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Posted on September 3, 2025 in Blog Posts
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In our final installment of our back-to-school toolkits, we curated some of our evergreen posts about AI. We know that there isn’t a simple solution to handling AI in educational settings or weighing its benefits and its harms, but we have had many librarians offer their perspectives and tactics, creating footholds into a difficult problem. We have published many, many posts on AI over the years, so the following represents only a small sample. We encourage you to pick through our archived posts to find other gems.

Nicole Hennig’s three-part guide to answering students’ technical, ethical, and practical questions about AI is the perfect starting point for librarians looking to build AI literacy and impart knowledge about AI to students.

Many librarians have noted the relevance of information literacy skills to generative AI. Indeed, an InfoLit course that doesn’t touch on AI seems outdated. So how can librarians integrate AI into InfoLit sessions? Bronte Chiang offers concrete strategies.

Librarians are often called to give information literacy sessions in composition classes. This two-part guide advises librarians about how they might integrate AI into their InfoLit instruction and makes specific recommendations for AI literacy in the context of composition courses.

Universities are eager to integrate AI tools into their offerings for students and researchers, and the buying decisions may, in many cases, fall to libraries. Rachel Hendrick provides some guiding questions for librarians to consider when making these decisions.

Rachel Hendrick provides a good introduction to Retrieval Augmented Generation (RAG), an important framework for AIs relevant to resesearch and academic applications. Expect to learn what RAG is and why you should consider it when purchasing and using generative AI tools.

Understandably, many people default to ChatGPT when selecting an AI tool. Gary Price has other ideas. This piece examines some non-ChatGPT tools that should be on librarians’ radars.

With more AI-powered products appearing on the market, it can be difficult to keep up. ITHAKA’s Product Tracker is a useful spreadsheet for tracking new developments, and Christine McEvilly highlights specific ways that librarians can use the Tracker to learn about, test, and evaluate AI tools.

Can AI tools help with the research process? Kari D. Weaver evaluates five popular AI research tools, noting their strengths, weaknesses, and applications for librarian workflows.

Scopus has emerged as a major AI tool for academic reference work. Shannon Pritting offers a great tutorial for how librarians can use Scopus AI in reference interactions with students and evaluates the outputs of various use cases.

No one likes taking notes during staff meetings. Steven Bell evaluates Zoom’s AI notetaking function. He covers the strengths and weaknesses users should know about and gives a brief tutorial for its various functions.

We know that learning is a communal process. The best way, then, to learn about AI is to build groups that allow people to experiment, discuss, and vent together. This piece offers specific tips for starting a community of practice at your library.

Nicole Hennig offered a six-week course teaching AI literacy to library workers. Her reflections provide pedagogical guidance for anyone seeking to teach AI literacy within the framework of librarian values.

One hope for AI is that it will facilitate important but big tasks, freeing up librarians’ time to work on other projects. The authors of this post discuss how they used generative AI to create alt text for items in a digital collection.

AI chatbots are an innovative way of interacting with the potentially thousands of items in a digital collection. This case study examines how one library integrated a chatbot into its collections, creating a new way for students and viewers to learn.

Already, students and scholars are using AI to create scholarship. Many have worried about the ethical consequences of AI use in this area. Kari D. Weaver proposes a useful framework for disclosing one’s use of AI, making the process more transparent.

How good is ChatGPT as a research tool? Which tasks is it particularly helpful for? Nick Pavlovski gives a deep-dive into these questions. Though ChatGPT has become more sophisticated at research tasks with its new Deep Research function, this piece outlines useful applications for ChatGPT in the research process.
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