Project Goals/Deliverables
A production-ready digital-twin platform on University-managed infrastructure, integrated with existing systems and configurable for multiple courses and instructors.
Improved student experience and learning, with more timely and personalized support.
Instructor benefits, including reduced repetitive work and more time on creative work.
Scalable implementation playbooks (technical templates, governance models, instructor/student guides) that can be adopted by all faculties.
Project Team
Victor Cui, Professor (Conrad School of Entrepreneurship and Business, Engineering)
Lucas John Allan, Undergraduate Research Coordinator
Adya Bhardwaj, Undergraduate Research Coordinator
Connect with the Digital-Twin Teaching Platform team!
Inquiries about this project can be directed to tii@uwaterloo.ca
Project Summary
Contemporary higher education still relies on a one-instructor-to-many-students model that struggles to serve large, diverse cohorts. The Digital-Twin Teaching Platform proposes to elevate Waterloo’s institutional capability to address this weakness, built on an innovative digital-twin system: a faculty twin that provides real-time, course-specific explanations grounded in Waterloo-owned materials, and a student twin that models each student’s learning capabilities and preferences. Together, they support real-time personalized learning in-class and continuous guidance, enabling mass-customized teaching. Instead of treating AI as an add-on, the project tests a new institutional capability. The underlying infrastructure is multi-course and multi-instructor by design.
Project Updates
Project updates to be shared here!
Proposed Project Impacts
For students, the goal is more timely, judgment-free access to help, improved metacognitive skills, and better support for diverse backgrounds and schedules. For instructors, the goal is measurable reduction in repetitive routinized work and more time for higher-order teaching. By accomplishing these goals, the project advances Waterloo’s Technological, Economic, and Societal Futures: it explores human-centred generative AI for teaching (Technological), prototypes more sustainable models of high-quality instruction under financial constraint (Economic), and aims to reduce hidden inequities in help-seeking by giving every student low-friction, judgment-free access to support (Societal).
Project Resources
There are currently no project resources. Once they become available, they will be posted here.