Opportunities
Opportunities
Generative Artificial Intelligence Task Force Report
AI technologies have the potential to enhance the core research and educational missions of universities. Knowledge generation could be vastly expanded and potentially accelerated, increasing our capacity to understand the world and address key problems and challenges. Learning could be further enhanced by the thoughtful, responsible, and ethical application of AI technologies, better preparing our students for future careers and expanding their ability to make positive change. And the operation of universities could be made more efficient with the use of human-centred AI applications to improve services and institutional planning.
Furthermore, universities have the opportunity to play key roles in both innovations in AI technologies and broadening knowledge and understanding of the effects of AI on society, the economy, and the environment. Universities were fundamental players in the development of the foundational technologies that now drive generative AI. They continue to innovate and improve upon those technologies. They are also at the forefront of the emergence of novel applications of AI in a range of fields and industries, particularly in healthcare. There is substantial opportunity for TMU to make contributions to this work, further enhancing and reinforcing the societal importance of universities.
All such applications of AI technologies require careful attention to critical challenges and risks (which we outline in the following section), but they also offer important opportunities likely to be yielded over the long term.
There are opportunities for applications of AI technologies to expand, accelerate, and improve knowledge generation at all stages of the research lifecycle within and across disciplinary contexts:
Brainstorming and ideation: This initial stage of research involves generating novel ideas. Researchers can engage AI in the process of ideation and leverage the technology to organize ideas, identify terminology, aid in the creative process, and structure a preliminary plan. AI tools can also help researchers locate relevant existing literature and data.
The continued integration of AI functionality in search and discovery environments (such as search engines and research databases) will likely make the use of AI in search a standard component of research for faculty and students. As such, the integration of these skills into undergraduate and graduate education will become more important over time.
Development of Grants: AI tools can serve as collaborative aids in the process of drafting research grants, helping reduce the time spent on administrative and writing tasks. They can also assist in optimizing grant applications and budgets with potential to improve application success rates (see Automated Grant Feedback at Western University (external link) ). There is no single, universal policy on the use of AI in the development of grant proposals. Applicants should consult the specific policy of the funder in question. If in doubt as to a grant funder’s policy, it is prudent to assume that transparency and disclosure are required.
Data Collection & Analysis: The application of new AI tools could also complement existing research methods for large data analysis, whether applied to numerical, textual or other types of data. Researchers systematically gather relevant information through various methodologies then rigorously process and interpret that data to identify patterns, draw inferences, and test hypotheses. AI may also validate data, identify inconsistencies, produce metadata, mine large corpuses of text, or transcribe interviews. Use of AI tools should be disclosed as part of the description of a data collection protocol in the research methodologies.
Content Creation: AI tools are also being used as an aid in drafting and revising content through writing, data visualization, and image generation. Publisher policies vary as to whether and to what extent AI tools can be used in generating content. Most publications permit the use of such tools in formatting and copy editing. Authors are generally required to disclose any use of AI tools in their manuscripts, detailing which tools were used and for what purpose. Publishers also typically emphasize that authors remain fully responsible for the accuracy, originality, and ethical soundness of their work, even if parts were generated or assisted by AI.
In addition to the application of AI tools through the research lifecycle, TMU has deep pockets of specialized research in machine learning and advanced applied AI.
Examples include:
- Responsible Artificial Intelligence Project
- Creative AI Hub (Creative School)
- Advanced Artificial Intelligence Initiative Laboratory
- Image Analysis in Medicine Lab
- Quantitative Image Analysis in Medicine (external link)
- The Dais (external link) (AI reports)
- Other areas of AI associated research at TMU
Such research has the potential to lead to breakthroughs and transformational knowledge generation that can make contributions to industry and society.
TMU researchers also have the opportunity to lead in innovations that expand and improve AI technologies. Universities were essential to the development of the underlying technologies that support new generative AI tools today. Our basic research should continue to improve upon those technologies and lead the way to future discoveries and innovations. This research should also include the development of new computing infrastructure that is more energy efficient and conserves water to address the environmental effects of the compute capacity needed to support AI technologies.
Finally, across a wide range of disciplines, there is an urgent need and opportunity for critical scholarship on the social, economic, and environmental effects of AI. Today we are grappling with understanding the effects of AI on society. Is AI replacing entry-level jobs? How is AI transforming the way we think and learn? What are the social effects of AI on existing and underlying inequities in society? In what ways have AI technologies begun to alter politics? These questions are opportunities for our researchers to generate new knowledge that can make significant contributions to how people will confront and adapt to technological change and disruption.
AI technologies have had some of their most immediate effects on higher education in the realm of learning and teaching. As previously mentioned, they have disrupted the learning environment by challenging traditional methods of assessment of student learning and undermining academic integrity. However, these technologies also present some opportunities to potentially improve learning and teaching and further enhance the relevance of university education.
Students now widely use AI to support their learning.6 In many ways, these technologies show significant promise to improve self-directed learning and study. Students can quickly generate study support materials (flash cards, practice tests, summaries of notes, etc…). They can also work with AI tools as collaborative learning partners (brainstorming, Socratic dialogues, simulations). AI tools can expand accessibility for study support by offering timely help. Some students may also benefit from and prefer seeking anonymous tutoring support from AI assistants. However, such applications of AI for learning require educational scaffolding to counter cognitive offloading and support cognitive augmentation.7 This also requires clear communication and training on academic integrity.
There are several opportunities to improve accessibility in learning environments through the use of AI technologies. Translation of course materials into more accessible language, the generation of alt-text for images and video, AI-assisted notetaking, and other AI-powered assistive technologies are just a few examples of the ways in which AI could aid TMU students and help the university achieve its accessibility goals and obligations.
Academic programs have the opportunity to be more responsive to student learning needs and changes in professions and fields by integrating AI competencies and skills into curriculum where it is appropriate.
Several undergraduate and continuing education programs at TMU are already beginning to take these steps:
| Select Undergrduate Programs with Integrated AI Competencies | ||
| Faculty | Program | Details & Context |
| The Creative School | Image Arts (Film & Photography) | Explicit curriculum modifications made to address AI. |
| Creative School Experience courses | Included a formal dimension to address AI. | |
| Journalism | Currently integrating AI considerations into the curriculum. | |
| Graphic Communications Management | Currently integrating AI considerations into the curriculum. | |
| Faculty of Engineering and Architectural Science | All undergraduate engineering programs | Use of AI tools a component of graduate attributes for the use of engineering tools. AI foundations taught in several courses (ie. ELE 888 and ELE 900). |
| Ted Rogers School of Management | Business Technology Management | Implemented curriculum modifications addressing AI several years ago. Added new concentration in AI & Analytics. |
| Hospitality and Tourism Management / Health Services Management | Modifications implemented that address AI in relevant professions. | |
| Faculty of Science | Computer Science & Cyber Security | Integrated explicit coursework dedicated to dealing with AI issues and teaching AI foundations. |
| Biomedical Science, Biology, Chemistry, Mathematics | Changed curriculum and instructional approach to contend with the impact of AI. | |
| Faculty of Arts | Liberal Studies | Select courses consider the role of AI within the context of Society, Ethics, Culture, etc. |
| The G. Raymond Chang School of Continuing Education | Data Science, Machine Learning, and Applied AI certificate | Certificate program recently updated to enhance and emphasize applied AI. |
| Master AI-Assisted Python Coding Course | New course integrated into Game Development Certificate and the Cybersecurity, Data Protection and Digital Forensics Certificate | |
| AI for Productivity Curv Microcredentials | Stackable competency-based microcredentials in data analysis, data visualization, presentations, reports, and prompting. | |
| AI Powered Futures program (partnership with Coalition of Innovation Leaders Advancing Respect) | Industry sponsored partnership program for technical training, mentorship, professional networking, and career development. | |
As programs continue with the cycle of Periodic Program Reviews, there are further opportunities to enhance the relevance of TMU degree programs through curriculum modifications that integrate critical AI skills and competencies based on changing professional expectations and standards. These modifications also have the opportunity to emphasize and reinforce the uniquely human capabilities that AI cannot replace (empathy, critical thinking, emotional intelligence, collaborative teamwork, physical craft, ethical decision-making), but are essential for preparing future-ready graduates. Such curriculum modifications will better prepare students to adapt to AI-influenced labour markets.
The disruption that AI has instigated in the learning environment also presents opportunities for renewed and enhanced teaching methods. This is particularly important for methods of assessing learning. There are opportunities to revise and update assessment methods to focus on process over product and enhance authentic assessment based on applied learning and simulations.
Across a range of services that support student success, there are potential opportunities and benefits to improve the student experience at TMU through the use of AI tools. These include services that cut across a number of different areas particularly in the Registrar’s Office and Student Affairs.
Potential benefits include:
- Administrative efficiency and faster response times for student services
- More consistent communications and document preparation
- Enhanced staff productivity and effectiveness
- Better student preparation for workforce expectations regarding AI
- Customized approaches for student support and engagement
- Improved access, inclusivity, and innovation in the learning environment
There are several ways in which AI tools could be deployed to support student career readiness. Career services may use tools that help to improve job interview preparation, modifications to resumes and cover letters, and simulations for job preparedness. AI tools may also assist students with job searches and career planning.
In student learning support, there are several opportunities to enhance training for students to better guide the way that students may use AI in their learning. As noted above, students now commonly use AI tools to support their learning in a variety of ways. However, student use of AI for learning must be scaffolded and supported with sufficient training to ensure that AI is augmenting their cognition rather than replacing it. Such training and support will help students to responsibly leverage AI tools for tutoring, problem-solving, planning, and to help better manage their busy academic and non-academic workloads. There are further opportunities to improve student accommodation supports with AI technologies. There are numerous assistive technologies that utilize AI to the benefit of the student experience and learning.
The university also has the opportunity to take advantage of AI technologies for administrative processes that may significantly improve the student experience at TMU. This may include technologies to support better registrarial services like scheduling, calendar planning, admissions, curriculum management, and communications. AI-powered data analysis could further enhance student success by expanding institutional capacity to assess and identify service needs that could improve degree progress, student persistence, and graduation rates.
There are several opportunities that will allow TMU to streamline administrative workflows, enhance staff support, and maintain robust data sovereignty, data privacy, and cybersecurity. Responsible deployment of AI tools for administration and operations could lead to improvements in operational efficiency, institutional knowledge management, policy navigation, data-informed decision-making and strategic planning, enhanced service delivery, and communication.
The integration of AI into the day-to-day operations of the university offers the opportunity to reduce the administrative burden of staff and eliminate bottlenecks in repetitive tasks. Using AI to prepare drafts of correspondence, standard operating procedures, meeting minutes, and memos can help to significantly reduce turnaround times for high volume documentation. Any such AI integrations must always ensure human oversight and authoritative review of outputs for accuracy and maintenance of institutional reputation. There is also the potential to securely implement AI triage tools to categorize and route complex inquiries in dense administrative units, reducing manual sorting and data management. Further efficiencies could be achieved through the adoption of AI summary tools to capture meeting notes and generate summaries and action items to keep teams aligned and make progress on institutional priorities.
TMU has a vast collection of Senate and administrative policies, collective agreements, and guidelines that are essential to its operations. The development of AI policy assistants could provide secure internal facing tools trained exclusively on TMU documents to allow staff to quickly locate and interpret complex institutional regulations, improving service levels and turnaround times. Further, AI tools could also be used to improve employee onboarding, professional training support, and standard operating procedures.
Institutional planning and analysis could be further enhanced by leveraging AI technologies to improve data informed decision support and strategic planning. AI systems can assist with rapid pattern analysis empowering administrative planners to identify trends in enrolment, space utilization, and operational expenditures.
There are significant opportunities to improve service delivery and communication with the support of AI technologies. Chabots and other tools could be deployed to support 24/7 basic inquiries and services for community members answering questions and providing timely feedback.
Finally, AI supported coding could assist in the work of CCS and other units across the university. Locally developed internal applications can now be easily created and deployed using AI coding tools, which opens up the possibility of creating highly specialized custom applications for a wide range of uses across the university, both for individuals and small teams. Such applications in the past may have been financially infeasible to develop or not available in the market. With AI supported coding, the cost and time of development for such applications is significantly reduced. AI code review could also help support cybersecurity efforts at the university, identifying vulnerabilities and fixes. AI-generated applications, however, require significant cybersecurity assessment and should be approached with caution.
6 A recent report by Anthropic reveals the extent and character of student uses of AI chatbots for learning, “Anthropic Education Report: How university students use Claude,” accessed on April 15, 2025, https://www.anthropic.com/news/anthropic-education-report-how-university-students-use-claude (external link)
7 Jason M. Lodge and Leslie Loble, "Artificial Intelligence, Cognitive Offloading and Implications for Education,” (Centre for Social Justice and Inclusion/Network for Quality Digital Education, 2026), accessed March 26, 2026, https://doi.org/10.71741/4pyxmbnjaq.31302475 (external link)