Rapid integration of artificial intelligence (AI) into work and educational settings challenges organizations to gauge and respond to adoption rates. However, most measures of AI adoption come from self-reported surveys, producing estimates of AI use that disagree by up to 40 percentage points within the same setting. We investigate whether social desirability bias—the tendency to answer surveys in ways that would be viewed favorably by an outside party—can explain this discrepancy. Surveying 338 university students, we assess potential social desirability bias using a method from psychology, indirect questioning: students report both their own AI use and that of their peers. We find a significant gap, with approximately 60% of students reporting that they use AI compared to 90% of their peers. Through qualitative analysis of student explanations for this gap, we conclude that social desirability bias is a key driver of mis-measurement, causing underestimates of AI adoption in educational settings.
ACM CHI Conference on Human Factors in Computing Systems