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

2025-07-10

AI Summary

This study investigates whether educational disparities drive conversational AI (CAI) avoidance behavior and exacerbate digital inequality. Method: A randomized online experiment was conducted, comparing Perplexity AI, conventional search engines, and a control task; behavioral and psychocognitive data were collected and analyzed via structural equation modeling grounded in the UTAUT2 framework, supplemented by LASSO regression. Contribution/Results: The study provides the first empirical evidence of a significant negative association between education level and CAI avoidance: 74.4% of low-education participants avoided CAI—substantially higher than the overall average (51%) and control group (16.8%). Critically, it identifies “self-selection bias” as a key confounding factor in AI adoption research and demonstrates that the education gap is internalized into technological inequity through active avoidance mechanisms. These findings underscore the necessity of integrating educational adaptability into the core design principles of inclusive generative AI systems.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Investigates education's link to conversational AI avoidance
• Examines digital inequality in AI tool adoption rates
• Analyzes behavioral differences in AI versus traditional search usage

Innovation

• Behavioral experiment on education and AI avoidance
• Structural equation modeling with UTAUT2 framework
• LASSO regressions to analyze cognitive predictors