This Framework is adapted by WCET from Cecilia Ka Yuk Chan's 2023 Comprehensive AI Policy Education Framework for University Teaching and Learning. It is free and openly available to anyone. It will complement the forthcoming WCET Artificial Intelligence Toolkit.

Courses on Artificial Intelligence (AI) and Librarianship in ALA-accredited Masters of Library and Information (MLIS) degrees are rare. We have all been surprised by ChatGPT and similar Large Language Models. Generative AI is an important new area for librarianship. It is also developing so rapidly that no one can really keep up. Those trying to produce AI courses for the MLIS degree need all the help they can get. This book is a gesture of support. It consists of about 95,000 words on the topic, with a 3-400 item bibliography.

Bloom’s Taxonomy is often used as a resource to help higher education faculty assess what kinds or “levels” of learning are planned based on course-level outcomes and, relatedly, to align appropriate activities and assessments to support student learning and success. Oregon State University's E-Campus used Bloom’s Taxonomy as a touchstone for reconsidering course outcomes and student learning in the age of generative AI. The visual outline and accompanying narrative are intended for faculty use as a guide to reflect on their activities, assessments, and (possibly) course outcomes and begin to identify what changes may be needed to ensure meaningful learning going forward.

This overview by CoSN delves into the facts and myths of AI:
"If you've had time to pay attention, you may have picked up on the noise happening in K-12
circles around ChatGPT and the myriad of other AI web tools that suddenly are in the spotlight.
Why all the noise and how do EdTech minded administrators respond?"

The Ethics of AI is a free online course created by the University of Helsinki. The course is for anyone who is interested in the ethical aspects of AI – we want to encourage people to learn what AI ethics means, what can and can’t be done to develop AI in an ethically sustainable way, and how to start thinking about AI from an ethical point of view.

The projects in this guide use a student-driven approach to learning. Instead of simply learning about AI through videos or lectures, the students completing these projects are active participants in their AI exploration. In the process, students work directly with innovative AI technologies, participate in “unplugged” activities that further their understanding of how AI technologies work, and create various authentic products—from machine learning models to video games—to demonstrate their learning.

This guide provides student-driven projects that can directly teach subject area standards in tandem with foundational understandings of what AI is, how it works, and how it impacts society.

This guide provides student-driven projects that can directly teach subject area standards in tandem with foundational understandings of what AI is, how it works, and how it impacts society. Several key approaches were taken into consideration in the design of these projects. Understanding these approaches will support both your understanding and implementation of the projects in this guide, as well as your own work to design further activities that integrate AI education into your curriculum.

In this guide, students’ exploration of AI is framed within the context of ethical considerations and aligned with standards and concepts, and depths of understanding that would be appropriate across various subject areas and grade levels in K–12. Depending on the level of your students and the amount of time you have available, you might complete an entire project, pick and choose from the listed activities, or you might take students’ learning further by taking advantage of the additional extensions and resources provided for you. For students with no previous experience with AI education, exposure to the guided learning activities alone will create an understanding of their world that they likely did not previously have. And for those with some background in computer science or AI, the complete projects and resources will still challenge their thinking and expose them to new AI technologies and applications across various fields of study.

This meta-analysis of research in Artificial Intelligence (AI) in higher education provides a synthesized examination of the emerging trends, applications, and implications of AI in this specialized educational sector. Drawing on articles, reports, and academic publications, this review aims to offer an integrated perspective on the dynamic role AI plays in higher education.