The Future of Information Literacy: How AI Changes the Information Landscape

When Adoria Williams was a student, her library had one database. She had to physically go to the library and sit down at it. Before that, there were card catalogs. Finding a source was slow, manual work, and learning to do it was tedious.

Adoria is now the Head Librarian and Department Chair at Merritt College in Oakland, CA, and she's watched that skill survive every upgrade since. The card catalog gave way to the database. The database gave way to the open web. Each time, the tools changed and the underlying job held: help people find information, judge whether it's any good, and use it honestly.

AI is the newest layer. Adoria knows AI is big, but she is equally clear that it doesn't retire her job.

"AI has been around for quite a while, and we use it every day in different ways. People just don't even realize that."

The obsolescence worry, read backwards

There's a fear running through the profession, spoken and unspoken, that a tool which answers any question in seconds makes the person who teaches research unnecessary. Adoria reads it the opposite way.

"Librarians and information professionals have always helped people find, evaluate, organize, preserve, and ethically use information. We are the keepers of information. Those responsibilities aren't going away. They're becoming more important."

The core of the discipline has not changed. What changed is the weight of the work. AI has added a new layer to the information environment. A confident, fluent answer that might be invented by AI is a harder thing to evaluate than a library record ever was, and the consequences of getting it wrong travel further.

"AI does not replace traditional library skills. It raises the stakes."

The stakes are clearest in what a mistake now costs. When a search returned ten database results, a weak source cost a student some credibility on a paper. When a generic chatbot returns a polished paragraph with a citation that doesn't exist, a student who can't catch it puts a falsehood into the world under their own name. The price of poor information literacy went up, and the people trained to teach it are needed more, not less.

The teaching list got longer

The reason the job grew is that the syllabus did. Source evaluation used to mean checking authority, currency, and purpose. Adoria still teaches all of that, and now she teaches a second column alongside it.

She has to help students and faculty reckon with AI-generated information, algorithmic bias, hallucinations, privacy and data use, authorship, and attribution. None of those were on the list a decade ago. All of them are information literacy problems, which means they land in her field.

"With AI fluency, it's important to explain to people what that even means. What biases even mean. A lot of students just don't have a clue what that is and what to look for."

Closing that gap is now part of the job. Teaching someone to spot a hallucination or name a bias in a generated answer is the same work as teaching them to question a source, but aimed at a newer, slipperier source.

Where the tool has to earn its place

A field this careful about sourcing can't teach against a black box. Adoria covers other AI tools directly in her course so students understand what they're handing over when they use one.

"We covered other AI tools, just so they can get an idea of all these other things that might not keep their information private, that use whatever they put in to enhance other LLMs, or that might give you hallucinations that actually lie to you."

Teaching source evaluation is cleaner when the classroom tool doesn't work against the lesson. Because Adoria's Assistant is grounded in her own course materials and never uses student data to train AI models, students can practice judgment against sources and standards she actually chose. The Assistant can also show the sources behind its answers, pushing a student back to a lecture slide or textbook chapter, which means the same evaluation habits a librarian has always taught apply directly to what the tool returns. A student can trace a claim, check what's missing, and decide whether to trust it, the way they would with any source. Nectir describes this as building research and information literacy skills that carry over into the rest of a student's work.

The skill that follows students into every career

Adoria teaches information literacy because it doesn't stay in her classroom, but follows students out.

"It exists. It's in every career. It's being used in some aspect. Students just have to remember to evaluate, verify, revise, and make their own decisions when using it."

She's specific about the useful parts. AI helps with brainstorming, organizing an approach, clarifying something confusing, and practicing a skill. Every one of those is a legitimate workplace use, and each still requires the person to check the output before relying on it, which is exactly what a librarian is trained to teach.

"We have to teach them how to ask better questions, and we have to teach them how to evaluate those machine-generated answers, and to recognize where human expertise is still required."

Knowing where human expertise is still required is what an information literacy course has always aimed to build toward, and learning to use AI well requires the same judgment. A student who can catch a hallucination and name a bias is doing the thing the field has always asked of them, on the newest object it's ever had to evaluate.

Adoria sees AI as one more layer on the information environment her students have to navigate. Her job is to make sure they can see through it.

Frequently asked questions about Nectir AI

What is Nectir AI? Nectir AI gives colleges, universities, and high schools the ability to deploy AI Assistants across their campus that are fully controlled by faculty and administrators, built into existing learning management systems, and compliant with FERPA and SOC 2 Type II standards. Nectir is trusted by 150,000+ students across 130+ campuses, including a partnership with California Community Colleges, which serves 2.2 million students across 116+ campuses.

How does grounding help students spot bad information? Faculty ground each Assistant in their own course materials, so students evaluate answers against the sources and standards their instructor actually chose, rather than whatever an open consumer tool surfaces. That makes source evaluation, citation-checking, and bias-spotting part of the assignment.

Does Nectir use student data to train AI models? No. Nectir is built for schools and is compliant with FERPA and SOC 2 Type II standards, which is part of why faculty like Adoria can teach against consumer tools that may use whatever students enter to train other models.

How can I learn more about Nectir? Want to see what AI support looks like when it's built into the tools your campus already uses and controlled by your faculty? Schedule a demo, and our team will walk you through how it works at schools like yours.