AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective
2026-06-22 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied how university students learning Python use AI tools to get help while programming. They looked at detailed patterns of student questions and code submissions to see if students used AI as a planned problem-solving aid or just to fix errors reactively. The results showed most students mainly used AI for quick troubleshooting rather than thoughtful, self-regulated help. Although the way students used AI didn't change their final scores much, it did affect how many attempts they needed to finish tasks. This means understanding how students interact with AI is important, not just if they use it or not.
Generative AISelf-Regulated Learning (SRL)Help-Seeking BehaviorProgramming EducationPythonDebuggingCode SubmissionProblem-Solving TrajectoriesAI-Assisted Learning
Authors
Boxuan Ma, Huiyong Li, Gen Li, Li Chen, Atsushi Shimada, Shin'ichi Konomi
Abstract
Generative AI tools provide novice programmers with instant, personalized support, but also raise concerns about whether AI use supports or bypasses students' regulation of problem-solving. Existing work has largely focused on correctness, usability, or overall usage frequency, with less attention to how student--AI help-seeking unfolds. This study addresses this gap by analyzing AI-assisted help-seeking trajectories in university-level programming. Using an SRL-informed analytical framework that links prompt-level help-seeking codes to conceptual, implementation, debugging, and reflective forms of support, we analyzed 1,290 task-specific student prompts linked to 17,190 code submissions from 71 students in introductory Python programming courses. Specifically, we examined how help-seeking interactions were structured across turns and attempts, and how trajectory patterns related to task scores and the number of code submissions. Results indicate that many students primarily used AI for reactive troubleshooting rather than for planned, self-regulated problem-solving. Although trajectory patterns were not associated with significant differences in task scores, they differed substantially in the number of code submissions required. These findings suggest that the educational significance of AI support lies not only in whether students use AI, but in how their help-seeking trajectories develop during programming problem-solving.