Introduction

Introduction

Innovations in information technologies have historically led to disruptions and transformations of higher education. Those changes, however, occurred over long periods of time.¹ In many ways, the advent of generative artificial intelligence is a constitutive component of the digital revolution in information technologies that higher education has been confronting since the latter half of the twentieth century. It has had some immediate effects on universities, primarily in its disruptions to the learning environment and academic integrity. Peer review and research integrity have also experienced some initial disruptions. And there are several examples of promising applications of AI for discovery and research acceleration. A deeper consideration of the histories of universities and information technologies suggests that any potential transformational changes to higher education are more likely to occur over the long term and intersect with other threads of technological change and adaptation over the past century.

Though the effects of generative AI on higher education are uncertain, and likely to manifest over many years, they still necessitate preparation and adaptation across all the functions of a university. In particular universities need to prepare for changes in the learning environment, the research enterprise, and administrative functions. 

TMU has demonstrated good preparedness and leadership concerning the effects of AI on the university and its operations. Our Senate Learning & Teaching Committee was engaged on this issue as early as the summer of 2022, prior to the public release of ChatGPT, and has remained active on questions about AI in learning and teaching ever since. In August 2023, the Centre for Excellence in Learning & Teaching (CELT) published the university’s first guidance on AI and updated that guidance (in collaboration with our Senate Learning and Teaching Committee) into more formal principles and guidelines in 2024. CELT also established a cross-campus working group on AI in Learning and Teaching and a community-of-practice. The Academic Integrity Office has led revisions to Senate Policy 60 to clarify language concerning academic misconduct involving AI tools. CELT has provided numerous workshops, resources, and consultations for faculty and contract lecturers to help with adapting teaching to a learning environment that has been disrupted by AI. Student Life and Learning Support (SLLS) has offered co-curricular programming to help students navigate learning and AI and TMU Libraries has launched an AI fluencies badging program. The Senate has now held several committee-of-the-whole discussions on the topic of AI in higher education.

 In March 2025, the task force was established and convened to develop a report for the Executive Group through the Provost & Vice-President, Academic and the Vice-President, Administration & Operations.

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Process overview

The task force established four working groups focused on the following areas of university operations:

• Learning and teaching
• Scholarly research and creative activities
• Student experience
• Administration and operations

Additionally, the task force consulted the TMU community including faculty, contract lecturers, students, and staff. During the Winter 2026 semester, the task force received 88 written responses to an online feedback survey. There were also 129 registrants for four in-person town hall meetings and one virtual town hall meeting. The task force chairs also engaged in direct outreach consultations with other groups and invested parties across the university during the course of the Winter semester, including the faculty association executive and the contract lecturers union executive. Working groups did further consultations with staff in functional units related to their areas of focus.

For the purposes of this report, the task force considered AI technologies that can generate text, code, images, video, audio, and other outputs from prompts based on deep-learning models (including Large Language Models, Diffusion Models, and others). The scope of this report is also inclusive of agentic AI tools and systems capable of autonomous tasks and multistep workflows.

This report endorses the view of the Council of Ontario Universities AI Task Force regarding adaptation as the appropriate response of the sector to AI technologies and their effects on higher education. As the COU report notes, adaptation refers to “reimagining institutional practices, operational structures, and expectations in response to AI mediated knowledge and work, not simply adopting or scaling new tools.”² The primary objective of any adaptation to AI at TMU should be to support and reinforce human flourishing for the fulfillment of our broader institutional mission.

This report is broken down into seven sections. The first offers a set of common institutional principles based on the analysis of the task force working groups and consultations with our community. The second section proposes a framework for AI adaptation at TMU, outlining the key areas where the university will need to make adjustments to mitigate the effects of AI on higher education and harness potential opportunities. The third section reviews existing policies and guidelines providing advice on future revisions and identifying gaps. The fourth section summarizes existing AI training offered at TMU and proposes future directions for supporting the development of AI skills and competencies for all community members. Section five surveys the opportunities ahead for TMU as it adapts to a higher education sector in which AI may play an increasing role in our work and how it might advance the university’s mission. Section six follows that discussion of opportunities with reflections on some of the critical challenges TMU faces in adapting to an AI-disrupted sector. The final section summarizes the task force’s findings by offering a series of recommendations. 

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