05/06/2026 –, Borduas-Krieghoff2 Langue: English
Keeping current on emerging AI tools for evidence synthesis can be a challenge for librarians who support knowledge synthesis projects like systematic, scoping, and other types of reviews. These tools are rapidly evolving and are increasingly being integrated into users' review processes. However, many AI tools for evidence synthesis exist behind paywalls with limited free trials and opportunities for librarians to fully explore all the features offered by the tool. This can greatly limit librarians' knowledge about how they might be used by a research team conducting a knowledge synthesis project.
This presentation is a case study that will showcase an approach that was employed by health sciences librarians at the University of Alberta for exploring and learning about the AI tool, Elicit Systematic Review. The process used was an informal one but still provided promising insights into the strengths and limitations of this AI tool. Librarians were able to develop evidence-based recommendations to provide practical guidance to researchers who are using this tool.
Megan Kennedy is a librarian at the University of Alberta supporting the Faculties of Nursing and Medicine & Dentistry, with a focus on geriatrics. Her work centers on teaching and collaborating with researchers, faculty, and students to develop effective search strategies for systematic, scoping, and realist reviews. Megan provides consultation and instruction on advanced database searching, supports evidence synthesis projects, and contributes to the development of research and information literacy skills across the health sciences. She is particularly interested in making complex searching methodologies more approachable and empowering learners to feel confident and capable in the evidence synthesis process.