Session Description:
PathwAI is an AI-powered curriculum comparison tool that uses a large language model to assess semantic similarity between course learning outcomes, comparing meaning rather than keywords. By extracting and standardizing learning outcomes from course materials, the model generates percentage-based similarity scores and outcome-level relatedness recommendations for course and program comparisons, supporting transfer credit and pathway development workflows. These AI-generated results and reports help teams quickly identify areas of alignment or gaps, while leaving final interpretation and decision-making with faculty and expert reviewers. PathwAI was developed by Epsilon Data Intelligence with funding support from ONCAT. ONCAT makes PathwAI available free of charge to staff at all publicly-assisted postsecondary institutions in Ontario.

Speakers’ biographies:

Dr. Vijay Mago is Chair of the School of Health Policy & Management at York University and a recognized leader in artificial intelligence for health systems. With over a decade of academic experience, he has established an internationally impactful research program in health informatics, natural language processing, and data analytics, securing more than $7 million in research funding as Principal Investigator and collaborator from major agencies including NSERC, SSHRC, Connected Minds, MITACS, ONCAT, and industry partners. He is the founder and director of the Data and Text Analytics Lab (DaTALab), where he leads the development of advanced computational methods to extract actionable insights from complex health data, supporting real-time surveillance, clinical decision-making, and policy innovation. His work spans critical areas such as mental health, homelessness, and chronic disease, and is grounded in strong interdisciplinary collaborations with academic, healthcare, government, and industry stakeholders.

Dr. Andrew Fisher is an Assistant Professor of AI in the Faculty of Computer Science at the University of New Brunswick. He earned a PhD in Applied Science from Saint Mary’s University in 2024 and completed a postdoctoral fellowship with Connected Minds at York University in 2025, building experience across academic and industry settings. His research focuses on practical, responsible AI that can be evaluated thoroughly and deployed in real-world contexts. He is particularly interested in developing lightweight machine learning and natural language processing methods that improve decision-making, efficiency, accessibility, and trust across domains such as health communication and the built environment.

Kaylin Kainulainen, MSc Kin, is a PhD Candidate in the Faculty of Education at Lakehead University. Her research in the field of Cognition and Learning focuses on academic transfer and student mobility within postsecondary education, particularly with regard to addressing  common barriers as well as creating strategies for improving the transfer process. Kaylin also works as a project manager and coordinator, guiding multidisciplinary teams through projects ranging from novel academic research to innovative technology development.