A Survey on AI and Self-Regulated Learning in Computer Science Education

PhD Qualifying Examination


Title: "A Survey on AI and Self-Regulated Learning in Computer Science 
Education"

by

Mr. Shixu ZHOU


Abstract:

To leverage the opportunities and benefits of AI in computer science (CS) 
education while mitigating the potential risks of student reliance, recent 
work has begun to explore how AI can support self-regulated learning (SRL). 
SRL focuses on students' ability to plan, monitor, and reflect on their 
learning processes, and AI offers new ways to scaffold programming and 
problem-solving activities. In this survey, we review and organize the 
literature on AI-supported SRL in CS education into three categories: task 
restructuring, which changes learning activities and intermediate artifacts to 
externalize SRL processes; dialogue guidance, which uses conversational 
interaction to scaffold metacognitive regulation and help-seeking; and 
workflow augmentation, which preserves existing learning workflows while 
providing auxiliary support. We synthesize findings across these categories 
and highlight future opportunities, including adaptive structuring of SRL 
supports through student profiling, designing engagement support through 
progress and task-grounded feedback, and clarifying learning goals and 
evidence standards for AI-supported SRL in CS. Overall, our survey provides an 
organizing taxonomy of prior work and research opportunities for future AI 
systems that aim to support SRL in computer science education.


Date:                   Monday, 30 March 2026

Time:                   9:00am - 11:00am

Venue:                  Room 2132C
                        Lift 22

Committee Members:      Dr. Xiaojuan Ma (Supervisor)
                        Prof. Raymond Wong (Chairperson)
                        Dr. Qijia Shao