AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students' AI self-efficacy

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, we identify three meaningful student groups, which can guide educational initiatives. We recruited 1,465 undergraduate and graduate students from the United States, the United Kingdom, and Germany and measured their AI self-efficacy, AI literacy, interest in AI, attitudes towards AI, and AI use. Using a path model, we examine the correlations and paths among these variables. Results reveal that AI usage and positive attitudes significantly predict interest in AI, which in turn and together with AI literacy, enhances AI self-efficacy. Moreover, using Gaussian Mixture Models, we identify three stable and distinct groups of students: 'AI Advocates,' 'Cautious Critics,' and 'Pragmatic Observers,' each exhibiting unique patterns of AI-related cognitive, affective, and behavioral traits. Our findings demonstrate the necessity of educational strategies that not only focus on AI literacy but also aim to foster students' attitudes, usage, and interest to effectively promote AI self-efficacy. Furthermore, we argue that educators who aim to design inclusive AI educational programs should take into account the distinct needs of different student groups identified here. • Validates a path model showing that AI use and positive AI attitudes significantly predict AI interest, which, along with AI literacy, enhances AI self-efficacy • Empirically uncovers and describes three groups of students and their socio-demographic characteristics: AI Advocates, Cautious Critics, and Pragmatic Observers • Recommends strategies to promote student AI self-efficacy in light of cognitive, affective, and behavioral factors