Understanding how students interact with AI tutors in introductory programming courses to improve learning outcomes.
Lead PI:
Team Members:
Research Period:
Prof. Ben Leong Wing Lup
Dr. Liu Liu, Markus Yeo, Lucius Khor
Aug 2025 – Ongoing
About This Project
This project is an educational data mining research project that investigates how students seek help from AI-powered tutoring systems in NUS’s introductory programming courses. Drawing on rich interaction logs from Coursemology, an AICET-built learning platform, the project examines the frequency, timing, and sequencing of help-seeking behaviours, tracing how students navigate confusion, formulate questions, and respond to AI-generated guidance across the arc of a programming task. A key methodological contribution is the development of a multi-dimensional analytical framework incorporating AST (Abstract Syntax Tree) distance and other behavioural metrics as proxies for learning progress: by measuring the structural gap between a student’s current code and a correct solution before and after each AI interaction, we can assess whether help-seeking is moving students toward genuine understanding or simply toward a working submission. In doing so, this project aims to distinguish between AI that scaffolds real learning and AI that merely offloads the cognitive work that learning requires.
GetHelp system overview
