Cuestionario vinculado a una guía · Medio Nivel
Cuestionario de IA para investigadores de UX
Prueba la IA en la investigación de UX: límites de usuarios sintéticos, verificación de citas y etiquetas de libros de códigos, medición del acuerdo del codificador y por qué los resúmenes no son sintéticos.
Pregunta 1 de 8
What is the core limitation of synthetic users?
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Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.
- What is the core limitation of synthetic users?
- Son demasiado caros para ejecutar
- They give plausible, averaged answers from training patterns rather than lived experience
- They can only answer in English
- They always refuse to answer interview questions
- Which is a reasonable use of synthetic users according to the guide?
- Replacing all usability testing
- Reporting findings to executives as customer evidence
- Piloting a discussion guide to find confusing questions
- Setting product pricing
- Why should researchers ask AI for verbatim quotes with timestamps?
- So each quote can be checked against the source, since models can misattribute or invent quotes
- Because timestamps make reports look more professional
- Because AI cannot summarize without timestamps
- To shorten the transcripts
- What does Cohen's kappa measure in AI-assisted tagging?
- The speed of transcription
- The number of themes in a dataset
- The sentiment of each comment
- Agreement between model labels and human coding, adjusted for chance
- Why is an AI summary not the same as research synthesis?
- Synthesis weighs evidence, notices contradictions and ties findings to decisions, while summaries can smooth over outliers
- Summaries are always longer than synthesis
- Synthesis can only be done on paper
- no hay diferencia
- Why can't synthetic users replace usability testing specifically?
- Usability tests require video cameras
- They cannot show what people actually do, only generate what someone might say
- Usability testing is illegal with AI
- Synthetic users are too critical of designs
- When should a codebook for AI tagging be written?
- After the model finishes tagging
- Never, because models create their own codes
- Before the model sees the data, with definitions, examples and counterexamples
- Only if the dataset is under 100 items
- What role do embeddings play in analyzing large feedback sets?
- They encrypt participant data
- They record audio
- They replace the need for a researcher to read feedback
- They represent text numerically so similar comments can be clustered