Personal and Academic Factors in Teachers’ Perceptions of Educational Artificial Intelligence

(1) Background: The aim of this study was to analyze how teacher trainees and teachers relate to Artificial Intelligence (AI) in education across socio-emotional, attitudinal, competency-based, and ethical dimensions, considering the influence of personal, academic, and professional variables. The study was conceptually framed by the UNESCO AI Competency Framework for Teachers (AI CFT), complemented by DigCompEdu as a broader reference for teacher digital competence. (2) Methods: A quantitative, non-experimental, descriptive, cross-sectional design was developed with a sample of 1292 participants from Andalusia, consisting mainly of university students, along with teachers. The ACISE-IA scale, consisting of 39 items developed by Serrano-García et al. (2026), was used for data collection. (3) Results: The results showed that age and teaching experience are negatively associated with favorable attitudes and digital competence, while academic performance is positively related to greater openness, critical reflection, and perception of competence. Likewise, significant differences were identified according to the branch of knowledge, employment status, and academic year, with more positive assessments among students and in social science areas. (4) Conclusions: Overall, the findings show that the relationship with AI is not homogeneous, but rather modulated by educational and professional trajectories, underscoring the need to design flexible teacher training models that integrate technological development with pedagogical, emotional, and ethical reflection. These findings also contribute to educational sustainability by highlighting the importance of developing teacher training strategies that support the responsible and sustained integration of AI into educational systems.