Changing the Culture 2026

Conference Program

8:00 Registration

8:45 Opening Remarks

9:00 Plenary Talk I: 

The Modern Traditionalist: Bridging the Post-Secondary Mathematics Gap in the Digital Age

Douglas Tam, St Georges School

Mathematics education, particularly in the secondary school setting is often caught in a philosophical divide. On one side is the modern movement towards Standards-Based Grading, Building Thinking Classrooms, and student well-being. On the other hand lies the traditional, uncompromising reality of university STEM programs, which demand objective precision, high-stakes performance, and deep cognitive stamina. Students caught in the middle often experience a “Post-Secondary Cliff”, a brutal transition from protected settings to stressful exams.

My journey in the last few years has been to not abandon either camp, but to combine the most prevalent parts of both the modern and traditional philosophies. The teaching and learning culture of my school is shifting, and students are becoming more aware of their own mathematics profile.

In this plenary talk, we will identify some of the cliffs naturally produced by the BC Ministry and curriculums like the IB. I will share how I’ve shifted our school’s assessment culture to be both progressive and traditional in hopes of better preparing students for post-secondary STEM, and how technology and AI have made this journey possible.

10:00 Coffee Break, (1400 Segal Centre)

10:30 Workshops

Workshop A: LaTeX + AI - The Perfect Duo for High Quality STEM Documentation

Douglas Tam, St. George's School

Tired of MS Word and Google Docs destroying your formatting every time you insert an equation or adjust a margin? This hands-on workshop introduces a frictionless workflow that pairs the typesetting power of LaTeX with the generative speed of AI. We will walk through a step-by-step process to instantly build flawlessly formatted, professional-grade math assessments, in a fraction of the time.

Participants will receive access to my personal LaTeX templates and AI prompt structures. Please bring a laptop and an existing quiz you would like to instantly upgrade during our live build session.

Workshop B: Why Machines Learn; using the mathematics of AI to motivate learning mathematics

Brenda Davison, SFU

Vijaykumar Singh, SFU

Students today are surrounded by artificial intelligence, yet the mathematics that powers it — vectors, dot products, derivatives, and the chain rule — is precisely the mathematics they meet in a first-year calculus or linear algebra classroom. This workshop equips participants with a coherent, classroom-ready narrative that connects what they already teach to the technologies their students use every day.

Following the arc of Anil Ananthaswamy's Why Machines Learn (2024), the session traces a single mathematical thread from Frank Rosenblatt's 1958 perceptron through backpropagation, AlexNet, the Transformer architecture, and today's large language models. Participants will work two hands-on examples that mirror what AI does at scale: computing cosine similarity between word vectors (the operation behind every search engine and embedding layer) and performing gradient descent by hand on a simple parabola (the optimization that trains every modern neural network). A concluding module unpacks how a large language model actually works, showing that attention reduces to dot products and softmax — operations any motivated first-year student can understand.

Participants leave with a set of examples, a glossary of AI vocabulary, and a discussion of AI-motivated examples they can use. The central claim of the workshop: the mathematics of AI is not an advanced specialty. It is the first-year curriculum, viewed through a 21st-century lens.

12:00 PIMS Award Ceremony

12:15 Lunch

13:00 Plenary Talk II.

The New Student-LLM Partnership: Feedback, Cognitive Offloading, and Exploration

Nahid Walji, UBC

As Large Language Models (LLMs) become a regular part of the undergraduate math experience, we need to understand more carefully how students are using them. In this talk, I will share what I have observed from students in my own classes as they integrate LLMs into their work. I will walk through three common patterns I have seen: using AI for self-correction and feedback, relying on it to shortcut the work through mindless copy-pasting, and using it creatively to explore new concepts. We will then discuss ongoing efforts, in a second-year Mathematical Proofs course at UBC, to steer students away from passive use and toward a more intentional and productive way of working with these tools.

14:00 Panel Discussion

• Brenda Davison, SFU
• Mayada Shahada, University of Calgary
• Douglas Tam, St. George's School)
• Nahid Walji, UBC
   

15:30 Concluding Remarks