AI Advocates and Cautious Critics: How AI Attitudes, AI Interest, Use of AI, and AI Literacy Build University Students' AI Self-Efficacy

Abstract

This study investigates how cognitive, affective, and behavioral variables related to artificial intelligence (AI) build AI self-efficacy among university students. Based on these variables, three meaningful student groups are identified to guide educational initiatives. The study recruits 1,465 undergraduate and graduate students from the United States, the United Kingdom, and Germany to measure AI self-efficacy, AI literacy, interest in AI, attitudes towards AI, and AI use. Results demonstrate that AI usage and positive AI attitudes significantly predict interest in AI, which, in turn, alongside AI literacy, enhance AI self-efficacy. Educational strategies focusing on these dimensions are proposed to promote effective AI self-efficacy development.