Moderated Usability Test Script
Write a moderated usability test a researcher can run without leading the participant. STUDY Product: [PRODUCT] Prototype fidelity: [LIVE | FIGMA | PAPER] Research question: [ONE QUESTION] Tasks that answer it: [TASK_1], [TASK_2], [TASK_3] Participant profile: [WHO] Session length: [30 | 45 | 60] minutes What we must not prime them with: [FEATURE NAMES OR OUR HYPOTHESIS] Success for each task: [HOW WE WILL KNOW] Device: [DESKTOP | MOBILE] SCRIPT 1) Intro (consent, recording, "we are testing the design, not you", think-aloud instructions). Include the exact line that gives them permission to struggle. 2) Warm-up, 2 questions about their current behavior, not about our product. 3) For each task: the scenario in the participant's language (no UI nouns), the starting state, when to start the timer, probes that do not give away the path ("what are you looking for?"), and the success / fail / partial criteria. 4) Post-task rating: one question, the scale, and why we are not asking "did you like it?" 5) Debrief, 4 questions, hardest first while memory is fresh. 6) Moderator notes column: what to write down (quotes, misclicks, time, where they hesitated). ALSO - A screener of 6 questions that filters for the profile and excludes people who work in [OUR INDUSTRY] or have seen the prototype. - Observer instructions: when they may speak (they may not). - A findings template: issue, severity (blocks task / slows task / cosmetic), quote, recommendation hypothesis. Empty, ready to fill. Do not invent findings. Never put the button name in the task instruction.
🌟 Example Output / Preview
Prompt Metadata
Primary Use Cases:
- •Content marketing campaigns & professional copywriting
- •Social media presence & audience building
- •Business communications & strategic summaries
Associated Tags:
💡 Pro Tips & Advice
1. Use bracketed items: Be sure to fill out all [PLACEHOLDER] elements with specific details before sending the prompt to the AI model.
2. Adjust temperature: For creative tasks, set AI temperature higher (e.g., 0.8), or lower (e.g., 0.2) for strict coding/technical tasks.
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