Community survey of power-and-energy researchers and practitioners finds barriers to running AI models are nearly universal, affecting even experienced users¶
Claim · All claims
Evidence · 1 study · associationalr1·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2031: baselines and conditions change
Subclaims¶
Reading the evidence codes
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q2 i? 92% of survey respondents reported at least one barrier before running an AI model, and 95% of respondents who had already completed AI projects still reported a barrier. → Junjie Yin 2026
Evidence¶
Junjie Yin 2026¶
Junjie Yin, Buxin She, Xinyu Feng, Fangxing (Fran) Li. (2026). Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework. arXiv preprint. https://arxiv.org/abs/2608.02599 doi:10.48550/arxiv.2608.02599
q2 · i? · associational · r1
Targeted nine-question community survey with 52 valid responses among power-and-energy researchers and practitioners. The aggregate barrier finding shows persistence even among experienced users: "95%still reported a barrier" among those with completed AI projects.
"In aggregate, 92%of respondents reported at least one barrier, and among the39respondents who had already completed one or more AI projects,95%still reported a barrier."
Discussion¶
Related Claims¶
- Practitioners hold near-universal commitments to human oversight and critical evaluation of AI outputs — related
- Most surveyed power-and-energy respondents would use a power-specific hands-on AI tutorial, and few rate generic image-based AI examples as very relevant — related
- Institutional barriers to AI integration — ethics concerns, insufficient training, policy gaps, and infrastructure limits — are widespread and co-occurring — related
- Academic integrity, professional development, and equity concerns about AI span all surveyed units, though interpreted differently — related
- Infrastructure and access constraints were reported in 18 of 54 studies (33%) as the second higher-order technical limitation category — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
survey-ai-barriers-power-community-nearly-universal |
title |
Community survey of power-and-energy researchers and practitioners finds barriers to running AI models are nearly universal, affecting even experienced users |
description |
Community survey of power-and-energy researchers and practitioners finds barriers to running AI models are nearly universal, affecting even experienced users |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2031 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — junjie-yin-2026 |
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