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Collaborating to create re‐usable training materials to increase knowledge and library professionals' understanding of artificial intelligence

Susan Smith et al.

Health Information and Libraries Journal2025https://doi.org/10.1111/hir.70006article
AJG 2
Weight
0.50

What the paper says

Artificial intelligence (AI) provides new challenges for knowledge and library professionals (KLPs) working in healthcare. In NHS England, KLPs created the AI Literacy Group, part of the NHS England Workforce, Transformation and Education (NHSE W,T&E) Current & Emerging Technology Community of Practice (CET CoP). The aim of the group was to develop their understanding of the changing landscape and understand how AI could be used in healthcare organisations and knowledge and library services (KLS). The group developed a series of presentations that KLPs could use to develop their own understanding of AI and which could be adapted for use in their local organisations. Case studies from KLPs working in two NHS organisations discuss the use of the presentations to train clinical and non-clinical staff, and consider their impact and next steps. Through collaboration, the group was able to learn together, develop a shared understanding of AI and create training resources to benefit the KLP community. The group has evolved to consider new areas of learning relating to the adoption of AI and to review the content of the presentations to ensure that they are up to date and relevant in this ever-changing landscape.

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https://doi.org/https://doi.org/10.1111/hir.70006

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@article{susan2025,
  title        = {{Collaborating to create re‐usable training materials to increase knowledge and library professionals' understanding of artificial intelligence}},
  author       = {Susan Smith et al.},
  journal      = {Health Information and Libraries Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1111/hir.70006},
}

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Collaborating to create re‐usable training materials to increase knowledge and library professionals' understanding of artificial intelligence

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.