शिक्षा नीति विश्लेषक
शिक्षा और प्रशिक्षणAI एक्सपोजर
- डेटा स्रोत: BLSप्रकाशन: 2026-08
बहुत उच्च· सापेक्ष
कमचार सापेक्ष श्रेणियाँबहुत उच्चव्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
चार सापेक्ष बैंड (कम / मध्यम / उच्च / बहुत उच्च)
BLS रोज़गार पूर्वानुमान तालिका के 831 विस्तृत व्यवसायों के आधार पर। मान NEM (नेशनल एम्प्लॉयमेंट मैट्रिक्स) कोड के स्तर पर दिया जाता है, इसलिए एक ही NEM कोड वाले व्यवसायों को वही बैंड मिलता है
- डेटा स्रोत: Anthropicप्रकाशन: 2026-03
0.033
0.000यहाँ दिए गए मानों की सीमा0.745मापक्रम, आधार और स्रोत
- डेटा स्रोत: ILOप्रकाशन: 2025
0.47
0.09यहाँ दिए गए मानों की सीमा0.70व्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
जेनरेटिव AI एक्सपोजर सूचकांक, प्रकाशित रूप में 0–1
ISCO-08 यूनिट समूह — समान कोड वाले सभी व्यवसायों को यही मान मिलता है
यह इस साइट की गणना है, ILO द्वारा प्रकाशित आँकड़ा नहीं: इस साइट द्वारा ILO डेटासेट से जोड़े गए 1,012 व्यवसायों में से 23% इस मान के बराबर या उससे अधिक हैं।
यह स्रोत किस प्रकार का आँकड़ा प्रकाशित करता है
BLS की श्रेणी सापेक्ष रैंक है, निरपेक्ष स्तर नहीं, और यह प्रथम-हस्त माप भी नहीं है: यह कई प्रकाशित अध्ययनों में व्यवसाय की पर्सेंटाइल रैंकों को चार बैंडों में बांटती है। यह रोज़गार या वेतन का पूर्वानुमान नहीं है, न अपनाए जाने की संभावना, और यह स्वचालन और संवर्धन में अंतर नहीं करती।
Task-level exposure
| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Interpret data from traffic modeling software, geographic information systems, or associated databases.19-3099 | 0.00000.0 | 0.00000.0 |
Prepare necessary documents to obtain planned project approvals or permits.19-3099 | 0.00000.0 | 0.00000.0 |
Prepare or review engineering studies or specifications.19-3099 | 0.00000.0 | —0 |
Produce environmental documents, such as environmental assessments or environmental impact statements.19-3099 | 0.00000.0 | —0 |
Evaluate transportation project needs or costs.19-3099 | 0.00000.0 | —0 |
| Not observed on any surface — 17 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. | ||
Direct urban traffic counting programs. | —0 | —0 |
Develop or test new methods or models of transportation analysis. | —0 | —0 |
Define or update information such as urban boundaries or classification of roadways. | —0 | —0 |
Analyze information from traffic counting programs. | —0 | —0 |
Review development plans for transportation system effects, infrastructure requirements, or compliance with applicable transportation regulations. | —0 | —0 |
Prepare reports or recommendations on transportation planning. | —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 |
Develop computer models to address transportation planning issues. | —0 | —0 |
Design transportation surveys to identify areas of public concern. | —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 |
Define regional or local transportation planning problems or priorities. | —0 | —0 |
Analyze information related to transportation, such as land use policies, environmental impact of projects, or long-range planning needs. | —0 | —0 |
Collaborate with other professionals to develop sustainable transportation strategies at the local, regional, or national level. | —0 | —0 |
Design new or improved transport infrastructure, such as junction improvements, pedestrian projects, bus facilities, or car parking areas. | —0 | —0 |
Evaluate transportation-related consequences of federal or state legislative proposals. | —0 | —0 |
Represent jurisdictions in the legislative or administrative approval of land development projects. | —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