ILO Working Paper 140 (2025): Generative AI and Jobs: A Refined Global Index of Occupational Exposure
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗Drama education is embodied, relational, and developmental. The drama teacher is the human guide of a fundamentally human experience — creative expression, vulnerability, and ensemble work. AI is irrelevant to this profession.
Drama teachers teach acting, directing, devising, and theatre history — but more fundamentally, they use theatre practice to develop communication skills, emotional intelligence, confidence, empathy, and the capacity to inhabit other perspectives. This is transformative educational work with the whole person.
AI script analysis tools can help students analyse dramatic texts. AI voice coaching apps can assist with vocal warm-up. But the drama class itself — the improvisation, the ensemble devising, the vulnerability of performance, the growth that comes from being directed by a skilled practitioner — is irreducibly human.
Drama education is experiencing a renaissance in therapeutic and educational applications: drama therapy, Theatre in Education, applied drama in schools and healthcare. Growing demand in all these areas.
These are the genuine threats to this profession. They are real, but they are not sufficient to overturn the fundamental analysis. Here is why.
Put the case that Drama Teacher / Theatre Practitioner will not survive AI displacement. The system responds with counterarguments from the research base. Strong arguments shift the score — up to a maximum of ±15 points. The system is not an AI. It is a structured argument engine.
This question layer is generated from the job verdict, the resistance case, the regional rollout logic, and the evidence status of this page. Use the filters to focus the discussion, or trigger a random question and work through the role from multiple angles.
Replace broad inference with occupation-specific literature, regulators, labour statistics, or professional-body evidence before publication-grade use.
TIER 1 review queue with 6 core sources and 3 framework signals.
This page is grounded in task exposure research and labour-market trend reports, then translated into a reasoned occupation-level argument.
This site now treats exact timelines, total job-loss counts, and regional speed as interpretive estimates unless a cited source states them directly. The argument on this page should be read as a structured forecast, not a guaranteed future.
These impact figures are site estimates for comparison and should not be read as official labour-market counts.
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗Finds clerical work is the most highly exposed occupational group and that augmentation is often more likely than full occupation automation.
OPEN SOURCE ↗Shows AI exposure is highest in many white-collar cognitive occupations, while manual occupations tend to have lower exposure.
OPEN SOURCE ↗Advanced economies are more exposed to AI because they have more cognitive-intensive jobs; infrastructure and skills limit adoption elsewhere.
OPEN SOURCE ↗Large-employer survey showing clerical roles among the fastest-declining and care, education, software and green-transition jobs among growth areas.
OPEN SOURCE ↗Argues advanced economies are better positioned to benefit from AI due to infrastructure, skills, and institutions.
OPEN SOURCE ↗