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

2026-06-21

AI Summary

This study investigates how generative AI influences help-seeking behaviors among novice programmers through the lens of self-regulated learning (SRL), moving beyond conventional metrics such as usage frequency or answer accuracy. Grounded in SRL theory, the authors developed a coding framework to conduct trajectory analyses of 1,290 AI prompts and 17,190 code submissions from 71 students enrolled in a Python course. This approach offers the first dynamic perspective on how learners’ help-seeking evolves across four support categories: conceptual understanding, implementation, debugging, and reflection. Findings reveal that students predominantly employed AI for reactive debugging rather than proactive planning. Although distinct help-seeking trajectories did not significantly affect task scores, they substantially influenced the number of code submissions required, underscoring that the manner in which AI is used holds greater educational value than mere usage itself.

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.

Problem

• help-seeking
• generative AI
• self-regulated learning
• programming education
• AI-assisted learning

Innovation

• help-seeking trajectories
• self-regulated learning (SRL)
• generative AI in education
• programming education
• prompt analysis