Introduction to Artificial Intelligence (AI) for Librarians - Proposed 2026 Course

Introduction to Artificial Intelligence (AI) for Librarians Proposed | Graduate Seminar | 13 Weeks | Instructor: Dean Giustini, UBC biomed librarian Course Description This course is meant to be a proposal; the course will provide a foundation in artificial intelligence (AI) for librarians and information professionals. Library and information studies students will acquire basic understanding and literacies regarding AI, including machine learning, large language models (LLMs), and deep research tools. Through lectures, workshops, and critical discussion, students explore AI's impact on library services, metadata practices, information retrieval, and scholarly communication. The course emphasizes ethical, social, and philosophical dimensions of AI in library and information contexts, equipping graduates to apply, critique, and lead AI initiatives in academic and community settings. Learning Objectives By the end of the course, students will be able to: Define core concepts: AI vs. genAI, machine learning, LLMs, and research reasoning tools, with relevance to library practice. Apply AI in libraries: Identify, evaluate, and critique AI-powered tools in reference services, cataloguing, metadata, and research support. Evaluate ethical and social issues: Assess implications of AI in libraries ie., privacy, surveillance, algorithmic bias, and responsible use. Support research communities: Analyze and apply deep research and reasoning tools to facilitate discovery, information retrieval, and evidence synthesis. Weekly Schedule Week 1 – Introduction: AI, Libraries, and the Information Professions What is AI? Historical overview and definitions of terms The AI literacy framework for librarians AI in higher education and knowledge institutions Readings: Floridi (2019), Bawden & Robinson (2020, The Dark Side of Information) Assignment: AI in everyday life reflection (short essay) Week 2 – Machine Learning Basics for Information Professionals Core concepts in machine learning: supervised, unsupervised, reinforcement learning From rules-based systems to probabilistic models Case studies: recommender systems, citation prediction Readings: Workshop: Exploring ML visualizations (e.g., TensorFlow playground) Week 3 – Large Language Models (LLMs) and Generative AI How Large language models (LLMs) ( such as GPT, Claude, Gemini, LLaMA) work Prompt engineering as a literacy skill Strengths and limits of LLMs in research tasks Readings: Discussion: Are LLMs “knowledge tools” or “stochastic parrots”? Week 4 – AI in Library Operations: Reference, Discovery, and Searching for Information Conversational AI for reference services Discovery layers and intelligent search AI in library instruction Readings: Workshop: Using ChatGPT by OpenAI, Elicit.com, Perplexity for search strategies Week 5 – Metadata, Cataloguing, and Knowledge Organization AI in metadata extraction and subject indexing Natural language processing in cataloguing workflows Errors, biases, and authority control Readings: Case Study: NLM’s Medical Text Indexer (MTI) and OCLC initiatives Week 6 – Scholarly Communication and Research Communities AI in peer review, citation analysis, knowledge synthesis AI-powered systematic review tools (e.g., Evidence Hunt, Open Evidence, Scite.ai) Librarians as research partners in AI-powered scholarship Readings: Assignment: Tool evaluation (select 1–2 AI research tools and critique usability & trustworthiness) Week 7 – Deep Research and Reasoning Tools Beyond search: reasoning engines, knowledge graphs, semantic AI Tools: Semantic Scholar, Scite, Scholarcy, Undermind.ai Readings: Workshop: Comparative use of deep reasoning tools in topic scoping Week 8 – Ethics of AI: Privacy, Equity, and Bias Algorithmic bias, representation, and equity issues Surveillance, patron privacy, and data ethics AI and Indigenous knowledge sovereignty Readings: Noble (Algorithms of Oppression), Benjamin (Race After Technology) Discussion: Should libraries adopt AI if bias cannot be eliminated? Week 9 – Responsible AI and Governance in Libraries Responsible AI frameworks (EU, UNESCO, CAI) Ethical guidelines for library AI adoption (IFLA, ALA, CFLA) Policy writing for AI in library institutions Readings: Assignment: Draft an ethical use guideline for AI in libraries Week 10 – Philosophical and Social Implications of AI AI and the nature of knowledge Posthumanism and human–machine symbiosis in knowledge institutions The future of librarianship in the age of AI Readings: Seminar: Debate – “Will AI replace librarians, or augment our work?” Week 11 – Case Studies in AI and Libraries National libraries, archives, and AI labs Case studies: British Library, Library of Congress, NLM, Europeana Community library applications of AI (local language services, accessibility) Readings: Assignment: Group presentations on a real-world case study Week 12 – Student Research Presentations Students present their applied research projects (tool analysis, ethical guideline, or AI pilot proposal) Peer feedback sessions Week 13 – Future Directions: AI Literacy for Librarianship Discussion and synthesis of topics: librarians as ethical stewards of AI adoption Emerging trends: agentic AI, multimodal models, automated reasoning Final reflections: AI, academic freedom, and the role of the information profession(al) Capstone Submission: Final research paper or project proposal Assignments & Evaluation Reflection paper (10%) – AI in everyday practice (Week 1) Tool evaluation (15%) – Critical analysis of one AI-powered tool (Week 6) Ethics guideline (15%) – Draft institutional policy (Week 9) Case study presentation (30%) – Group project (Week 11) Final paper/project (30%) – Applied research, tool design, or policy proposal Related iSchool courses San Jose State University iSchool. Undergraduate Certificate in Human-Centered Artificial Intelligence.; Syracuse and Washington iSchools provide structured AI programs; Illinois focuses on AI through research and elective coursework (e.g., ischool.syracuse.edu, ischool.uw.edu, ischool.illinois.edu, ischool.utoronto.ca). UofT and UBC iSchools offer a single course offered periodically; https://ischool.utoronto.ca/wp-content/uploads/2025/05/INF482H-2025-Winter-Mim-Syllabus.pdf https://ischool.ubc.ca/libr-559w-3-human-centred-ai/ Disclaimer Note: Please use your critical reading skills while reading entries. No warranties, implied or actual, are granted for any health or medical search or AI information obtained while using these pages. Check with your librarian for more contextual, accurate information.