How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Compile, administer, and grade examinations, or assign this work to others.76
- Prepare course materials, such as syllabi, homework assignments, and handouts.76
- Perform administrative duties, such as serving as department head.76
- Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.76
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.01
- Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media.02
- Plan, evaluate, and revise curricula, course content, course materials, and methods of instruction.03
- Compile, administer, and grade examinations, or assign this work to others.04
- Prepare course materials, such as syllabi, homework assignments, and handouts.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
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Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
