Artificial Intelligence (AI)
This page highlights tools and learning resources used by health sciences librarians to understand, evaluate, and responsibly integrate AI into research support, instruction, and information services.
Guidelines
The ACRL AI Competencies for Academic Library Workers provide a comprehensive framework outlining the mindsets, ethical considerations, and practical skills library workers need to understand, evaluate, and responsibly use AI technologies in academic settings. This is an important resource for health sciences librarians because it helps them navigate AI’s growing impact on research workflows, information literacy instruction, data practices, and clinical education environments.
Using AI in Libraries
This webinar explores how librarians can use prompt engineering and the CLEAR framework to improve the effectiveness of their interactions with generative AI, strengthening their support for research, instruction, and user engagement.
This lesson aims to empower GLAM (Galleries, Libraries, Archives, and Museums) staff by providing the foundation to support, participate in and begin to undertake in their own right, machine learning-based research and projects with heritage collections.
This open NNLM short course introduces librarians to the core concepts, ethical considerations, and practical applications of generative AI, offering training materials and hands‑on sessions to help them integrate AI tools into library services and operations.
AI Use for Evidence Synthesis
Position statement on artificial intelligence (AI) use in evidence synthesis across Cochrane, the Campbell Collaboration, JBI and the Collaboration for Environmental Evidence.
RAISE 1 provides tailored recommendations for eight of the distinct roles in the evidence synthesis ecosystem: evidence synthesists, methodologists, AI tool developments teams, organisations that produce evidence synthesis, publishers, funders, users, and trainers of evidence synthesis methods.