باحثون تربويون
التعليم والتدريبالتعرّض للذكاء الاصطناعي
- مصدر البيانات: BLSتاريخ النشر: 2026-08
مرتفع جدًا· نسبي
منخفضأربع فئات نسبيةمرتفع جدًاقيمة على مستوى المجموعة المهنية
المقياس والأساس والمصدر
أربع فئات نسبية (منخفض / متوسط / مرتفع / مرتفع جدًا)
831 مهنة تفصيلية في جدول توقعات التوظيف لدى BLS. وتُسند القيمة على مستوى رمز مصفوفة التوظيف الوطنية (NEM)، فالمهن التي تشترك في الرمز نفسه تأخذ النطاق نفسه
- مصدر البيانات: Anthropicتاريخ النشر: 2026-03
0.033
0.000نطاق القيم المعروضة هنا0.745المقياس والأساس والمصدر
- مصدر البيانات: ILOتاريخ النشر: 2025
0.59
0.09نطاق القيم المعروضة هنا0.70قيمة على مستوى المجموعة المهنية
المقياس والأساس والمصدر
مؤشر التعرّض للذكاء الاصطناعي التوليدي، 0–1 كما نُشر
وحدة التصنيف ISCO-08 — تأخذ كل المهن ذات الرمز نفسه هذه القيمة
محسوب من قِبل هذا الموقع وليس منشورًا من منظمة العمل الدولية: من بين 1,012 مهنة يربطها هذا الموقع بمجموعة بيانات المنظمة، تبلغ نسبة التي تساوي هذه القيمة أو تتجاوزها 5%.
ما نوع القيمة التي ينشرها هذا المصدر
فئة BLS رتبة نسبية لا مستوى مطلق، وليست قياسًا مباشرًا: فهي تجمع الرتب المئينية للمهنة في عدة دراسات منشورة في أربع فئات. وهي ليست توقّعًا للتوظيف أو الأجور، ولا احتمالًا للتبني، ولا تفرّق بين الأتمتة والتعزيز.
Task-level exposure
المهام ذات التعرض فقط| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Prepare or review engineering studies or specifications.19-3099 | 0.005527.9 | 0.003740.9 |
Prepare reports or recommendations on transportation planning.19-3099 | 0.003417.3 | —0 |
Review development plans for transportation system effects, infrastructure requirements, or compliance with applicable transportation regulations.19-3099 | 0.002814.2 | —0 |
Design transportation surveys to identify areas of public concern.19-3099 | 0.002713.7 | —0 |
Prepare necessary documents to obtain project approvals or permits.19-3099 | 0.00199.6 | —0 |
Define regional or local transportation planning problems or priorities.19-3099 | 0.00189.1 | —0 |
Develop computer models to address transportation planning issues.19-3099 | 0.00168.1 | —0 |
Analyze information from traffic counting programs. | —0 | 0.005459.1 |
| Not observed on any surface — 14 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Represent jurisdictions in the legislative or administrative approval of land development projects. | —0 | —0 |
Direct urban traffic counting programs. | —0 | —0 |
Develop or test new methods or models of transportation analysis. | —0 | —0 |
Analyze transportation-related consequences of federal and state legislative proposals. | —0 | —0 |
Produce environmental documents, such as environmental assessments or environmental impact statements. | —0 | —0 |
Participate in public meetings or hearings to explain planning proposals, to gather feedback from those affected by projects, or to achieve consensus on project designs. | —0 | —0 |
Document and evaluate transportation project needs and costs. | —0 | —0 |
Develop design ideas for new or improved transport infrastructure, such as junction improvements, pedestrian projects, bus facilities, and car parking areas. | —0 | —0 |
Analyze and interpret data from traffic modeling software, geographic information systems, or associated databases. | —0 | —0 |
Collaborate with engineers to research, analyze, or resolve complex transportation design issues. | —0 | —0 |
Recommend transportation system improvements or projects, based on economic, population, land-use, or traffic projections. | —0 | —0 |
Analyze information related to transportation, such as land use policies, environmental impact of projects, or long-range planning needs. | —0 | —0 |
Define or update information such as urban boundaries or classification of roadways. | —0 | —0 |
Collaborate with other professionals to develop sustainable transportation strategies at the local, regional, or national levels. | —0 | —0 |
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.
| Task | βE1 + 0.5 × E2 |
|---|---|
Develop computer models to address transportation planning issues.O*NET Task ID 16948 | 1.0 |
Prepare necessary documents to obtain planned project approvals or permits.O*NET Task ID 20979 | 1.0 |
Prepare or review engineering studies or specifications.O*NET Task ID 16934 | 0.5 |
Represent jurisdictions in the legislative or administrative approval of land development projects.O*NET Task ID 16935 | 0.5 |
Direct urban traffic counting programs.O*NET Task ID 16937 | 0.5 |
Develop or test new methods or models of transportation analysis.O*NET Task ID 16938 | 0.5 |
Define or update information such as urban boundaries or classification of roadways.O*NET Task ID 16939 | 0.5 |
Analyze information from traffic counting programs.O*NET Task ID 16941 | 0.5 |
Review development plans for transportation system effects, infrastructure requirements, or compliance with applicable transportation regulations.O*NET Task ID 16942 | 0.5 |
Prepare reports or recommendations on transportation planning.O*NET Task ID 16943 | 0.5 |
Participate in public meetings or hearings to explain planning proposals, to gather feedback from those affected by projects, or to achieve consensus on project designs.O*NET Task ID 16945 | 0.0 |
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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page