A key role of librarians who teach is to guide learners in selecting the appropriate tool for the task at hand and using it effectively. With the recent proliferation of AI search engines (eg. Consensus, Elicit, Perplexity, etc), there is an opportunity for librarians to harness what we already know about teaching information resources to help our learners integrate the use of these tools into their research practices in a way that’s effective and thoughtful. During this workshop, designed for health information professionals involved in teaching, participants will learn how to create activities that develop their learners’ awareness and critical thinking regarding the use of AI search engines. This workshop will focus on instructional design and lesson planning, structured around three key pillars: situational factors, learning outcomes, and active learning. The facilitators will lead participants through a variety of activities, including mind-mapping exercises and group discussions, and will draw on the collective experience of participants as well as their own experience teaching AI search engines. This workshop will inspire participants to collectively explore and reflect on how to incorporate AI search engine instruction into their teaching.

Objective: This lightning talk will provide a brief description of a scoping review designed to identify the extent to which generative AI is being used in the search methods of evidence synthesis reviews. We will compare tools and strategies used by review authors, and extract details on the level of reporting.

Methods: The JBI scoping review methodology will guide the conduct of this review. After calibration exercises on screening and data extraction were completed, an a priori protocol was published on OSF Registries. To be eligible for inclusion, a review must be a known type of evidence synthesis, and authors must have either used a generative-AI powered tool to develop database search strategies or used an AI search engine to locate references directly. We will search from Jan 2022 to present: Ovid Medline, Ovid Embase, Ovid PsycINFO, EBSCO CINAHL, EBSCO ERIC, ProQuest Sociological Abstracts, Elsevier Scopus, and Clarivate Web of Science Core Collection. We will conduct a supplementary full-text search in EBSCO MEDLINE, EBSCO CINAHL and Lens.org. We will independently screen in two stages in Covidence; disagreements will be resolved by consensus and discussion. We will extract study characteristics; characteristics related to the method, type of chatbot or AI search engine used; description of search method; and search reporting elements. The results will be presented in tables, accompanied by descriptive summaries.

Results and Discussion: This project will provide insights into the adoption and reporting of generative AI tools in KS searches.