TRV-2026-2317Certified recordPeer-reviewed

The Use of Artificial Intelligence in Creating Educational Material for Critical Care Clinicians: A Survey

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…

Labor · The Trace — both readings · certified 2026-10-30 · v1 · article view · machine-readable

Current reading — gain

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.

Current reading — problem

Every AI user surveyed had to fix errors in AI-generated teaching materials, including ambiguous wording, unsuitable images and incorrect content.

What this doesn’t fix

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.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-2317, v1: “The Use of Artificial Intelligence in Creating Educational Material for Critical Care Clinicians: A Survey.” Truvace, 2026-10-30. /record/TRV-2026-2317 (accessed at citation time). sha256 f932e7765766ae8a…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv1f932e7765766…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-2317 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.