AI in User Research: Tools, Use Cases and Best Practices
summary
Practical use of AI tools in user research: a pipeline using a local Notebook LM to upload and cluster 12 hours of audio, then ChatGPT to synthesize insights and quantify phenomena across 100+ interviews, delivering insights in two days. Discusses concrete benefits (faster turnaround and workable reports compared with 2024) and practical pitfalls—audio noise causing false transcripts, the need for strict prompts and note-taking, limits with screen analysis, and the continued need for human verification.
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chapters (10)
- 0:00AI in user research
- 1:27User research challenges
- 3:11Research process with AI
- 4:16Case study: audio analysis with Notebook LM
- 8:58Notebook LM limitations
- 10:04Usability testing and AI synthesis
- 13:57Screenshot analysis strengths and limits
- 16:27AI vs non-AI research comparison
- 18:33Strategic value of AI in research
- 20:18Best practices and ethics
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