Full Curriculum

Research support is where many librarians first encounter real AI utility, and real AI failure. This module maps where AI adds value to literature reviews, search strategy, and evidence synthesis, and where it gets students into trouble.

Reference and instruction are where librarians spend the most time, and where AI provides the most immediate daily utility. This module is entirely practical: real tasks, real prompts, real time savings.

Metadata work is labor intensive, detail oriented, and increasingly supported by AI tools. This module covers the real state of AI in cataloging: where it saves hours, where it introduces errors, and how to design quality-controlled workflows.

AI is reshaping how patrons discover library collections, and how libraries describe them. This module covers the practical state of AI in discovery layers, digital collections, and institutional repositories.

The difference between occasional AI users and power users is a prompt library. This module teaches you to build one, systematically capturing what works so you don't start from scratch every time.

Vendors are selling academic libraries AI products faster than libraries can evaluate them. This module covers where AI actually fits in collection development and acquisitions, how to test vendor AI search tools against your own queries, and how to decide what is worth the budget.

You've tried AI, you've seen the value, and now you need to bring your institution along. This module covers the practical advocacy work: how to frame the case, what data to use, and how to run a pilot that builds institutional confidence.

Every AI adoption decision is also a labor decision. This module covers ALA's Labor value: why AI must not justify job cuts or deskilling, how to build genuine worker input into AI decisions, the hidden human labor behind the tools, and how to turn efficiency gains into better working conditions rather than fewer positions.