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.

Academic cheating is a growing problem in educational institutions around the world, and it can take many forms, including plagiarism, collusion, and cheating during exams. Academic cheating can have serious consequences for students, educators, and the academic community as a whole.The Academic Integrity Instructor Resource Platform is a growing and evolving online resource designed to support and promote academic integrity among faculty members. This resource serves as a go-to platform for faculty seeking guidance, tools, and best practices to foster a culture of academic honesty and prevent plagiarism and cheating within their online, online-live, hybrid, and face-to-face, classrooms. 

An infographic with Quick Facts on AI in higher education by Mandy Jordan, Researcher, SHSU Online

The U.S. Department of Education Office of Educational Technology’s new policy report, Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations, addresses the clear need for sharing knowledge, engaging educators, and refining technology plans and policies for artificial intelligence (AI) use in education. The report describes AI as a rapidly-advancing set of technologies for recognizing patterns in data and automating actions, and guides educators in understanding what these emerging technologies can do to advance educational goals—while evaluating and limiting key risks.

This video covers using the Blackboard Learn Ultra AI Design Assistant to auto-generate suggested course content in the form of Learning Modules.

This Quick Start Guide covers using the Blackboard Learn Ultra AI Design Assistant to auto-generate suggested course content in the form of Learning Modules.

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.

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, city governments worldwide are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more accessible with technology and more connected.

In this section, you will learn about the importance of ethical considerations and implications of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs) like ChatGPT. This section highlights that LLMs are not inherently good or bad. Instead, the importance of user engagement in ethical practices is emphasized to ensure responsible use of these tools.

Ethical considerations for educators include attention to student privacy, expectations, and consequences—all of which should clearly be defined in syllabus statements, classroom policies, or institutional statements. Meanwhile, ethical implications exist involving varying ethical standards for how people approach LLMs differently, how human and machine bias influence GenAI, and how style guides differ on citing information garnered from ChatGPT.

After reading this section, you should be able to articulate your own ethical queries and concerns related to LLMs, such as ChatGPT, both as a general user and an educator.

Author: C. Anneke Snyder
Contributors: Gwendolyn Inocencio, Mary Landry, Jonahs Kneitly
Designers: Irene AI, Sweta Kailani
Supervisors: Terri Pantuso, Sarah LeMire

This video covers using suggestions from Unsplash for Course Bannvers via Blackboard Learn Ultra's AI Design assistant.

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.

This two-part resource is designed to support instructors and students as they navigate the presence of generative AI tools, specifically Large Language Models (LLMs) such as ChatGPT, in the rhetoric and composition classroom. Part I of this resource offers an instructor-focused introduction to what LLMs are and how they operate, as well as an in-depth exploration of the privacy concerns and ethical considerations related to using a tool like ChatGPT. Additionally, Part I provides insights on the practical application of LLMs within the realm of reading and writing in the rhetoric and composition classroom, while promoting a modified stasis theory as a strategy for evaluating any generated output.

Part II of this resource offers student-focused tutorials that demonstrate how ChatGPT can augment the writing process for assignments commonly given in a rhetoric and composition course. These tutorials cover the evaluation essay, rhetorical analysis, Rogerian argument, annotated bibliography, and research essay—all while promoting the responsible and ethical use of AI in writing and research. With this comprehensive resource, instructors and students can not only build confidence in their understanding of generative AI within academia, but also build digital literacy that will serve them in the world beyond.

The handout summarizes core messages in a report by the US Department of Education, “AI and the Future of Teaching and Learning: Insights and Recommendations".

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 resource which emerged from a keynote delivered at Husson University includes both an annotated slide deck with links to resources and examples and includes a Prompt Guide with example prompts to try out.

In this section, we will examine how generative AI (GenAI) tools may assist with academic reading and research. Examples of content generated by ChatGPT will show how GenAI may be incorporated into a classroom setting. Each section offers suggestions for use and various strategies that could be incorporated for those who wish to allow the use of these tools for assignments. Included throughout are suggestions on how to promote students’ ethical and effective use of these tools and to possibly limit their use if desired. By the end of this section, you should be able to use GenAI to support reading practices.

Author: Jonahs Kneitly
Contributors: Gwendolyn Inocencio, Mary Landry, C. Anneke Snyder
Designers: Irene AI, Sweta Kailani
Supervisors: Terri Pantuso, Sarah LeMire