When I imagine redesigning the Library and Information Science curriculum for the age of AI, I see it semester by semester, like walking through the library stacks, each level taking me closer to new knowledge, but always with a familiar fragrance of books and values.

Semester 1 – The Roots
Here I would begin with Foundations of Library Science, Information Sources & Services, and alongside them introduce Introduction to AI and Data Literacy. Students should learn what algorithms are, how language models work, and why data matters. Just remember, this is not to turn them into computer scientists, but into informed professionals who can converse with both technology and community.

Semester 2 – The Tools
This stage could focus on Knowledge Organization, Cataloguing and Metadata, but reframed to show how AI assists in subject indexing, semantic search, and linked data. Alongside, a course on Digital Libraries and Discovery Systems will let them experiment with AI-powered platforms. By the way, assignments could include building small datasets and watching how AI classifies them — both the brilliance and the flaws.

Semester 3 – The Questions
Here ethics must enter the room strongly. A full course on AI, Ethics, and Information Policy is essential: patron privacy, copyright, algorithmic bias, transparency. At the same time, practical subjects like Digital Curation and Preservation should demonstrate how AI restores manuscripts, enhances images, or predicts file degradation. No wonder, students will begin to see AI as both a tool and a responsibility.

Semester 4 – The Bridge
I see this as a turning point: courses on Human–AI Interaction in Libraries, Information Literacy Instruction in the AI Era, and Data Visualization for Librarians. Students would learn to teach communities about AI tools, to verify machine answers, and to advocate for responsible use. A lab-based course could even simulate AI chatbots for reference desks, showing how humans must stay in the loop.

Semester 5 – The Expansion
By now, students are ready for deeper exploration. They could take electives like AI in Scholarly Communication (covering plagiarism detection, trend forecasting, citation networks) or AI for Community Engagement (local language NLP, accessibility, inclusive design). At the same time, collaboration with computer science or digital humanities departments could be formalized as joint workshops.

Semester 6 – The Future
The final stage should be open-ended: a Capstone Project in AI and Libraries, where each student selects a challenge — say, AI in cataloguing, or a chatbot for local history archives — and builds a small prototype or research study. Supplement this with an Internship or Residency in a library, tech lab, or cultural institution. Just imagine the confidence this gives: they graduate not as passive observers of AI but as active participants in shaping it.

And beyond…
I must not forget lifelong learning. The curriculum should be porous, allowing micro-credentials, short courses, and professional updates, because AI won’t stop evolving. In fact, it will keep testing us — and so our readiness must be continuous.

Looking back at this imagined curriculum, I feel it keeps the spirit of librarianship alive — service, access, ethics — while opening the doors to AI-driven realities. It is like adding a new wing to the old library: modern, glowing, full of machines perhaps, but still part of the same house of knowledge where the librarian remains a human guide.