Are you a UX researcher or designer curious about using AI in your research workflow, but not sure where to begin, or wondering how others are really using it?
Well, here’s a candid, behind-the-scenes look at how we’re using AI in UX projects in the form of a research diary!
We’re sharing what worked, what didn’t, where the tools fell short, and how we worked around the gaps. Think of it as a human-first view of AI in UX research, full of honest reflections, practical tips, and the creative ways we’ve made these tools work for us.
Whether you’re just starting to explore AI or already experimenting with tools like ChatGPT, Gemini, or Perplexity, this research diary is meant to help you make smarter, faster, and more impactful research decisions — without replacing the human touch.
In this Article
Why You Should Read This Article
This article is perfect for:
- UX professionals and researchers who want to level up their workflow with AI.
- Designers and product strategists interested in real examples of how AI fits into the research process.
- Anyone new to AI tools, but eager to unlock their potential in a user-centric, practical way.
If you’re tired of vague overviews and want something practical, nuanced, and experience-based — you’re in the right place. This article is a field-tested, hands-on breakdown of how to actually use AI in your research. And we’re sharing exactly how we do it.
What You’ll Gain From Reading This Article
By accessing the full content, you’ll get:
- A phase-by-phase look at how we used AI tools across a UX research project, from planning to analysis.
- Prompt examples and tips to help you get better results (and avoid AI dead ends).
- Real talk on AI’s limitations and how to work alongside it, not blindly trust it.
Ready to Dive In?
Access the full article and:
✅ Learn how to use AI in a way that’s smart, grounded, and collaborative.
✅ Explore tested workflows, prompt templates, and workarounds for common pitfalls.
✅ Get inspired to experiment — and make AI your research sidekick, not your replacement.
👇 Fill out the form below to unlock the full article and discover a more human way to work with AI in UX research.
Not Convinced Yet?
Here’s a sneak peek into how you can use AI in UX Research
In this research diary, we don’t just explore how to use AI, we take you behind the scenes into how it actually performs in a UX research project. This is a hands-on, human-centered look at where AI shines, where it struggles, and how to bridge those gaps with your own expertise.
Here’s a peek at how we used AI in creating a research plan, often the most time-consuming step:
Phase 1: Project Planning – Laying the Groundwork with Gemini
Let’s start with Phase 1 – project planning. My first interaction with Gemini involved creating a research plan. I provided it with the specifics of the project: my role, the objectives, the target participants (internal colleagues in this case), and the expected deliverables. I also uploaded a sample of a past user persona document as a reference point.
Efficiency Boost: Initial Output Generation
As you can already guess, Gemini is capable of almost immediately generating initial project outlines and plans, accelerating the beginning stages. This turnaround was undeniably faster than your usual manual process. All I had to do was transfer the output to a new file for documentation purposes.
If there is anything to take note of, it is essential for the UX researcher to be clear about research objectives and needs for effective AI utilization, which we will talk about next.
We also covered how AI can keep up with real-world Malaysian conversations where different languages are often found in one sentence. We explored Google Meet’s live transcription feature and later used Gemini to summarize these transcription. We saw first hand how AI handles messy, multilingual input — and the results were unexpected.
AI-Powered Contextual Understanding in Post-Interview Analysis
An additional pleasant discovery was AI’s ability to understand Malaysian’s habit of mixing multiple languages within a single sentence. As a company based in Malaysia, which is a multicultural and multilingual country where “bahasa campur” (mixed language) is a common way of speaking, we’re used to blending words from Malay, English, and sometimes other languages in everyday conversations. Curious to see how well Google Meet’s transcription could handle this, I intentionally asked a colleague to mix Malay words into their interview responses.
Surprisingly, while Google Meet’s transcription struggled and transcribed some Malay words into gibberish, rendering kampung (village) as “kong kong”, and pisang goreng (banana fritters) as “goring pis”, Gemini was able to infer the intended meaning in its summary.
For instance, it correctly picked up that my participant was referring to rural areas, despite the error in the transcript being written as “kong kong”. This discovery was indeed a nice surprise, demonstrating Gemini’s contextual understanding of mixed-language communication, a reality deeply embedded in the Malaysian experience.
“Kong Kong” to kampung: Even with transcription errors, Gemini gets the context.
These are just two highlights into the research diary!
The full article walks you through every major phase of a UX Research project, complete with tools used, prompts, limitations and strategies to get the most out of AI without losing your own critical thinking.
👉 Ready to see the full breakdown? Drop your details here and unlock the complete article.
💡 Not quite ready to dive into the full research diary just yet?
You can still explore the big-picture ideas in a lighter, high-level overview. It covers key limitations of using AI in UX research and how to work around them — phase by phase. Read it here.
