Cuestionario vinculado a una guía · duro Nivel
Cuestionario de IA y empleos
Evalúa la exposición a la tarea, la incertidumbre en la adopción, el aumento, la carga de trabajo de revisión, la agencia de los trabajadores, la calidad del trabajo y la evidencia del mercado laboral local.
Pregunta 1 de 8
Una ocupación tiene una puntuación alta de exposición a la IA. ¿Qué establece esa puntuación?
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Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.
- Una ocupación tiene una puntuación alta de exposición a la IA. ¿Qué establece esa puntuación?
- Posible superposición de tareas, no una previsión directa de pérdida de empleo
- Que cada trabajador será reemplazado
- ¿Cuántos nuevos empleos se crearán?
- La fecha en que un lugar de trabajo adoptará la IA
- Why analyze tasks within a job rather than treating the occupation as one unit?
- Every task in an occupation has the same technical requirements
- A role combines tasks that may be automated, augmented, or still need human context
- Occupation titles fully describe daily work
- One automated task means the role can be removed
- A model can draft a report. Why might an organization still not adopt it?
- Technical feasibility guarantees deployment
- Only model size determines adoption
- Reliability, cost, integration, regulation, and work choices affect adoption
- Worker training is irrelevant
- A tool speeds up one stage but adds a large review queue. What should be evaluated?
- Only generation speed
- The number of prompts used
- The model’s benchmark score alone
- Total work and job quality after review and correction are included
- Which statement distinguishes augmentation from replacement?
- A system may assist one workflow while automating part of another
- Every AI use either fully replaces or never changes a job
- Augmentation means no work changes
- Replacement can be inferred from task exposure alone
- What labor-market evidence should inform a career decision?
- A global exposure estimate used as a personal forecast
- Current information for the specific location or occupation
- A single company’s product launch
- An old job title list without a date
- Which capability can help workers evaluate AI-supported tasks?
- Prompt memorization without subject knowledge
- Avoiding all review responsibility
- Domain knowledge, output evaluation, exception handling, and clear communication
- Choosing a model by parameter count
- Why should workers be able to challenge inaccurate AI outputs?
- A challenge process guarantees no errors
- Only managers can detect mistakes
- A model recommendation removes human accountability
- They may carry error responsibility and need a way to correct outcomes