Yuzhe Zhou

Lead Designer & Developer · PI: Dr. Tianyi Li

An AI-assisted learnersourcing system engaging students in co-creating contextualized, relatable learning materials for programming education. Built with Django and PostgreSQL, with AI features that support students through ideation, generation, and evaluation — refined through one-on-one user studies and prepared for classroom deployment.

Researcher · PIs: Dr. Tianyi Li, Dr. Alejandra J. Magana

A learnersourcing workflow engaging students across multiple semesters in co-creating contextualized worked-out examples, improving learning transfer in database programming. Iteratively refined through repeated classroom deployments, integrating tools such as MTurk, Qualtrics, and Circuits to strengthen adoption and scale.

Co-Developer · PIs: Dr. Tianyi Li, Dr. Jian Zhao (University of Waterloo)

A Purdue × Waterloo collaboration providing personalized, standardized, and scalable feedback for coding assignments in large courses. Features include a smart comment bank and error-detection capabilities — type-mismatch checks and AI-generated-code detection — keeping instructors in control of AI-assisted grading while improving consistency, timeliness, and fairness.

Active research project · Purdue University × University of Waterloo.

Researcher · PIs: Dr. Tianyi Li, Dr. Paul J. Thomas

A mixed-methods quasi-experimental study in an introductory database course, investigating the gap between instructor-defined and student-perceived context familiarity and its impact on SQL performance and confidence. Both familiar and unfamiliar contexts improved learning — but familiar contexts significantly boosted student confidence, highlighting the role of perceived familiarity in motivation and engagement.

Undergraduate Researcher · with Dr. Alejandra J. Magana

Compared LDA topic modeling with human coding of qualitative data: focused literature review, qualitative and quantitative analysis with systematic inter-rater reliability, and a detailed technical report that became the foundation for a peer-reviewed journal article.