Boglarka Huddleston
Boglarka Huddleston is the Manager of Research & Instruction Services at Lane Medical Library, Stanford School of Medicine. In this role, she leads a team that supports faculty, students, clinicians, and researchers as they navigate the evolving scholarly information landscape, including evidence synthesis, responsible AI use, emerging technologies, and research tools. With master’s degrees in Library Science and Psychology, Boglarka brings a practical, learner-centered approach to instruction and collaboration across the biomedical and health sciences. She has demonstrated success in building partnerships across academic and clinical communities, identifying emerging needs, and developing responsive services.
From Search Strings to Synthesis: Teaching AI Literacy Across the Research Lifecycle
As generative AI tools rapidly enter the research lifecycle, academic libraries face an urgent teaching responsibility: helping students and researchers develop AI literacy as an essential extension of digital literacy. This plenary presentation explores how librarians can move beyond one-time tool demonstrations to teach responsible, critical, and transparent AI use across the research process.
Drawing from practical, free or free-tier use cases, the session follows three common stages of student research. First, it considers how generative AI such as ChatGPT can help develop, translate, and refine database search strings, while also introducing errors, missing controlled vocabulary, or unsupported assumptions. Second, it examines how tools such as Consensus can help users scan the breadth of published literature and identify areas of apparent agreement or disagreement, while requiring careful attention to database coverage, ranking, and disciplinary bias. Third, it discusses how Elicit AI can support article summaries and early literature review workflows, while emphasizing that AI-generated synthesis must never replace close reading, source verification, or methodological transparency.
Across these examples, the presentation identifies key intervention points where academic librarians can protect research integrity: teaching students to verify AI-generated searches in established databases, evaluate whether AI-selected literature is comprehensive and representative, fact-check summaries against original sources, and document AI tool use in research methods or acknowledgments when appropriate.
The presentation concludes with actionable recommendations for academic libraries seeking to embed AI literacy into research support, instruction, and consultation services. These include developing adaptable AI-literacy learning outcomes, modeling transparent disclosure practices, and partnering with faculty to integrate safeguards into assignments and research training. By positioning librarians as critical guides rather than tool promoters, academic libraries can help ensure that AI becomes a foundation for stronger, more ethical research rather than a shortcut around scholarly judgment and practice.