Athletic Trainers

Education & Training

AI exposure

  • Data source: BLSPublished: 2026-08

    Moderate· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.053

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.25

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 77% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Show 12 hidden tasks

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Perform general administrative tasks, such as keeping records or writing reports.

O*NET Task ID 18691

1.0
File athlete insurance claims and communicate with insurance providers.

O*NET Task ID 18692

1.0
Teach sports medicine courses to athletic training students.

O*NET Task ID 18693

1.0
Conduct an initial assessment of an athlete's injury or illness to provide emergency or continued care and to determine whether they should be referred to physicians for definitive diagnosis and treatment.

O*NET Task ID 4221

0.5
Assess and report the progress of recovering athletes to coaches or physicians.

O*NET Task ID 4225

0.5
Collaborate with physicians to develop and implement comprehensive rehabilitation programs for athletic injuries.

O*NET Task ID 4226

0.5
Plan or implement comprehensive athletic injury or illness prevention programs.

O*NET Task ID 4228

0.5
Develop training programs or routines designed to improve athletic performance.

O*NET Task ID 4229

0.5
Inspect playing fields to locate any items that could injure players.

O*NET Task ID 4232

0.5
Conduct research or provide instruction on subject matter related to athletic training or sports medicine.

O*NET Task ID 4233

0.5

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information