Quan Nguyen ☕️

Quan Nguyen

Assistant Professor of Computing Science

Thompson Rivers University

Professional Summary

I am a tenure-track Assistant Professor in the Computer Science department at Thompson Rivers University. My research centers on the impact of generative AI on the learning behavior and outcome in computer science education. Before joining TRU, I was a Postdoctoral Fellow at the UBC Master of Data Science, where I developed and taught a variety of data science courses, including those on statistical inference, machine learning, and technical communication. In addition to teaching, I coordinated the capstone program, facilitating student collaborations with industry partners on real-world data science projects.

Prior to UBC, I worked as a Postdoctoral Fellow in Learning Analytics at the School of Information, University of Michigan. My research focuses on analyzing students social interactions and peer effects from spatio-temporal large scale data. My work has been recognized with competitive grants, and multiple best paper awards at prominent conferences, including LAK18 and HCI International 17.

I hold a PhD in Learning Analytics at The Open University UK, a BSc and MSc in Economics from Maastricht University, Netherlands.

Education

Postdoctoral Fellow

2021-08-01
2024-08-01

University of British Columbia

Postdoctoral Fellow

2019-09-01
2021-08-01

University of Michigan

Ph.D., Education Technology

2016-01-01
2020-01-01

The Open University, United Kingdom

M.Sc. Economics & Information Management

2015-01-01
2016-01-01

Maastricht University, Netherlands

B.Sc., Economics

2012-01-01
2015-01-01

Maastricht University, Netherlands

Interests

Learning Analytics Artificial Intelligence in Education Data Science Educational Technology
Featured Publications
Cross-institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity featured image

Cross-institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity

This paper explores cross-institutional transfer learning for educational models, focusing on its implications for model performance, fairness, and equity.

j.-gardner
Predictive Modeling of Student Success featured image

Predictive Modeling of Student Success

This chapter discusses predictive modeling techniques for student success, exploring their applications and implications in educational contexts.

c.-brooks
An example preprint / working paper featured image

An example preprint / working paper

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avatar
Quan Nguyen
BELAI Lab
BELAI Lab Logo

In rock climbing, a belayer doesn’t pull you up the wall; they secure the rope so you can attempt the hardest moves without fear. We apply this philosophy to education. Rejecting the model of AI as an “answer engine,” we build Socratic systems that act as active partners in productive struggle. Our tools empower students to take risks, own their failures, and build resilience. We don’t remove the struggle of learning; we make it safe to struggle.

Quan Nguyen, PhD featured image

Quan Nguyen, PhD

Principal Investigator. Research on AI in Education, Learning Analytics, and Educational Data Science.

Cristian de Toro

Reinforcement learning for pedagogically-aligned AI systems.

Kenji Lopez

AI-driven automated assignment generation for programming courses.

Mint Thai

Multi-modal knowledge synthesis for accessible educational content.

Rohit Thimaya

RAG systems for Moodle integration and AI-enabled group collaboration.

Recent News
  • Feb 2026 — 🎉 Thrilled to be stepping into the role of faculty lead for TRU’s Game Changer AI initiative — I’ll be working on bringing AI into education in meaningful ways. Check out tru.ca/ai for more details!
  • Jan 2026 — 🎤 Looking forward to joining a panel discussion on reimagining assessment in the age of AI at the Science Faculty Meeting — should be a great conversation!
  • Dec 2025 — 🤝 We hosted our second TRU AI Showcase — loved hearing how faculty across campus are weaving AI into their teaching!
  • Nov 2025 — 🤖 I’m leading a new project to automate programming assignment grading through Gradescope, funded by the Faculty of Science — hoping this saves our TAs some headaches!
  • Oct 2025 — ✨ Just submitted my NSERC Discovery Grant application… fingers crossed!
  • Jun 2025 — 🌟 My lab snagged a Mitacs Global Research Link internship for summer 2026 — excited to welcome a new researcher to the team!
  • Apr 2025 — 🧠 Our research group just released TRU-Think, a VS Code extension designed to foster productive struggle in learning. It’s a pedagogy-driven AI that encourages students to think rather than just copy answers!
  • Mar 2025 — 🚀 I organized the TRU AI Showcase: Exploring Responsible Use & Opportunities for AI in Teaching & Learning — great turnout and discussions!
  • Mar 2025 — 💡 I received a $5,000 grant from the TRU Instruction Innovation Fund to integrate AI tools into my computer science courses — can’t wait to see what we build!
  • Nov 2024 — 📝 I secured $6,000 from the TRU Undergraduate Research Experience Award Program to develop an adaptive quiz generation tool to help students prepare for exams!
  • Oct 2024 — 🔬 I was awarded $6,000 from the TRU Undergraduate Apprenticeship Fund to explore how generative AI is impacting computer science education!