The impact of generative artificial intelligence (AI) on our classrooms, assessments, teaching practices, and learning environments has quickly emerged as a critical priority across all faculties in the past few years. For many educators, these changes have prompted important questions about assessment design, academic integrity, student learning, disciplinary practices, workload, and the evolving role of teaching itself. While some instructors view these developments with feelings of curiosity and hope, there are also understandable feelings of uncertainty, frustration, and concern.
The educational implications of generative AI are complex. There is no single approach that will be appropriate for every discipline, course, instructor, or learning outcome. We have heard clearly that faculties are seeking support as they navigate these realities, and we are committed to helping meet that need.
Our role at the MacPherson Institute is to support evidence-informed approaches and to offer guidance that recognizes a range of perspectives. While some instructors, departments and faculties are actively exploring opportunities to integrate AI into teaching and learning, others may be seeking ways to limit or reduce its role to protect critical learning processes or disciplinary practices.
At the core of these conversations is a principle that remains unchanged: teaching and learning deeply matter. Thoughtful teaching practices, meaningful learning experiences, strong relationships between instructors and students, and carefully designed assessments continue to be fundamental to student success. As technologies evolve, our teaching and learning community must ensure that our educational practices continue to support student learning and growth.
The MacPherson Institute has various resources and is preparing additional supports to address this critical priority and will continue to share guidance and resources through the 2026-27 academic year.
To access these resources visit the Resources and Teaching in the Age of AI: Examples tabs.
The MacPherson Institute and campus partners have developed a variety of resources to support instructors who are navigatingthe educational implications of generative AI. There is no single approach that will be appropriate for every discipline, course, instructor, or learning outcome. Whether you are looking for opportunities to integrate AI into your teaching or seeking ways to limit or reduce its role to protect critical learning processes or disciplinary practices, the following resources offer evidence-informed approaches with guidance that recognizes a range of perspectives.
This resource is intended to help you, as an instructor, reflect on your views and assumptions about generative AI and how they align with a specific course’s intended learning outcomes, assessments, and context. Through a series of prompts, you will develop a course-specific generative AI statement for inclusion in a course outline, Avenue site or both, that communicates your generative AI-related expectations and rationale.
This conversation guide is designed to provide practical, learning-centered approaches for instructors to discuss GenAI with students, whether at the start of a course or in response to emerging questions and situations. It features adaptable discussion prompts, facilitation strategies, and follow-up suggestions that aim to help instructors engage students in thoughtful dialogue about the role of GenAI in their learning.
This Guidebook provides helpful information for educators to better understand the various aspects of how Generative AI may apply to the teaching and learning space. Educators will learn about the general limitations and risks, contexts for McMaster University, using AI as an instructor, student perceptions, opportunities for student usage, and guidance on redesigning assessments and other approaches.
This resource aims to be your companion in making informed, strategic decisions about using AI to enhance higher education. It begins with a foundational approach to assist readers in aligning AI with their educational curiosities and goals, followed by an exploration of AI tools that can be applied in teaching and research contexts. Additionally, readers will learn strategies for monitoring, evaluating, and reflecting on the use of AI.
This online module aims to provide an understanding of generative AI to help educators think through how these technologies intersect with teaching practices. Whether you have reservations or enthusiasm about AI in education, this learning module offers a space for exploration and thoughtful consideration.
Educational developers from the MacPherson Institute can design and deliver program and department specific workshops and training related to generative AI. Reach out here to mi@mcmaster.ca to discuss.
Learn from others working in the generative AI space in this McMaster Community of Practice.
MI Postdoctoral Fellow Ben Lee Taylor is conducting a study on generative AI and assessment in higher education. Results are forthcoming, but one output includes an open access repository (CC licensed for free use) of sample assessments that address or respond to generative AI.
This resource was created in 2024-2025.
Copilot is a generative AI chat assistant. McMaster faculty and staff can leverage Copilot for various tasks including searching for relevant information on the web, generating content, rewriting/improving/optimizing existing content, and creating images based on your descriptions.
Scite is a feature-rich AI-powered assistant that can help you investigate research questions by finding and evaluating relevant literature. McMaster students, faculty, and staff have full access to scite with a valid MacID.
This podcast delves into the ethical and practical questions of generative AI for the McMaster campus community, bridging the gap between knowledgeable educators, students, and practitioners and those less familiar with AI technology. Each episode explores the complexities of AI, its potential for innovation, and the challenges it poses.
The MacPherson Institute is working with faculties and instructors to gather and share examples of how to use AI or limit the use of AI in teaching. There is no single approach that will be appropriate for every discipline, course, instructor, or learning outcome. Whether you are looking for opportunities to integrate AI into your teaching or seeking ways to reduce its role to protect critical learning processes or disciplinary practices, the following examples from McMaster instructors offer a range of ideas and perspectives for your consideration.
Have an example you would like to share?
Submission process coming soon!
Dr. Felicia Vulcu Biochemistry and Biomedical Sciences Faculty of Health Sciences
This example shows how one instructor introduced AI policies related to student work for lab reports as well as utilizing ChatGPT to generate overviews of class AI policies for students.
This example shows how one instructor utilized ChatGPT to assist with developing teaching activities focused on retention of students’ critical thinking and analysis skills in multiple lecture and lab based courses.
This example shows how utilizing ChatGPT to assist with the development of life-like scientific images can demonstrate what students can expect to see during their laboratory experiments.
Dr. Sarah Whitwell History Faculty of Humanities
This example shows how it is possible to leverage Generative AI to help students build critical thinking and feedback skills.
Dr. Kevin Browne and Dr. Seshasai Srinivasan Software Engineering Technology Faculty of Engineering
This example shows how a scalable asynchronous interview format for oral examinations was used to solve challenges with traditional written assessments and online quizzes as a result of AI.
Dr. Mike Welland Engineering Physics Program Faculty of Engineering
This example shows how unrestricted AI use and oral interviews were used instead of a written assignment to assess students actual understanding of assignment material.
This example shows how Google’s Gemini Notebook was used to create an AI chatbot trained on instructor-selected sources to build structured overviews of course content and answer student queries.