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Opinion

Opinion
Ebubechukwu Uba

Should We Audit AI Before We Adopt It?

Whenever we hear AI Audit, what comes to mind is a technology already in use. We examine its outputs for bias, test its accuracy, investigate failures, or study its effects on users. By then, however, an institution may already have purchased the technology, integrated it into existing systems, trained employees to use it, and introduced it to students or community members. What would change if some of this scrutiny occurred earlier? As AI-powered technologies become more common in schools, universities, libraries, and other information environments, there is a case for treating the period before adoption as an important point of inquiry. Since we increasingly audit what AI does, should we also audit what we know about an AI system before we decide to adopt it?

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Opinion
Loida Garcia-Febo

Why Teach Strategic Foresight in Library and Information Science Now?

As the information environment continues to change through artificial intelligence, geopolitical shifts, demographic transformation, climate pressures, and economic uncertainty, strategic foresight can complement the knowledge and skills already central to LIS education. By helping future information professionals recognize emerging change, question assumptions, explore possibilities, and consider different futures, foresight offers another way to prepare students to make thoughtful decisions in the present.

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FeaturedOpinion
Md Anwarul Islam

AI-Driven LIS Education: Transforming Pedagogy, Curriculum, and Assessment in South Asia

What happens when students can write a literature review, analyze information, and receive instant feedback with a few prompts? What happens when they make a summary of literatures and answers questions based on the documents they upload? What happens when they find relevant questions and answers for discussion in their thesis work using Grok? Yes, these are possible and researchers are using these tools in effective and productive ways.

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Opinion
Ken Herold

Warrant After the Document: A Reply to Lankes’s Information Shock

R. David Lankes has written here and posted a preprint, submitted to the Journal of Documentation, arguing that generative AI is causing an “Information Shock”: a disruption so deep that it dissolves the document — the field’s organizing unit — and demands a new information science. It is the best account we have of what generative AI does to the documentary foundations of information science, and I agree with nearly all of it. The distinction between AI that produces document-like objects and AI that co-authors exchanges is the sharpest framing of the problem in our literature. The argument that AI literacy carries document-evaluation over intact and aims it at something that is not a document is correct and overdue. What follows is not a rebuttal but an extension — because the most important sentence in his argument is nearly its last, and it stops one step short of a tradition built for exactly this moment.

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FeaturedOpinion
Yanling Liu

What Happens When Research Starts with an Answer?

Have you ever asked ChatGPT a question just to get a quick answer? You’re not alone. Millions of people now turn to AI assistants before opening Google, visiting a website, or asking a librarian for help. College students are no different. Instead of beginning a research assignment by searching for books, articles, or websites, many now begin by asking a generative AI assistant a question and receiving a polished response within seconds. It feels much faster and easier. Yet this seemingly simple shift raises an important question: if research increasingly starts with an answer instead of a search, how do learners move from information to understanding?

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FeaturedOpinion
Shaharima Parvin

AI Hallucinations in Academic Research: Why Libraries Matter More Than Ever

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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FeaturedOpinion
Sheila Webber

Information Literacy in a Time of Polycrisis

You know that polycrisis isn’t just an academic obsession when the accountants start saying it’s a thing. In a document that mentions polycrisis 84 times, the Chartered Institute of Management Accountants (CIMA) identifies that understanding “the interconnectedness of external factors such as climate change, nature, or inequality, and seeing the patterns of change and the feedback loops between the factors” is essential for developing a sustainable business model.

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