Building a strong foundation for AI-enabled research, education and care at uOttawa

But responsible AI adoption depends on more than interest in the technology. For AI to meaningfully support research, education or care, people need the right foundations: secure tools, usable data, clear guidance, practical training and digital infrastructure that reflects the needs of the communities using it. Across the University of Ottawa, that means creating common supports that help people leverage and use AI responsibly. In medicine, it also means adding the specialized layers needed for highly sensitive health data, complex research environments, clinical partnerships and pathways into care.

At uOttawa, a common institutional foundation is being established to support responsible AI use across the institution. That work is being advanced in part by Information Technology, where Ilva Peci, Director of Technology Enablement, helps guide efforts related to AI literacy, tool adoption, governance supports, institutional platforms and practical resources for the uOttawa community. These efforts create a shared baseline that faculties and services can build on, according to their needs. 

Her team’s approach has been grounded in real needs. Rather than asking faculties and services what tools and technologies they wanted, they asked what challenges they were trying to solve.

The work began not with a focus on AI itself, but with a focus on people. By listening to the challenges and opportunities emerging across the university, the team identified areas where AI could provide practical benefits, while also putting in place the supports needed to help users adopt the technology responsibly and confidently.

That approach has shaped a growing body of work across the university. It includes an AI roadmap, an AI Advisory Committee, guidance on generative AI, an institutional AI Hub, AI literacy events and bootcamps, and a funded AI program that brings together projects across administrative, academic and research areas.

For members of the uOttawa community looking for a place to begin, the AI Hub is becoming a key resource. It gathers information, guidance and learning opportunities in one place, with resources for professors, students and employees. The Hub responds to one of the clearest needs Peci’s team heard through consultations and survey work: people want to learn more about AI, but they need practical guidance on where to start, what tools are available and how to use them appropriately.

That focus on awareness and training is central to the university’s approach. AI adoption is not a software rollout alone. People need to understand how to use AI tools, when not to use them, what risks to consider, how to assess outputs, and how these tools fit into their work as researchers, educators, learners or staff.

The work has also included structured testing and rollout of various tools. For example, web-based Microsoft Copilot is available to the uOttawa community as a supported generative AI option within the university environment. A separate Microsoft 365 Copilot pilot helped Peci’s team assess whether deeper integration into daily work could create measurable value. The pilot showed a conservative estimated capacity gain of 10 to 14 per cent, consistent with what other universities were seeing. A year later, feedback from the same users was even more positive, as the tool had matured and people had become more familiar with how to use it.

The next phase is now underway, with 1,000 Microsoft 365 Copilot licences being granted through a structured allocation process. A large share is going to faculties, and access is paired with mandatory training in both product use and responsible practices.

Other AI-enabled services are helping teams learn what works in real settings. An HR chatbot has supported employees with common questions following the Workday implementation. A Health and Wellness Companion helps students navigate available services without providing health advice. Lumi, a personal tutor capability within Brightspace, was piloted with students as part of a “buy before build” approach that uses existing tools where they can meet a need.

For researchers, access to the right supports can shape how quickly ideas move forward. AI research often requires computing capacity, data, specialized tools, technical expertise, funding awareness and knowledge of available services. Researchers may not always know where to go for help, what resources exist, or how to access national research computing infrastructure.

A research support chatbot is now being tested to help researchers find relevant services and resources more quickly. More broadly, the work reflects a shift toward institutional capabilities that can support more than one project or unit, while still being developed and adopted responsibly.

That shift is especially relevant for health and medical research, where the gap between an AI idea and real-world use can be wide. Teams need more than a model or a licence: they need secure data environments, computing capacity, privacy and governance guidance, training and support to work seamlessly across university, hospital and research institute settings. 

In health and medical research, AI tools are already being used and evaluated across areas such as prediction, documentation, imaging, triage, education, patient-facing supports and clinical decision support. Large language models (LLMs) and other AI tools are also being studied in their own right, including their accuracy, limits, risks and potential roles in research, teaching and practice.

Building on the institutional-level work taking shape across uOttawa, the Faculty of Medicine is also looking at how to establish strong digital foundations across its unique ecosystem. That ecosystem includes researchers, educators, learners, clinicians, hospitals, research institutes, health system partners and companies, beyond the University of Ottawa, working to bring new solutions into practice, and may involve additional layers required for medical research, health data, clinical collaboration and responsible medical AI innovation. 

Health data is sensitive. Research environments are highly regulated. Clinical workflows are complex. Collaboration often extends across organizations. A promising AI tool may need validation, workflow integration, privacy review, regulatory consideration, procurement planning, commercialization support and sustained operational capacity before it can be used more broadly.

Isabelle Tanguay, Senior Lead Strategist, IT and Partner Relations, joined the Faculty of Medicine to support its digital transformation and help shape the technology direction needed for that environment. Her work is focused on the systems, relationships and infrastructure that can support medicine, research, education and hospital collaboration over the long term. That includes the idea of a trusted AI ecosystem: an environment where people, data, platforms, governance, infrastructure and partnerships work together to support innovation, particularly in settings where privacy, security and trust are essential. 

“In the Faculty of Medicine, research, education, clinical partnerships and innovation are deeply connected,” says Tanguay. “Our focus is on creating an ecosystem that gives people trusted foundations and the additional supports medicine requires, while enabling innovation to happen close to the expertise and the problems being solved.”

For the Faculty of Medicine, this is not only a technology conversation. It is about creating the digital environment needed to support researchers, educators, clinicians, learners and partners as they use AI to address real health care challenges. That includes education and training, responsible use, data readiness, infrastructure, collaboration and support for innovation that may eventually move into practice or the marketplace.

The work underway points to a practical reality for academic medicine: AI will not advance through tools alone. Researchers, educators, clinicians, learners and staff need the knowledge, supports, data environments and infrastructure to use those tools well.

For uOttawa, the next phase is about building that capacity in a coordinated way. The institution-level work is helping people access tools, training and guidance. The Faculty of Medicine’s digital transformation work is focused on the more specialized environment of medical research, education, clinical partnerships and health innovation. Together, these efforts are helping create the conditions for AI to support stronger research, better education, responsible innovation and, over time, solutions that can improve health systems and patient care.