Block 6

Adapt Our Lesson Plans!

In this block, we share tested lesson plans so you can adapt them to your own library, audience, and context.

In previous blocks of our methodology, we discussed the approach you can take when working with your target audience and how to use the resources at your disposal. We hope that BLOCK #5 gave you some ideas on how to adapt activities already done at your library to promote AI literacy. But if you do not feel quite ready for that, or if you are just looking for additional ways to enrich AI literacy activities at your institution, the following formats that we have developed and tested might be useful. They are designed for librarians specifically. Each one can be used as a standalone session or integrated into a broader training program within your institution. They are meant to be adapted to your audience, your context, and your community’s needs.

LESSON 1:
INTRODUCTION TO AI FOR LIBRARIANS

This lesson offers a grounded introduction to artificial intelligence for library professionals with no technical background. It covers what AI is and isn’t, how it differs from traditional search and retrieval systems, and where it already appears in library workflows and user interactions. Participants will explore real examples from library settings, discuss how to evaluate AI tools critically, and begin building a shared vocabulary for the conversations ahead. By the end of the session, librarians will feel confident engaging with AI as informed professionals rather than uncertain bystanders.
LESSON 2:
AI TOOLS IN LIBRARY PRACTICE – GENERATIVE AI

This lesson moves participants from conceptual understanding (Lesson 1) to practical application. It introduces the current AI tool landscape in libraries, with particular emphasis on Generative AI and Large Language Models (LLMs), while maintaining a strong critical and ethical lens. The objective is not only to familiarise librarians with AI tools, but to equip them with the knowledge and skills required to experiment responsibly, evaluate limitations, and integrate AI meaningfully into library workflows.

LESSON 3:
ETHICAL AND STRUCTURAL EVALUATION OF AI

This lesson applies a structured ethical audit framework to AI systems used in libraries, examining five interlinked dimensions: ethics, bias, privacy, environmental impact, and sovereignty. Participants are guided to analyse how harm can arise across the AI lifecycle — from data collection and model design to deployment and governance — and to situate AI tools within existing regulatory and professional frameworks. The lesson combines conceptual grounding (understanding allocative, representational, and epistemic harms) with practical institutional safeguards such as data protection impact assessments, vendor policy review, bias, sustainability considerations, and sovereignty checks. The goal is not abstract compliance, but the development of institutional capacity to critically assess AI systems before adoption and to maintain human oversight, transparency, and public-interest alignment throughout their use.
LESSON 4:
AI & THE LAW

This lesson explores the complex legal landscape surrounding AI use in libraries, focusing on four interconnected areas: copyright, data protection, the EU AI Act, and liability. It examines how AI systems raise questions about training data and ownership of AI-generated outputs, how GDPR applies when patron data is processed, and when tools may require safeguards such as DPIAs or Data Processing Agreements. The lesson also introduces the EU AI Act’s risk-based framework from prohibited to minimal-risk systems and clarifies libraries’ responsibilities as deployers of AI technologies. Finally, it addresses liability concerns and the importance of strong vendor contracts, encouraging library professionals to apply their critical evaluation skills to AI for governance and compliance decisions.