From anxiety to advantage
Tamir Borensztajn reframes artificial intelligence as the “connective tissue of academic knowledge”
I began my career as a librarian and earned my MLIS at Simmons University in Boston. Like many early career librarians, I spent countless hours working with students who arrived carrying a familiar mix of urgency and uncertainty. They were not looking for “information” in the abstract. They were trying to finish an assignment, start a research project, or make sense of a topic they did not yet understand.
What always struck me was how little time we had to bridge the gap between their specific academic need and the vast collections sitting behind us. The challenge was never the lack of resources. It was how quickly we could understand what they were trying to do and connect them to the most relevant material at that moment.
At the time, I thought of this as a reference problem. In hindsight, it was something much larger. It was a structural problem in how academic knowledge is delivered.
Today, that structural problem has only become more visible.
Today’s students and researchers work in fast, fragmented environments. They move between learning platforms, document editors, collaboration tools, and increasingly, AI interfaces. Meanwhile, libraries continue to curate extraordinary collections and invest significant expertise in building research guides, reading lists, and recommended pathways intended to support learning and research.
Yet there is often a gap between this work and the way students actually experience their academic tasks.
Artificial intelligence is frequently framed as a threat in this context. Students using generative tools instead of databases. Missing or hallucinated citations. Questionable sources. Erosion of academic rigor. These concerns are valid. But they are incomplete.
There is another possibility. AI can also be used to strengthen the library’s role in teaching, learning, and research. Not by replacing librarians, but by extending their expertise and making their curatorial work more directly connected to the assignments, projects, and questions students are actually working on.
Not as an answer engine, but as connective tissue. As the intelligence layer that links content, context, and intent.
The missing intelligence layer
Academia has never lacked content. Libraries invest heavily in licensed collections, open access resources, institutional repositories, and curated digital assets. Faculty design thoughtful curricula. Researchers generate new knowledge at a staggering pace. The problem is not scarcity. It is disconnection.
There is a growing gap between how academic knowledge is curated and how academic work actually unfolds.
Resources live in library systems. Research guides live on websites. Expertise lives with librarians. But assignments, projects, and research questions take shape inside courses, documents, and learning platforms, disconnected from the library’s canvas.
We have built powerful systems for collecting and organizing information, and equally powerful systems for teaching, writing, and researching. What we have not built is the connective intelligence between them.
There is no layer that consistently understands what a student is being asked to do and aligns library expertise with that context. No system that reads an assignment prompt or syllabus and helps translate that academic intent into relevant, librarian-curated resources.
The result is familiar. Significant investment in scholarly resources, paired with extensive professional curation. Librarians thoughtfully assemble research guides, reading lists, and recommended pathways designed to support learning. Yet much of this work operates with limited visibility, and many students never encounter it in the course of completing their assignments.
This is not because the work lacks quality. It is because it is structurally separated from the flow of coursework and research activity.
The distance between curation and coursework
Libraries invest enormous professional effort in building subject guides, course guides, and instructional materials aligned to disciplines and learning objectives. This work is foundational. It reflects deep knowledge of both content and pedagogy.
Yet from a student’s perspective, this expertise is often one or two steps removed from where their work actually begins.
A student does not start with a research guide. They start with an assignment prompt.
A faculty member does not begin with tools or systems. They begin with learning outcomes and course objectives.
A researcher does not frame their work as “library use.” They frame it as a question they are trying to answer.
When research guides and recommended resources are disconnected from these entry points, their impact is naturally constrained.
This is not a failure of librarianship. It is a structural gap in delivery.
Awareness, friction, and the path of least resistance
When students once turned to Google, and now increasingly turn to ChatGPT, they are not rejecting authority. They are choosing immediacy. They are following the path of least resistance.
Many students are simply not aware of the breadth and depth of what their library provides. Others are aware, but encounter too much friction in accessing it at the moment they need it. When deadlines are tight and pressure is high, convenience wins.
If the library is not visible at the point where students are trying to interpret an assignment or begin a project, it is functionally absent.
This is not a marketing issue. It is a structural one.
Importantly, this is not an argument against exploration, open-ended inquiry, or the process of searching itself. These remain central to how students and researchers build understanding. The opportunity is not to replace these experiences, but to support them more effectively by first understanding context and intent.
When a system can recognise what a student is being asked to do, it can surface relevant starting points and guide them forward with greater clarity and confidence. Exploration does not go away. It becomes more grounded. Inquiry does not diminish. It becomes more focused.
Intent is everywhere in academia
One of the most overlooked realities of academic life is how explicit intent actually is.
Syllabi outline what will be taught. Assignments define what is expected. Research proposals describe what questions are being pursued. Learning outcomes articulate what students should be able to do. These are not vague signals. They are concrete declarations of purpose.
In other industries, companies spend enormous amounts of time and money trying to infer user intent. In higher education, intent is documented, structured, and abundant.
The problem is not the absence of intent. It is the absence of systems that use it.
When a student uploads an assignment brief, they are telling us exactly what they are trying to accomplish. When a faculty member designs a course, they are declaring what kinds of learning they want to support. When a researcher drafts a proposal, they are articulating the scope and direction of their inquiry.
These artifacts are not administrative overhead. They are roadmaps.
AI gives us a way to read and interpret these signals at scale. It allows us to understand context, not just keywords. It allows us to distinguish between background reading and targeted inquiry.
This matters because academic work is not generic. It is situational.
Used well, these signals do not shortcut research. They shape it. They help students and researchers enter the process with clearer direction and stronger grounding.
From curation to context
Libraries devote enormous expertise to building research guides, reading lists, and recommended pathways aligned to disciplines and learning goals. This work reflects deep knowledge of both content and pedagogy.
The challenge is not the quality of this curation. It is the distance between where it lives and where academic work actually begins.
AI offers a way to narrow this gap. By interpreting academic context, such as an assignment description or syllabus, it becomes possible to more directly connect existing research guides and recommended resources to the work students are actually doing.
This is not about redefining librarianship. It is about making librarian expertise easier to encounter at the point where it is most relevant.
Availability versus presence
Libraries are excellent at making resources available. They are less successful at making them present.
Access does not equal visibility. A resource that exists but is not encountered might as well not exist at all.
If research guides and recommended resources are not visible in the context of coursework and project work, they remain peripheral.
AI provides an opportunity to change this. Not by pushing content, but by aligning it more naturally with the work students are already doing.
This is also not about narrowing intellectual exploration. Once students are connected to relevant resources, the full richness of browsing, questioning, and following unexpected paths remains.
A responsible intelligence layer
None of this works without trust.
Libraries have earned their position as stewards of information. Any use of AI in this space must be transparent, explainable, and accountable. Data must be handled responsibly. Librarians must retain control. Users must understand what is happening.
AI should never replace professional judgment. It should support it.
Libraries are uniquely positioned to lead responsible AI adoption because they already operate with strong ethical frameworks. They already teach critical evaluation. They already prioritize privacy and integrity.
AI does not have to erode these values. It can reinforce them.
Reconnecting the library to academic work
At its core, this is about reconnecting the work of the library with the work of the student and the researcher.
Not in theory. In practice.
The opportunity is to move from static guides to contextual support. From resources waiting to be found to expertise aligned with real academic tasks. From the library as a destination to the library as a partner in the academic process.
This is not about chasing trends. It is about fulfilling the mission libraries have always had, using tools that reflect how learning actually happens today.
AI gives us the ability to understand academic intent and respond to it. It gives us a way to make research guides, recommended resources, and librarian expertise more visible and more useful. It gives us a chance to strengthen the role of the library in the daily work of teaching and learning.
That work has always belonged to the library. Now we have new tools to carry it forward.
Tamir Borensztajn is the founder of WyderNet