"What We've Learned: Confronting AI's Challenges in Libraries" is a two-day virtual conference on the practical realities of generative AI in libraries. Several years into widespread adoption, this conference brings practitioners into honest conversation about what AI integration has actually required, and its effects on the field of libraries. 

We invite proposals across the full landscape of challenges:

• Systems and tools — integrating AI into databases and workflows; customized RAGs, agents, and models; AI chatbots and refusal; hidden costs, vendor lock-in, and the economics of AI tools.

• Teaching and instruction — AI literacy; the realities of programming and instruction; assessment, grading, and course development; academic integrity; detecting and managing AI-generated content in student and scholarly work.

• Trust and accuracy — hallucinations, verification, and research support; bias, misinformation, and disinformation.

• Access and ethics — equitable access for students and patrons; balancing AI against available resources; accessibility and assistive technology; copyright, fair use, and AI-generated content in collections; collection development and licensing in an AI era.

• People and institutions — university adoption; administrative expectations; AI policies (or their absence); change management.

• Wellbeing and human impact — staff burnout and workforce wellbeing; cognitive load and the physical and mental toll of constant adoption; cognitive dissonance; sustaining attention, judgment, and craft amid automation.

We welcome perspectives from all types of libraries: school, academic, special, law, medical, military, and others. What has worked? What hasn't? And what comes next?

Submissions can be 5-7 minute presentations or demos of a specific tool, suite of tools, or AI-centered project. 

We welcome demo submissions that show innovative uses of AI tools, which would be at an intermediate or advanced level. This may include demos which demonstrate the use of custom GPTs/LLMs, MCPs, APIs, Agentic AI, programming, and other intermediate and advanced use cases.

All presentations and demos should be in English. All library professionals (Librarians, administrative staff, library staff, library assistants, consultants, retired library professionals, etc.) are encouraged to submit. Current MLS and MLIS students, as well as Library student assistants, are also encouraged to submit.

Submission Deadline: Friday September 4, 2026

Questions? Contact Grace Adeneye at goa2592@nyu.edu and Amani Magid at am6087@nyu.edu.