This study aims to design, produce, and validate an information collection instrument to evaluate the opinions of teachers at non-university educational levels on the quality of training in artificial intelligence (AI) applied to education. The questionnaire was structured around five key dimensions: (a) knowledge and previous experience in AI, (b) perception of the benefits and applications of AI in education, (c) AI training, and (d) expectations of the courses and (e) impact on teaching practice. Validation was performed through expert judgment, which ensured the internal validity and reliability of the instrument. Statistical analyses, which included measures of central tendency, dispersion, and internal consistency, yielded a Cronbach's alpha of .953, indicating excellent reliability. The findings reveal a generally positive attitude towards AI in education, emphasizing its potential to personalize learning and improve academic outcomes. However, significant variability in teachers' training experiences underscores the need for more standardized training programs. The validated questionnaire emerges as a reliable tool for future research on teachers' perceptions of AI in educational contexts. From a practical perspective, the validated questionnaire provides a structured framework for assessing teacher training programs in AI, offering valuable insights for improving educational policies and program design. It enables a deeper exploration of educational AI, a field still in its early stages of research and implementation. This tool supports the development of targeted training initiatives, fostering more effective integration of AI into educational practices.

This PRISMA-based systematic review analyzes how artificial intelligence (AI) and Machine Learning (ML) are integrated into educational institutions, examining the challenges and opportunities associated with their adoption. Through a structured selection process, 27 relevant studies published between 2019 and 2023 were analyzed. The results indicate that AI adoption in education remains uneven, with significant barriers such as limited teacher training, technological accessibility gaps, and ethical concerns. However, findings also highlight promising applications, including AI-driven adaptive learning systems, intelligent tutoring, and automated assessment tools that enhance personalized education. The geographical analysis reveals that most research on AI in education originates from North America, Europe, and East Asia, while developing regions remain underrepresented. Without strategic integration, the uneven implementation of AI in education may widen social inequalities, limiting access to innovative learning opportunities for disadvantaged populations. Consequently, this study underscores the urgent need for policies and teacher training programs to ensure equitable AI adoption in education, fostering an inclusive and technologically prepared learning environment.

Artificial intelligence (AI) is influencing various sectors of society, including the educational field. The use of AI can have great potential in education, however, it is necessary to know both its performance and its limitations. The main objective of this study is to analyze the prompts made by teachers in initial training in relation to the topic of specific educational support needs, classifying them according to Bloom's Taxonomy. For this, 63 students from the first year of the Primary Education Degree in the subject Information and Communication Technology applied to Education participated. The results show that the highest frequency of prompts made by students correspond to the highest levels of Bloom's taxonomy (apply and create), which suggests that students are capable of using the knowledge acquired in the subject to create new learning situations with their future students. This confirms that the implementation of this methodology is beneficial for the development of cognitive and pedagogical skills of future teachers.

Introducción: Las fronteras entre la humanidad y la inteligencia artificial se están desdibujando, lo que plantea desafíos importantes en el ámbito educativo. Aunque las instituciones muestran interés en incorporar tecnologías como ChatGPT, persisten debates sobre su impacto en la formación de ciudadanos críticos y líderes del futuro. Metodología: Esta revisión sistemática, guiada por el método PRISMA, analiza estudios sobre el uso de herramientas de IA en contextos escolares, combinando enfoques cualitativos para valorar su relevancia pedagógica y cuantitativos para identificar tendencias de publicación. Resultados: Si bien muchas investigaciones se centran en disciplinas científicas donde la IA ya está integrada, los hallazgos revelan que el ámbito educativo aún enfrenta dificultades para adaptarse a estas tecnologías, lo que podría agravar las desigualdades existentes. Discusión: La incorporación de IA en la educación requiere estrategias que consideren no solo la dimensión tecnológica, sino también los factores sociales, éticos y pedagógicos que influyen en su implementación efectiva. Conclusiones: Se destaca la necesidad de avanzar en investigaciones con enfoques sociológicos y educativos que permitan diseñar políticas y prácticas eficaces para integrar la inteligencia artificial en el sistema escolar, formando estudiantes críticos, éticos y competentes en una sociedad cada vez más digitalizada.

This study analyses the professional development provision in artificial intelligence (AI) offered to non-university teachers in Andalusia during the 2023–2024 academic year. The research follows a descriptive documentary design based on the systematic review of training activities provided by the Teacher Training Centres (CEPs) in the eight Andalusian provinces. The initial corpus comprised 1832 training activities. After screening course titles and, when available, objectives, descriptions, metadata and content lists, 26 AI-related activities were identified; after excluding repeated editions and activities whose information was not accessible, the final analysable corpus consisted of 15 distinct and visible courses. The analysis combined quantitative description of distribution by province with qualitative content analysis supported by Atlas.ti. The results show that AI-related training represented only 1.42% of the overall offer and that the final analysable corpus represented 0.82% of all training activities. The provision was also unevenly distributed across provinces and tended to privilege specific tools, generative AI applications and productivity-oriented uses, with comparatively limited attention to ethical, pedagogical, assessment-related and interdisciplinary dimensions. The findings suggest the need for a more coherent regional strategy based on common minimum AI literacy modules, differentiated pathways by educational stage, blended and mentored formats, classroom-based projects and systematic evaluation of training impact.