old UX patterns or assumptions.
To keep up with AI Natives, we don’t need more rigid models. We need more openness. Instead of asking AI-Natives if they’re using AI, we should ask: What are they trying to do, and what can we learn from the way they do it?
For example, Que’s goal ultimately wasn’t to identify a watch model. The goal was to surprise her grandmother with a thoughtful gift. My daughter used a multimodal AI workflow to do something kind. And it was remarkably quick and intuitive to her. That’s the part we, as researchers, often overlook when we focus too much on the mechanics.
How art school taught me to stay open
Before I became a design researcher, I went to art school. Every class, we put our work on the wall for group critiques, aka “crits.” These sessions taught me to focus on intention over medium—it didn’t matter whether you used charcoal or collage, what mattered was the story you were trying to tell.
I walked away with the critical analysis skills that make me a great researcher: the ability to detach my ego from my output and respond to feedback with confidence rather than defensiveness. Crits taught me that observation often reveals more than interrogation. The best feedback always came from watching how people actually responded to my work rather than explaining my intentions or asking them to explain their reactions. This lesson shaped how I approach research today.
Observation over interrogation
So how do we study a generation that isn’t consciously using AI, but is becoming deeply fluent in it? Start by remembering that the reason why this generation is adopting it is still for fundamentally human reasons: connection, curiosity, creativity, care.
With that in mind, we can fall back on the fundamentals of critical analysis I learned in art school: Observation over interrogation.
Rather than basing user testing questions for AI natives around how they’d solve a problem—ask them to show you how they would approach it—even better if it’s in the environment they’d naturally be in. Let them use their own devices in their own contexts. You’ll learn more by letting them lead. Besides, most of what’s insightful often happens between the tools, not within them.
This mentality aids inclusion and accessibility as well. For many neurodiverse users, AI isn’t just helpful—it’s freeing. It bypasses the traditional friction of navigation and information processing. Users don’t have to search, scan, or decode long lists of product SKUs or dense instructions. They can show instead of describe. Ask instead of navigate. That kind of accessibility unlocks independence and creativity in ways that text-based tools often don’t.
How to structure research for AI natives
This easy-to-read checklist can be used as a jumping off point when planning research with AI-natives in mind:
The next generation won’t wait for onboarding tutorials or product marketing
They’re already miles ahead, solving problems creatively, intuitively, and in ways we don’t always see. Some of the best researchers of tomorrow are already teaching us today: One screenshot, one prompt, one unfiltered instinct at a time. We just need to learn to sit back and watch.
More Resources on AI and Research
- AI Broke Brains and Research Protocols—Here's How Researchers Can Adapt
- The Next Decade of Fraud in User Research: A Guide to Staying Ahead
- 20+ AI Tools for Every Phase of UX Research
- The 2024 AI in UX Research Report
- The Early Adopter's Guide to AI Moderation in UX Research
- How a UX Researcher Uses AI in Their Daily Workflow
