AI Said So: Navigating Pro Se and Client Use of AI-Generated Legal Information

This session explores a novel phenomenon unique to the AI-age: self-represented litigants are increasingly arriving to public law libraries anchored in AI-generated legal information and rejecting law librarians’ expertise and authoritative information. The presenter is an MILS student who has designed a research study regarding this phenomenon and will be conducting the research this coming Spring. Attendees will be introduced to theoretical frameworks and literature underpinning this study, the study’s purpose, design, and research questions, and will feature an opportunity to share firsthand experiences and strategies from your own location.

This session is geared toward legal information professionals who have struggled when encountering an AI-influenced SRL and wants to understand: 1. Why this situation occurred, and 2. What they can do next time—because there will be a next time.

TARGET AUDIENCE:

Public-facing law library professionals and firm librarians who are training lawyers to assist clients with their AI usage.

TAKEAWAYS:

1. Attendees will learn what automation bias is and how it manifests in public law libraries.
2. Attendees will learn how affective states (emotions) influence information behavior and increase the likelihood of overreliance on AI and rejection of the law librarian's subject matter authority.
3. Attendees will learn what strategies are positioned to be successful vs. unsuccessful, informing future reference encounters with AI-influenced patrons.

SPEAKERS:

• Ella Pieterse, Law Library Assistant, New York State Unified Court System
• Alison Mallonee, AI Integration Manager, Williams Mullen

AALL BODY OF KNOWLEDGE DOMAINS: Professionalism + Leadership At Every Level, Teaching + Training

All AALL webinars include live captioning services.

The opinions shared during this program represent the views of the speakers and do not necessarily reflect those of the American Association of Law Libraries (AALL). Recording, capturing, or using AI tools to duplicate, transcribe, or otherwise reproduce an AALL program in any form is strictly prohibited without prior written consent from AALL. This includes, but is not limited to audio, video, or any other content shared. By attending an AALL program or accessing an AALL recording, you agree to adhere to this policy.