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¶

Claim · All claims
Evidence · 1 study (2 entries) · design r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 2 of 2 entries) · not reviewed by a person

Subclaims¶

q2 i? 65.4% of respondents said yes and a further 28.8% said maybe to a one-click power-specific hands-on AI tutorial, a combined 94.2%. → Junjie Yin 2026 q2 i? Only 11.5% of respondents rated generic image-based AI examples such as MNIST as very relevant to power-system applications, while 53.8% were neutral-to-negative. → Junjie Yin 2026 (2)

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? · design · r2

Community survey of 52 power-and-energy researchers and practitioners measuring interest in a tailored tutorial. Stated willingness was high: "a combined94.2%" answered yes or maybe to a one-click hands-on AI tutorial designed for power and energy systems. This measures willingness to use such a tutorial, not a preference over other materials.

"When asked whether they would use a one- click, hands-on AI tutorial designed specifically for power and energy systems,65.4%answered “yes” and a further 28.8%answered “maybe,” for a combined94.2%(Table I)."

Junjie Yin 2026 (2)¶

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

q2 · i? · design · r2

Survey question on perceived relevance of image-based examples (e.g., MNIST) to power-system applications. Generic computer-vision exemplars were seen as weakly relevant: "only11.5%of respondents rated them “very relevant,”" with open-ended responses echoing this gap. The item measured perceived relevance; the survey did not ask respondents to compare these examples with power-specific materials.

"only11.5%of respondents rated them “very relevant,” whereas53.8%were neutral-to-negative."

Discussion¶

Related Claims¶

• After gamified security training, female students gave more positive confidence and format-preference ratings than male students (descriptive survey percentages, no inferential test) — related
• AI-mediated feedback in hands-on exhibits improves learning and engagement — related
• College students overwhelmingly prefer short, mobile-friendly gamified security training over long desktop training or videos — related
• 76.5% of students demand a shift toward hands-on practical computer activities over theoretical instruction — related
• Community survey of power-and-energy researchers and practitioners finds barriers to running AI models are nearly universal, affecting even experienced users — related