We are living in the early stages of an Information Shock caused by generative AI. Information Shocks are rare, but historically transformative. This one will require a reinvention of information science itself.
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A few months ago, an undergraduate student came to the library searching for a specific article. She had found a citation in another research paper complete with journal name, volume, issue, and page numbers. Everything looked legitimate. The only missing element was a DOI. At first, I thought it was a simple indexing issue. But when I checked the journal’s volume and issue, I discovered no such article existed. The citation was completely fabricated. It looked real, but it was not real. In the age of generative AI and large language models, this kind of incident is increasingly common in academic research. As we widely utilize AI tools for academic writing and literature review, a new problem has emerged: AI hallucination.
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Even when people use the same AI system, they do not evaluate AI-generated information in the same way. For example, imagine two students using Gemini or other generative AI tools for the same assignment and both receive nearly identical answers. One student quickly accepts the response and moves on. The other pauses, checks the information against outside sources, and revises the AI-generated output before using it.
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For decades, the golden rule of information literacy was simple: check the source. Who wrote the article? When was it published? Does the URL end in .gov or .edu? Those questions still matter, but in today’s digital ecosystem, they are no longer enough. Modern users don’t just read static webpages; they navigate a chaotic blend of search engine snippets, algorithmic social feeds, influencer testimonials, and AI-generated summaries. In high-stakes arenas like personal health, evaluating a single “source” is no longer the primary task. The real challenge is making sense of an entire information environment.
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We began teaching an online class about records scandals in 2023, at the very beginning of what would become the gen AI takeover. In 2024, we found that 67 of our 100 students used gen AI on the first essay assignment despite our express prohibition of its use—in any capacity. So, we had to pivot our teaching approach. By 2025, we actually asked students to use gen AI in their work in order to learn from it. Here, we lay out the changes we made and how we’re using gen AI in our writing instruction to build critical thinking skills and AI literacy.
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This is the conclusion I’ve come to after witnessing the growing popularity of AI tools. AI is everywhere, despite its propensity to produce misleading or incorrect information, and the moral objections of workers who fear losing their livelihoods. Why do patrons choose AI tools while more reliable (and less controversial) options sit on the shelf?
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Users of GenAI applications need to understand that using GenAI can lead to benefits but also introduces burdens. The benefits and the burdens are two sides of the same coin.
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One of the main challenges which academic librarians face when trying to develop programs and services that support AI literacy is the wide array of stances taken by institutions, and individual faculty members, when it comes to teaching with and about AI tools. These dimensions are not only applicable to students, but also to faculty, who also need help and guidance navigating the new technologies. One aspect which becomes central to this conversation is promoting the ethical use of AI tools in the academic environment. Although the topic has gathered considerable attention in recent conversations, these remain fragmented and divisive.
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As artificial intelligence reshapes how we search, write, and learn, librarians are increasingly expected to help communities navigate an unfamiliar digital landscape. This article advocates for incorporating AI literacy into Library and Information Science education and introduces a new course, “AI and Libraries,” designed to prepare future-ready information professionals. It emphasizes that AI literacy is critical for promoting equitable understanding and access in an age defined by intelligent systems.
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Library workers can be AI literacy leaders without using, or encouraging the use of, generative AI tools.
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