AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective

This study analyzes AI-assisted help-seeking trajectories in programming education, focusing on 1,290 student prompts linked to 17,190 code submissions from 71 students in introductory Python courses. It reveals that students predominantly use AI for reactive troubleshooting rather than self-regulated problem-solving, with distinct trajectory patterns affecting the number of code submissions but not significantly impacting task scores. This research highlights the importance of understanding how students interact with AI tools to optimize educational outcomes in programming.