Less Deliberate in Teams: Student LLM Use Across Individual and Collaborative Work
This paper identifies a critical, unsupported turning point in computing course design where collaborative context reduces deliberate LLM engagement beyond task type alone.
In a semester-long study of 96 undergraduates, LLM usage dropped by 42.7 percentage points when moving from individual to team work, with students writing fewer prompts, using less deliberate strategies, and checking output less carefully. The share of students testing AI-generated code fell by 19.4 percentage points during team assignments.
As large language models (LLMs) become common in computing courses, we need to understand how the social setting shapes how students use them. This paper reports findings from a semester-long study of 96 undergraduate students who completed six assignments, alternating between individual homework and team project milestones. We tracked LLM usage, prompting habits, and how students verified AI-generated output across all six assignments. LLM usage dropped by 42.7 percentage points when students moved from individual work to their first team milestone, then partly recovered in later team tasks. Students also wrote fewer and simpler prompts, used fewer intentional prompting strategies, and checked LLM output less carefully. The share of students who ran tests on AI-generated code fell by 19.4 percentage points during team assignments and never fully rebounded. A within-student analysis found that 18.9% of students who consistently used LLMs on their own stopped using them entirely in teams, while only 3.2% went the other direction. These results suggest that collaborative context is associated with reduced deliberate LLM engagement beyond what task type alone can explain. The moment students form teams appears to be a critical and currently unsupported turning point in computing course design.