Help-Seeking in LLM-Assisted Learning: Behavioral Pathways and Their Limited Association with Subsequent Coding Process Efficiency

Large language models (LLMs) are increasingly used in programming education to provide on-demand conceptual clarification, yet how students actually use this feature in mastery learning systems (in which learners must demonstrate conceptual competence before progressing)—and whether clarification interactions relate to subsequent learning—has received limited empirical study. This paper analyzes 732 student remediation episodes (366 students, 43 assignments) to examine how students move through the remediation branch of an LLM-assisted programming course, whether their behavioral pathway choices are associated with subsequent coding challenge efficiency, and what theoretical role the clarification function plays. The results show that 78.0% of remediation episodes follow a pure retesting strategy, with only 22.0% involving any clarification interaction. Clarification is highly concentrated on conceptual questions (84.7%) and occurs mostly in the first remediation round (86.3%). An effect size analysis reveals a large difference in remediation rounds between single immediate and single delayed clarifiers (Cliff’s δ=−0.912), suggesting that the timing of clarification is more strongly associated with remediation efficiency than its occurrence alone. mixed-effect linear models show no significant pathway effects on coding challenge process efficiency (active time and number of code snapshots; all p>0.05), a null result that is further examined through code-variability subgroup analyses. We argue that the clarification feature acts as a selective process-support mechanism: its observable value appears to lie in a shorter remediation process rather than in improved subsequent task efficiency, and this association is clearest when clarification occurs early. The findings have practical implications for the design of clarification features in AI-assisted learning systems and for instructional intervention strategies.