When Do Students Seek AI Help? Exploring Within-Person and Between-Person Differences

Abstract

Most research on student AI use asks who adopts these tools, treating AI use as a stable personal characteristic. Yet each time students encounter a problem, they face a fresh decision about whether to seek AI help. We examined when students choose AI assistance by analyzing both stable individual differences and moment-to-moment decisions. Undergraduate students (N = 398) completed 30 quiz trials, freely choosing on each trial to answer independently, consult an AI chatbot, or rely entirely on AI. Before each decision, students rated their topic expertise. We also measured cognitive abilities, AI attitudes, and demographics. Multilevel modeling revealed that almost half the variance in AI use reflected stable individual differences; the other half varied within individuals across trials. Self-assessed expertise accounted for this within-person variation: students sought AI help primarily when they felt unknowledgeable about a topic. At the between-person level, fluid intelligence predicted more chatbot consultation but less full delegation, suggesting higher-ability students use AI strategically rather than as a substitute for thinking. Male students and those with favorable AI attitudes showed greater willingness to delegate entirely. These findings reframe AI use as adaptive help-seeking rather than a fixed trait. Educational interventions that target metacognitive calibration, helping students accurately judge when they need assistance, may prove more effective than blanket AI policies that assume uniform use patterns.

Ähnliche Arbeiten