AI Collaborative in the Faculty of Medicine

I’m excited to share that I am launching a new AI Collaborative for the Faculty of Medicine.

The goal of this collaborative is simple but meaningful: to create a space where we can share ideas, projects, and innovations related to the use of AI in medical education. As AI continues to rapidly evolve, this collaborative will help us stay informed, learn from one another, and explore opportunities to enhance teaching, learning, assessment, and curriculum design across our programs.

Whether you’re already working on an AI-related initiative, curious about how AI might support your educational activities, or simply interested in staying connected to emerging practices, you are welcome to join.  If you would like to participate in this collaborative or if you have someone in your unit/division/discipline that you would prefer to take part in the collaborative from your area, please let me know. Contact me at Steve Pennell, spennell@mun.ca. 

Explore Memorial AI resources for medicine

Use this site to:

• identify secure AI tools for university work
• explore teaching and learning guidance
• support learner AI literacy
• strengthen assessment and feedback practices
• build AI and digital skills
• connect educational practice with broader research and innovation supports

Why this matters for the Faculty of Medicine

The Faculty of Medicine’s work is grounded in educational quality, social accountability, professional identity formation, and the preparation of physicians to serve patients and communities. Any use of AI in this environment should reinforce those goals.

A Faculty of Medicine approach to AI should therefore emphasize:

• thoughtful and evidence-informed adoption
• clear expectations for learners and faculty
• protection of institutional and learner data
• alignment with teaching, learning, and assessment goals
• responsible and transparent educational practice

AI in Medical Education 

Supporting teaching, learning, and assessment in Memorial University’s Faculty of Medicine

Artificial intelligence is increasingly shaping how medical education is designed, delivered, and evaluated. Across undergraduate, postgraduate, research and graduate studies, these tools create new opportunities to support teaching, strengthen learner engagement, enhance feedback, and inform assessment design. They also raise important questions about academic integrity, transparency, privacy, and responsible use.

This site is intended as a practical starting point for faculty, staff, and learners who want to explore the role of AI across the medical education continuum. It brings together Memorial-based resources that can support teaching in the MD program, residency education, graduate studies, faculty development, scholarly work, and evolving approaches to assessment and feedback.

AI in teaching, learning, and assessment

In medical education, AI can support a wide range of activities. In teaching, it can help with planning, content development, case creation, and communication. In learning, it can help students organize information, generate study supports, and build AI literacy. In assessment, it creates opportunities to rethink feedback, authenticity, transparency, and acceptable use.

For the Faculty of Medicine, the goal is not simply to adopt AI tools, but to use them thoughtfully in ways that support educational quality, learner development, professionalism, and responsible practice.

For undergraduate medical education

In undergraduate medical education, AI can be used to support course design, small-group learning, formative assessment, case-based teaching, and student learning supports. At the same time, learners and faculty need clear expectations around acceptable use, disclosure, and the boundaries between support and substitution.

These conversations are especially important in medicine, where strong foundations in clinical reasoning, communication, ethics, and professional identity remain central to the educational experience.

For postgraduate medical education

In postgraduate training, AI may support teaching preparation, feedback processes, academic half-days, workplace-based assessment review, and program-level educational improvement. Residency programs may also wish to explore how AI can assist with data-informed review of assessment patterns, feedback quality, and educational processes, while maintaining appropriate oversight and alignment with accreditation and professional standards.

Research and Graduate Studies

AI is also increasingly relevant to medical education scholarship, health research, and graduate studies—supporting activities such as literature synthesis, data analysis, qualitative and mixed-methods workflows, and the development of research tools and prototypes. For teams and trainees pursuing AI-enabled educational research or analytics, Memorial’s Centre for Analytics, Informatics and Research (CAIR) can provide research computing infrastructure, secure storage, and technical support.

For faculty development

Faculty members need practical support in understanding what AI tools can do, when they can be used appropriately, and how they relate to course design, assessment, learner guidance, and academic integrity. This includes not only technical familiarity, but also educational judgment.

This site can help faculty members identify institutionally supported tools, find teaching guidance, build confidence in responsible use, and connect with broader Memorial resources.

Memorial resources

Secure institutional AI tools

For university work, Memorial provides access to Microsoft Copilot Chat. This is an important starting point for faculty and staff seeking an institutionally supported AI tool for drafting, summarizing, editing, analysis, and other productivity tasks.

Teaching and learning guidance

Memorial’s Generative AI resources from the Centre for Innovation in Teaching and Learning provide guidance relevant to educators and learners, including teaching considerations, student guidance, and broader issues related to academic practice and responsible use.

Library support and AI literacy

The Memorial University Libraries AI Guide offers additional support for informed and critical engagement with AI tools. This resource can help learners and educators think carefully about appropriate use, source evaluation, and responsible academic practice.

Research, analytics, and innovation

The Centre for Analytics, Informatics and Research (CAIR) provides research computing infrastructure and support that may be relevant for educational innovation, analytics, and AI-related scholarship in medicine.

Skills development

Through CAIR, Memorial community members can also explore IBM SkillsBuild, which offers opportunities for AI and digital skills development through self-directed learning and credentialing pathways.

Quick links

• Microsoft Copilot Chat
• CITL Generative AI
• Memorial University Libraries AI Guide
• Centre for Analytics, Informatics and Research (CAIR)
• IBM SkillsBuild