User AI Self-Efficacy and Attitudes Toward AI Predict Positive Perceptions of AI-Generated Text
TL;DR
It is suggested that perceived interaction competence and general evaluative orientation independently contribute to user experience with socially capable AI agents, which have practical implications for the design of AI systems.
Abstract
As socially capable artificial intelligence (AI) rapidly advances in sophistication, prevalence, and ubiquity, it becomes increasingly important to understand how human users perceive AI-generated messages. The present study examined whether users’ AI self-efficacy and attitudes toward AI predict their evaluations of AI-generated text. One hundred ninety-one participants rated 32 GPT-4-generated passages on likeability and perceived informational value, and completed measures of AI self-efficacy and attitudes toward AI. Multiple regression models were used to analyze the self-report data. Results indicated that both trait-level constructs contributed unique variance to each outcome, with AI self-efficacy more closely associated with perceived value and attitudes more closely associated with likeability. These findings suggest that perceived interaction competence and general evaluative orientation independently contribute to user experience with socially capable AI agents, which have practical implications for the design of AI systems.