The advancement of Artificial Intelligence (AI) in the educational field has highlighted the importance of analyzing how students perceive their ability to use AI tools effectively. In this regard, self-efficacy related to AI use emerges as a key factor in addressing the challenges of an increasingly digitalized academic environment. However, despite the growing importance of AI use, few studies have been conducted to validate reliable measures for assessing AI-related self-efficacy. One of the most relevant instruments developed for this purpose is the AI Self-Efficacy Scale (AISES), designed to assess students' perceptions of their own competencies in managing AI within higher education. This assessment instrument consists of 22 items with a 7-point Likert-type response scale (1 = Strongly disagree, 7 = Strongly agree), structured into four dimensions: Assistance, Anthropomorphic Interaction, Comfort with AI, and Technological Skills. In its original version, the instrument has demonstrated adequate psychometric properties, with reliability coefficients ranging from .86 to .97. Therefore, the main objective of the present study was to carry out a cross-cultural validation of the AISES and a preliminary exploratory factor analysis of the Spanish version. The linguistic validation of the instrument was conducted following the standardized translation–back-translation procedure. Once the final Spanish version of the scale was obtained, it was administered to 217 university students (81.6% female and 18.4% male), with a mean age of 22.54 ± 7.78 years. The sampling adequacy criteria (KMO = .902) and Bartlett’s test of sphericity (χ² = 3058.692, df = 231, p < .001) showed excellent indices, allowing for the application of factor analysis. The results of the exploratory factor analysis with Promax rotation confirmed the four-factor structure of the original AISES in our Spanish sample, explaining 67.6% of the variance. The results obtained indicate that the AISES may be an appropriate instrument for analyzing AI-related self-efficacy among university students in the Spanish context. However, future studies should confirm the construct validity of the scale through confirmatory factor analysis. The availability of tools with adequate psychometric properties for assessing AI-related self-efficacy among Spanish university students could represent a significant step forward in understanding the level of confidence they have in their ability to learn, apply, and operate with AI technologies. This, in turn, could enable the design of more effective training interventions and promote the responsible and ethical use of AI in educational settings.

Keywords: Spanish Version of AISES, Cross-Cultural Validation of AISES, Artificial Intelligence, AI Self-Efficacy, Higher Education Students.