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TRUVACE RECORD VERSION
record: TRV-2026-2317
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-30T18:35:00.252927Z
status: published
lens: trace
sector: labor
headline: The Use of Artificial Intelligence in Creating Educational Material for Critical Care Clinicians: A Survey
dek: Background: One Minute Wonders (OMWs) are workplace-based microlearning resources that support continuing professional development in healthcare. Generative artificial intelligence (AI) is increasingly used to create educational materials, but little is known about its application in OMWs development.
Aims: To examine the use, experiences, opportunities and challenges related to AI-assisted generation of OMWs as perceived among healthcare professionals participating in the German-speaking OMW Network.
Study desi…
gain_title: AI is seen as a tool that speeds up the creation of workplace learning resources and helps turn knowledge into teaching material, with 46.3% of surveyed professionals already using it.
problem_title: Every AI user surveyed had to fix errors in AI-generated teaching materials, including ambiguous wording, unsuitable images and incorrect content.
trace_subject: AI-assisted creation of One Minute Wonder microlearning materials by healthcare professionals
gain_reading: AI is seen as a tool that speeds up the creation of workplace learning resources and helps turn knowledge into teaching material, with 46.3% of surveyed professionals already using it.
gain_evidence: AI offers benefits for efficiency and content creation
problem_reading: Every AI user surveyed had to fix errors in AI-generated teaching materials, including ambiguous wording, unsuitable images and incorrect content.
problem_evidence: incorrect content (n = 29, 46.0%)
quick_read: A cross-sectional online survey of 136 members of the German-speaking One Minute Wonder Network, mostly nurses, asked how they use AI to build short workplace-based learning resources. Just under half said they use AI for this, mainly for figures, graphics and design elements, and almost all of those users relied on ChatGPT.

The findings matter because these materials support continuing professional development in critical care, where accuracy affects safety. Most users reviewed AI output, but errors were universal among users. The survey captures self-reported perceptions from one network, so it cannot show how often errors reach finished materials or whether the resources improve learning.
limitation: The survey drew on members of one regional network and was overwhelmingly nurses, with self-reported perceptions, so findings may not generalise to other clinicians or settings.
tag: Dual reading
key_points: 46.3% of 136 respondents (n = 63) reported using AI to create One Minute Wonders. | Among AI users, figures (65.1%), graphics (55.6%) and design elements (52.4%) were the most common uses. | ChatGPT was the most frequently used tool, cited by 95.2% of AI users. | 79.4% of AI users critically reviewed AI-generated content. | Respondents described AI as helping resource development but threatening trustworthiness and professional integrity.
rundown: The study surveyed healthcare professionals in Germany, Austria and Switzerland who belong to the One Minute Wonder Network. Respondents were predominantly nurses (128 of 136, 94.1%). The questionnaire covered AI use, quality assurance practices, perceived benefits and risks, and educational support needs.

Reported problems included ambiguous wording and inappropriate images (each 50.8% of AI users) and incorrect content (46.0%). Qualitative analysis of free-text answers produced one theme on AI as a facilitator of educational resource development and knowledge translation, and another on AI as a potential threat to the trustworthiness and professional integrity of educational resources.

The authors conclude that AI use for such materials appears established among participants and that careful human oversight is required, because inaccuracies and fabricated information are common.
sources:
- peer_reviewed | Nursing in Critical Care | https://doi.org/10.1111/nicc.70753 | 2026-11-01
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