Opting Out of Generative AI: a Behavioral Experiment on the Role of Education in Perplexity AI Avoidance

Abstract

The rise of conversational AI (CAI), powered by large language models, is transforming how individuals access and interact with digital information. However, these tools may inadvertently amplify existing digital inequalities. This study investigates whether differences in formal education are associated with CAI avoidance, leveraging behavioral data from an online experiment (N = 1,636). Participants were randomly assigned to a control or an information-seeking task, either a traditional online search or a CAI (Perplexity AI). Task avoidance (operationalized as survey abandonment or providing unrelated responses during task assignment) was significantly higher in the CAI group (51%) compared to the search (30.9%) and control (16.8%) groups, with the highest CAI avoidance among participants with lower education levels (~74.4%). Structural equation modeling based on the theoretical framework UTAUT2 and LASSO regressions reveal that education is strongly associated with CAI avoidance, even after accounting for various cognitive and affective predictors of technology adoption. These findings underscore education's central role in shaping AI adoption and the role of self-selection biases in AI-related research, stressing the need for inclusive design to ensure equitable access to emerging technologies.

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