Status of This Work
This page summarizes the work and emerging recommendations of the Ad Hoc Committee on the Use of Artificial Intelligence Technologies in Instructional Settings. It is intended to support consultation, discussion, and institutional learning. Final recommendations and any resulting policy or procedural changes will be brought forward through appropriate Senate and governance processes.
Terms of Reference
The Ad Hoc Committee on the Use of Artificial Intelligence Technologies in Instructional Settings was established to provide recommendations that:
- Support the ethical use of artificial intelligence in instructional settings.
- Identify potential revisions to existing policies or protocols for consideration by appropriate Senate committees.
- Explore how artificial intelligence may be integrated into, or limited within, curriculum and instruction to enhance student learning and engagement.
- Examine the implications of artificial intelligence for student assessment and academic support.
- Identify strategies for supporting faculty and staff while maintaining academic integrity.
Report deadline (target):
April 2026
Definitions
Modified from OED 1955Generative Artificial Intelligence (AI)
“The capacity of computers and/or other technology to exhibit or simulate intelligent behaviour; technology used to perform tasks or produce output previously thought to require human intelligence. A (notional) entity exhibiting such behaviour”.
Modified from https://www.umb.edu/media/umassboston/content-assets/learningdesign/pdf/InstructionalSetting10-1-17.pdfInstructional Settings
“Learning and the associated exchange of ideas may take place in many settings, including but also beyond the formal classroom. Instructional settings include but are not limited to classrooms, libraries, group meetings, tutorials, lab sessions, office hours, practicum and placement environments, community service learning, and off-campus venues. Instructional settings may also be virtual, for example, email or telephone-based instruction, chat rooms, and web activities associated with courses.”
Approach
Guiding Values
Guiding Values
- Upholding integrity. The integrity of our academic work is a core principle for faculty, staff and students as members of a community of scholars. It spans the integrity of academic standards and practices as well as the integrity of learning outcomes and how they are assessed.
- Maintaining respect. Our institutions are environments that thrive and grow on a diversity of approaches, perspectives and ideas. The ways in which generative AI tools are to be used, or not to be used, to support and enhance teaching and learning must be subject to the same collegial processes that are used to design and enhance programs of study. This includes respect for the subject matter experts who shape curriculum and teaching practices.
- Building trust. The newness of these tools, coupled with rapid advances and improvements, raise many questions. As we learn and gain experience through responsible use, we can and must build trust across campus communities, Indigenous and other partner organizations.
- Ensuring ethical and legal application. Our use of these systems in education must comply with current and emerging guidelines and regulation of the AI space, as well as the policies and ethical standards of our own institutions.
Modified from: Navigating AI in Teaching and Learning – U15 Canada (September 2024)
Guiding Principles
Guiding Principles
Data is Not Truth
- Human-Centred Learning and Decision Making. Learning, assessing, and decision-making (with or without AI use) must emphasize and prioritize the human.
Respect Privacy and the Collective Good
- Accountable, Ethical, and Responsible Use of AI. Instructors and students, not systems, are accountable for AI use and output; users should be aware of ethical considerations and use AI responsibly.
- Transparency and Disclosure. Students and instructors should declare AI use and its role in instructional settings.
- Accessibility and Equity. No student should be disadvantaged by tool availability, disability, bandwidth, or cost; instructors should provide non-AI pathways to equivalent outcomes.
- Security, Data Privacy, and Intellectual Property. Users should understand and respect data privacy, copyright, and authorship; data security must be maintained.
Don’t Presume the Desirability of AI
- Critical Adoption, Not Blind Enthusiasm. Context matters; ethical use of AI should be embedded in policies and actions, not just rules.
- Design to Support Learning, Not Production. AI should offer explanations, not solutions; encourage critical thinking rather than reduce workload.
Unintended Consequences of AI are Opportunities for Design
- Assessment of Learning must be Redesigned and Reframed. Identify low- and high-risk AI use; focus on critical/creative thinking, process, and reflection.
- Develop a Culture and Community of Practice. Students and instructors share responsibility to understand the strengths and limitations of AI.
Adapted/Inspired from: U15, University of Toronto, IDEO.org, Campus AI Exchange, Government of Canada, and others.
Practices and Considerations
Practices and Considerations
Communication and Support
- CTL for Instructors, SDS for Students
A Culture Shift
- Top-Down vs Bottom-Up
- Faculty Autonomy vs Institutional Consistency
- Risks vs Innovation
- Education vs Punishment
- Tool-Centric vs Use-Centric
Membership
Graydon Raymer- Chair
Barbara Popkie- Administrative Support/Recording Secretary
Ysabel Castle
James Bowen
James Murton
Julie Corkett
Anahit Armenakyan
Adegoke Ojeniyi
Laura Killam
Martin Holmes
Jonathan Muterera
David Hemsworth
Tina Benevides
Mark Wachowiak
Carly Byers
John Allison
Blaine Hatt
Ryan Hehn
Trevor Holmes
Working Groups Recommendations
Academic Integrity and Ethics
Key Questions
The following questions guided the Committee’s discussion:
- What ethical frameworks should guide artificial intelligence use in instructional settings?
- How can academic integrity be maintained in an era of generative artificial intelligence?
Potential Recommendations
The Committee identified the following potential directions for consideration:
- Develop clear institutional policies on artificial intelligence–assisted work.
- Create guidelines for faculty and students on acceptable artificial intelligence use.
- Integrate artificial intelligence ethics into curriculum design where appropriate.
AI Infrastructure
Key Questions
The following questions guided the Committee’s discussion:
- What core technologies and platforms are needed to support artificial intelligence integration?
- How can equitable access to artificial intelligence tools be ensured for all faculty and students?
Potential Recommendations
The Committee identified the following potential directions for consideration:
- Invest in scalable and sustainable artificial intelligence infrastructure.
- Establish partnerships to support shared resources.
- Provide technical support and training for faculty.
- Examine the use of artificial intelligence tutors and learning supports.
Assessment of Human Creative and Critical Thinking
Key Questions
The following questions guided the Committee’s discussion:
- How can creativity and critical thinking be assessed when artificial intelligence tools are involved?
- Can artificial intelligence complement rather than replace human intellectual processes?
Potential Recommendations
The Committee identified the following potential directions for consideration:
- Redefine assessment rubrics to account for artificial intelligence–assisted work.
- Encourage assignments that require human judgment and originality.
- Promote blended approaches that combine artificial intelligence tools with human reasoning.
Community of Learnership
Key Questions
- How can a culture of responsible artificial intelligence use be fostered among educators?
- What strategies support collaboration and shared learning about artificial intelligence?
Potential Recommendations
The Committee identified the following potential directions for consideration:
- Shift from a community of practice to a community of learnership.
- Offer regular workshops and case studies.
- Create an Artificial Intelligence Teaching Chair role and embed professional development opportunities.
- Ensure diverse perspectives are included in decision-making processes.
Student AI Literacy
Key Questions
The following questions guided the Committee’s discussion:
- What artificial intelligence literacy skills will students need for future careers?
- What shared policies and supports make artificial intelligence literacy sustainable?
Potential Recommendations
The Committee identified the following potential directions for consideration:
- Define core artificial intelligence literacy competencies.
- Develop supports such as tutorials, partnerships, and access to technology.
- Consult with students and faculty to refine and sustain artificial intelligence literacy strategies.