Integrating AI into My UX Research Workflow
How I leverage Artificial Intelligence as a strategic partner to enhance efficiency and perspective,
while always grounding the work in human-centered expertise.
I. Research Planning
How I Use AI:
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Secondary/Desk Research: Using tools like Perplexity and ChatGPT for quick, data-supported answers and theme identification.
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Grounded Synthesis: Using NotebookLM Pro to synthesize data based on specific uploaded datasets (research notes, transcripts, articles), reducing hallucinations and enabling quick summaries of sources like YouTube videos.
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Competitive & Market Analysis: Leveraging Claude for competitive research, market analysis, social listening, and online ethnography to gain a holistic view of the opportunity landscape.
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Method Exploration: Getting quick rundowns of UX methods and citations.
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Question Generation: Brainstorming initial research questions when blocked.
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Protocol & Script Drafting: Generating draft guides based on project goals for refinement.
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Summarization: Quickly summarizing articles and spotting design trends.
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Brainstorming: Using tools like Notion AI for bouncing around app/concept ideas.
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Why I Use AI Here:
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To save time, broaden perspective, ensure grounding in specific data, and act as a creative thought partner.​
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Where My Expertise Comes In:
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Knowing what to ask and how to frame prompts effectively.
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Selecting and curating the right datasets for grounded synthesis (e.g., in NotebookLM Pro).
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Verifying all AI outputs (especially stats/citations) against hallucinations.
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Defining research objectives, constraints, and context.
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Deciding when and where AI adds value; providing necessary guidance and oversight.
II. Conduct Research
How I Use AI:
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Transcription: Using AI tools to capture session audio, freeing me to focus.
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Live Summarization/Note-Taking: Leveraging AI for real-time summaries or key point highlighting.
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Clip Refinement: Using AI to help edit out filler words (like “um”s) from research video clips for cleaner playback and analysis.
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Why I Use AI Here:
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To automate repetitive tasks, allowing more presence with participants and streamlining post-session processing.
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Where My Expertise Comes In:
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Moderating conversations, reading non-verbal cues, and adapting mid-interview.
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Building empathy and rapport – essential human skills for participant comfort.
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Using judgment and adaptability when unexpected situations arise.
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Ensuring AI editing doesn’t inadvertently remove important nuance or context.
III. Analysis & Synthesis
How I Use AI:
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Thematic Analysis: Using LLMs to surface high-level and sub-themes (often grounded with tools like NotebookLM Pro).
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Pattern Detection: Sifting through large datasets to flag patterns/outliers.
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Process Enhancement: Utilizing AI agents for automating repetitive analysis tasks and providing initial data insights.
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Summarizing Research: Creating early drafts of summaries/findings.
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Predicting Stakeholder Reactions: Prompting AI to anticipate pushback or risks.
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Why I Use AI Here:
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To speed up analysis, explore patterns differently, handle large data volumes, and automate routine steps.
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Where My Expertise Comes In:
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Crafting smart prompts and using techniques like chain-of-thought.
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Evaluating if AI insights are deep enough for decisions or just surface-level.
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Watching for and mitigating bias in AI output.
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Connecting insights to strategic business goals.
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Catching nuances AI misses and using researcher's intuition.
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Deciding what’s important and how to structure the narrative.
IV. Ideation & Design
How I Use AI:
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Idea Generation: Tapping AI for early brainstorming or alternative directions.
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Content Style Guides: Using Claude to help build guides based on personas, values, and insights (including UX writing).
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Mockups & Prototypes: Leveraging tools like Firebase Studio, Lovable, v0 by Vercel, and Gemini 2.5 Pro to rapidly create mockups, working flows, and prototypes from prompts or sketches.
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Video Creation: Using Descript for efficient video editing, audio cleaning, and creating highlight reels or product demos.
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Trend Research: Surfacing current UX/UI trends for inspiration.
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Placeholder Content: Generating quick mock text or data.
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Personalization Insights: Exploring how AI could enable personalized user experiences based on behavior, informing design choices.
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Why I Use AI Here:
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To overcome creative blocks, accelerate the design/prototyping process, and maintain momentum.
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Where My Expertise Comes In:
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Keeping design human-centered, reflecting real user needs over AI suggestions.
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Using deep user understanding from research to evaluate/reject AI ideas and outputs.
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Guiding AI generation tools with clear prompts and iterative refinement.
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Integrating real user feedback iteratively – something AI can’t simulate.
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Communicating design rationale and collaborating cross-functionally.
V. Evaluation & Testing
How I Use AI:
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Drafting Questions/Tasks: Generating rough drafts for surveys or study tasks.
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Simulating Personas: Bouncing ideas off AI personas for quick checks on clarity or potential flaws.
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Qualitative Analysis: Getting a head start on analyzing open-text feedback or post-test comments.
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Why I Use AI Here:
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To speed up test preparation and explore early reactions to concepts efficiently.
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Where My Expertise Comes In:
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Designing effective, appropriate methodologies for research goals.
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Writing unbiased questions and tasks, avoiding AI pitfalls.
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Interpreting data within the correct context of user behavior/motivations.
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Prioritizing issues and guiding the team toward meaningful action based on real user data.
🚫 When I Avoid AI (or Use It With Caution)
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Generating primary research data (AI doesn’t represent actual users).
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Situations where empathy and trust-building are paramount.
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Making high-stakes product decisions based solely on AI insights.
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Sharing sensitive or proprietary information without approved, secure tools.
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Areas where I lack the expertise to critically evaluate the AI’s output.
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💡The Value of My Expertise
AI is fast, scalable, and efficient but it doesn’t replace the depth of critical design thinking. I bring:
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Strategic Thinking: Defining clear research goals, choosing the right methods for each context, and framing problems in ways that align user needs with business outcomes.
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Critical Evaluation: Surfacing deeper meaning, challenging superficial patterns, and identifying what truly matters.
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Empathy and Ethics: Prioritizing user dignity, building trust, and navigating sensitive topics with care.
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Synthesis: Making sense of complexity, connecting insights across data sources, and structuring compelling narratives.
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Storytelling: Translating research into actionable strategy through compelling communication and cross-functional alignment.
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Adaptability: Staying ahead of emerging tools while maintaining focus on timeless human principles.​​