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Federal data × LLM scoring

AI Career Stats

Speech-Language Pathologists: daily tasks & AI impact

This page breaks Speech-Language Pathologists into the top 15 daily O*NET tasks and scores each for Generative AI automation probability. Those task scores roll up to an overall vulnerability index of 36/100 (Moderate automation risk). Struck-through rows are ≥80% automatable; highlighted safer rows are under 30% or primarily physical.

0 highly automatable 0 safer / physical Back to risk score Alternative careers

High-automation exposure

No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.

Durable human work

This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.

Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.

Task Importance AI probability Nature
Evaluate hearing or speech and language test results, barium swallow results, or medical or background information to diagnose and plan treatment for speech, language, fluency, voice, or swallowing disorders. Core 4.88
Write reports and maintain proper documentation of information, such as client Medicaid or billing records or caseload activities, including the initial evaluation, treatment, progress, and discharge of clients. Core 4.87
Monitor patients' progress and adjust treatments accordingly. Core 4.84
Develop or implement treatment plans for problems such as stuttering, delayed language, swallowing disorders, or inappropriate pitch or harsh voice problems, based on own assessments and recommendations of physicians, psychologists, or social workers. Core 4.84
Administer hearing or speech and language evaluations, tests, or examinations to patients to collect information on type and degree of impairments, using written or oral tests or special instruments. Core 4.81
Educate patients and family members about various topics, such as communication techniques or strategies to cope with or to avoid personal misunderstandings. Core 4.68
Supervise or collaborate with therapy team. Core 4.65
Participate in and write reports for meetings regarding patients' progress, such as individualized educational planning (IEP) meetings, in-service meetings, or intervention assistance team meetings. Core 4.62
Teach clients to control or strengthen tongue, jaw, face muscles, or breathing mechanisms. Core 4.46
Instruct clients in techniques for more effective communication, such as sign language, lip reading, or voice improvement. Core 4.46
Consult with and advise educators or medical staff on speech or hearing topics, such as communication strategies or speech and language stimulation. Core 4.43
Develop speech exercise programs to reduce disabilities. Core 4.43
Complete administrative responsibilities, such as coordinating paperwork, scheduling case management activities, or writing lesson plans. Core 4.31
Consult with and refer clients to additional medical or educational services. Core 4.21
Design, develop, or employ alternative diagnostic or communication devices or strategies. Core 4.12

What remains human

No tasks currently fall under the safe threshold for this occupation.

FAQ

Which Speech-Language Pathologists tasks are most exposed to AI?

No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.

Which Speech-Language Pathologists tasks are safest from Generative AI?

This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.

How should I read the Speech-Language Pathologists task table?

Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.

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