AI SESSIONS

Overcoming AI Pitfalls: Practical Frameworks for Deliberative & Ethical Use
A Library 2.0 / Learning Revolution Workshop with Reed Hepler

OVERVIEW

Artificial intelligence is now deeply embedded in library systems, academic tools, and daily workflows—yet most librarians and educators use these systems without fully understanding their limitations, hidden influences, or the risks they pose to professional judgment, information equity, and ethical practice. This workshop equips participants with practical frameworks to recognize these challenges and engage with AI more deliberately and effectively.

Participants will first examine how personification shapes AI interactions in consequential ways. When individuals refer to AI as "smart," inquire about what it "thinks," or express concern about what it "wants," they fundamentally alter how they evaluate outputs and integrate these tools into professional practice. The workshop illuminates the subtle mechanisms through which personification influences decision-making and provides concrete strategies for maintaining appropriate epistemic distance. Participants will analyze authentic conversation examples, identify linguistic patterns that suggest unwarranted agency, and practice reframing AI relationships as human-directed collaboration rather than consultation with an intelligent entity. Understanding AI as a sophisticated pattern-matching system rather than an intelligent agent carries direct implications for information literacy instruction, research support, and professional workflows.

The session then turns to the invisible AI systems operating within common academic tools—from email filtering and autocomplete suggestions to learning management systems and library discovery platforms. These hidden integrations shape information access, communication patterns, and professional workflows in ways that most users do not recognize or critically examine. Participants will learn to identify AI integration points across platforms, evaluate how these systems affect information quality and access equity, and develop strategies for maintaining professional judgment when AI operates as an invisible intermediary. Through case studies from academic databases, institutional platforms, and productivity tools, attendees will distinguish between beneficial AI assistance and problematic automation that undermines professional expertise or introduces systematic biases into scholarly work.

Finally, the workshop addresses the institutional and ethical dimensions of AI adoption in library and educational environments. As these technologies increasingly shape how libraries serve their patrons and support academic missions, librarians must establish ethically sound practices that address data privacy, misinformation, algorithmic bias, academic integrity, and authorship. Participants will explore how AI tools intersect with information literacy, labor ethics, and professional responsibility, drawing on scholarly literature, institutional guides, and frameworks from ethics bodies and practitioners. The session provides structured approaches to identifying ethical concerns and translating them into actionable institutional practices that align with professional values and pedagogical goals.

By the conclusion of this workshop, participants will possess a comprehensive toolkit for deliberative AI engagement across professional contexts. Attendees will leave with conversation templates that resist personification, an AI audit checklist for identifying hidden systems in common tools, decision frameworks for evaluating AI appropriateness and reliability, and a customizable template for creating institutional ethical frameworks. Most importantly, participants will understand that effective AI collaboration requires humans to remain in control of creative and analytical processes, treating AI as an instrument rather than an autonomous collaborator. This workshop emphasizes that awareness and deliberation do not constitute rejection of AI technologies but rather represent pathways to using them more effectively by understanding what they actually are, what they actually do, and how they should be governed within professional and educational contexts.

LEARNING OBJECTIVES: 

• Identify specific linguistic patterns and interaction behaviors that inappropriately attribute agency to AI systems, as well as hidden AI integrations operating within common academic and professional tools
• Evaluate AI outputs using frameworks that account for statistical pattern generation rather than intelligent reasoning, assess the impact of hidden AI on information quality and access equity, and analyze ethical concerns related to privacy, bias, integrity, and labor
• Apply conversation steering techniques that maintain human agency and appropriate epistemic distance, strategies for preserving professional judgment when working with AI-mediated tools, and best practices for AI use that align with educational and professional values
• Create personification-resistant workflows and language protocols, teaching approaches that help students recognize algorithmic mediation in their research processes, and institutional AI ethical frameworks tailored to local contexts and values
  

The recording and presentation slides will be available to all who register. 

DATE: Tuesday, June 30th, 2026, 2:00 - 3:30 pm US - Eastern Time