AI-Assisted Computing Education
Developing AI-assisted personalized learning in project-based programming education for K-12 students, and exploring social science students motivation and coding performance in higher education.
Sub-Project 1: AI-Assisted Personalized Learning in Project-Based Programming Education
This sub-project investigates how AI-driven personalized scaffolding can support K-12 students in project-based programming courses. As students work through open-ended coding projects, an AI system provides adaptive hints, real-time error diagnosis, and tailored feedback based on individual learner profiles. The research examines whether personalized AI assistance improves students' computational thinking skills, coding self-efficacy, and sustained engagement compared to traditional instructional approaches.
Sub-Project 2: Vibe Coding Research
This sub-project explores how social science students in higher education experience learning to code with emerging AI-assisted "vibe coding" tools. Through a quasi-experimental design, the study compares students' motivation, self-regulation, and coding performance when using AI-assisted coding environments versus conventional programming tools. The research pays particular attention to how students with non-technical backgrounds develop programming identities and overcome barriers to computational literacy.