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AI literacy

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Bo Hyun Hong

When AI Output Becomes “Good Enough”: Not Everyone Evaluates AI the Same Way

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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FeaturedOpinion
Elaine Kong

Beyond “Check the Source”: Information Literacy for Health Decisions in the Age of AI

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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Education
Jason R. Baron

Same Class, New Approach: Reimagining Information Students’ Use of Gen AI in Formative Writing Assessments

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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Education
Mónica Colón-Aguirre

The Complexity of Ethic Centered AI Literacy in Higher Education

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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Education
Agnes Pearcy

From Information Literacy to AI Literacy: Preparing Librarians for Emerging Responsibilities

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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Education
Jenna Hillhouse

Confidence Without Comprehension: Why AI Literacy Needs a Reset

When AI tools collapse complex search processes into seamless responses, they can obscure uncertainty, mask gaps in understanding, and smooth over meaningful distinctions of meaning, relevance, and confidence. Users may feel informed without ever confronting the limits of their knowledge or the assumptions guiding how information is interpreted. The challenge for libraries is not just teaching people how to use AI tools, but how to think with them without surrendering judgement.

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Education
Michael Ridley

Lessons in AI Literacy and Explainability from Lucy and Ricky

In the classic 1950s TV sitcom I Love Lucy, when Lucy did something outrageous her husband Ricky would exclaim “Lucy, you’ve got some explainin’ to do!” Typically, Lucy would come up with some sort of implausible response. Hilarity ensued. Well, it’s not the 1950s anymore but 70+ years later Large Language Models (LLMs) and AI chatbots (e.g., ChatGPT, Gemini) are doing outrageous things (hallucinations, fabrications, misinformation, and worse) and the explanations, if there are any, are just as implausible. And it isn’t funny.

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