Guidance
Teaching and Learning Guidance for the Use of Generative AI at TRU.
Recommendations for TRU Instructors
1.
State your policy clearly
in course syllabi, including what is permitted, what is prohibited, and expectations for acknowledgment or citation.
2.
Use unapproved tools with caution
as they may breach TRU’s privacy, IP, or data security standards. As of Fall 2025, Microsoft Copilot is the only GenAI tool that has undergone a Privacy Impact Assessment. Personally identifiable information or other peoples’ work should not be submitted to unapproved tools. Contact learningtech@tru.ca if you have questions on using tools beyond Copilot.
3.
Promote discussion and critical engagement
with AI tools in ways that align with the university’s policies and values.
Working Draft of Guidelines – For Consultation
This draft reflects:
- TRU’s institutional priorities, including the 10-year Strategic Change Goals (eliminating achievement gaps; honouring truth, reconciliation, and rights; leading in community research; and designing lifelong learning).
- Discussions and collaborations of the Data Stewardship and Analytics for Learning and Teaching (DSALT) working group of the Teaching and Learning Committee (TLC) of Senate.
- Review of guidance from peer institutions across Canada.
- Alignment with TRU policies on academic integrity, intellectual property, privacy, acceptable use of technology, and respect for Indigenous ways of knowing.
Guiding Principles for GenAI in Teaching and Learning
1. Human-Centred Learning and Shared Responsibility
We uphold a learner-centred approach where humans remain at the heart of teaching, learning, and academic decision-making. Generative AI is a tool that can support learning and creative expression, but ultimate responsibility for submitted work lies with the individual. Faculty and students share responsibility for setting and understanding clear expectations for GenAI use in learning environments.
Recommendations
- Faculty determine whether and how GenAI is permitted in their courses, subject to program or departmental requirements.
- Course outlines and assignments should explicitly state permitted and prohibited uses of GenAI, including citation and disclosure requirements where applicable.
- Students are responsible for understanding their instructor’s position on GenAI use and should seek clarification when needed.
- Faculty should avoid automated detection tools, which are unreliable and may violate TRU’s privacy policies.
- Faculty and students are responsible for verifying the accuracy of AI-generated content and taking accountability for its use in their work.
2. Inclusive, Responsive, and Committed to Indigenization
In the spirit of Kw’seltktnéws — our interconnectedness with nature, one another, and all things — GenAI should be used to foster inclusive, responsive, and accessible learning environments. This includes Indigenous data sovereignty, the protection of sacred knowledge, and the critical evaluation of systems that may reproduce bias or exclusion.
Recommendations
- GenAI use must avoid harm from false or biased representations of Indigenous communities, cultures, histories, or knowledges.
- Faculty and students should critically evaluate GenAI outputs for Western-dominant ways of knowing to avoid reproducing inequities and perpetuating biases.
- Where Indigenous knowledge is used, TRU community members must follow relevant protocols and respect intellectual property rights.
- Accessibility and equity of access to GenAI tools must be considered before integrating them into coursework.
3. AI Literacy, Lifelong Learning, and Empowerment
We affirm the value of curiosity and lifelong learning in a rapidly evolving technological world. GenAI literacy should empower faculty, students, and staff to critically engage with emerging technologies in ways that enhance their disciplinary fluency, professional growth, and personal learning pathways. We collaborate and share experiences to build a GenAI-informed community that is driven by the needs of our learners.
Recommendations
- Faculty, students, and staff are encouraged to actively engage in learning about GenAI’s capabilities, limitations, and ethical implications.
- GenAI literacy should be embedded in learning activities where appropriate, such as helping learners evaluate AI-generated outputs critically.
- TRU will promote sharing of effective practices and case examples from across disciplines, including through CELT and LTI workshops in partnership with the Writing Centre.
4. Ethical, Transparent, and Sustainable Use
We use GenAI in ethical, transparent, and sustainable ways and make informed decisions that consider its impact. This includes respect for intellectual property, privacy, and informed consent. We discourage reliance on GenAI-detection tools, as they are unreliable and may violate institutional privacy policies. We promote open practices about GenAI use, where applicable. Environmental and cultural sustainability should guide our choices about when and how to integrate these technologies.
Recommendations
- Protect privacy and intellectual property by not inputting confidential, personal, or proprietary information into GenAI systems unless the tool has been approved through TRU’s privacy and data security processes. This includes not inputting the intellectual property of others without their permission.
- Avoid using GenAI detection tools for academic misconduct cases as they are unreliable and may violate institutional privacy policies.
- Evaluate the environmental impact of GenAI systems and thoughtfully use them only when they add significant value to learning or research.
- Be transparent about GenAI use in course activities, research, and administrative work where appropriate.
Next Steps for TRU Community Engagement
- The DSALT Working Group will continue to refine this guidance, and will be reaching out across the TRU community for further collaboration and revision.
- Faculty, staff, and students are invited to provide feedback on the clarity, completeness, and alignment of these principles with TRU’s mission, culture, and teaching values.
- Examples of discipline-specific best practices and syllabus language are especially welcome.
- Feedback will inform the finalization of TRU’s Guidelines for Generative AI, to be supported by workshops, sample policies, and discipline-specific case studies.