Bridging the AI Skills Gap for Sustainable Education: A Structural Model of In-Service Teachers’ Learning Intentions and Behaviors

As artificial intelligence (AI) drives significant challenges in education, understanding and addressing the training needs of in-service teachers has become a critical issue for ensuring a responsible and long-term technological transition. Framed within Sustainable Development Goal 4 (SDG 4) and the principles of Education for Sustainable Development (ESD), teacher preparation in AI is increasingly recognized as a key mechanism for promoting ethical, equitable, and inclusive educational transformation. This study explores the influence of several key variables on intention and learning behaviors in relation to AI among a sample of 704 Spanish in-service teachers (71% women) from all compulsory educational levels. Using a validated questionnaire, this study assessed teachers’ anxiety towards AI, basic AI knowledge, personal relevance of AI, AI for social good, perceived self-efficacy, social pressure, and perceived usefulness of AI. Structural equation modeling (SEM) was employed to analyze the direct and indirect relationships among these variables. The results indicate that the perceived usefulness of AI and self-efficacy directly and positively influence the behavioral intention to learn about AI. Furthermore, social pressure and basic AI knowledge indirectly influence this intention. In turn, both behavioral intention and social pressure significantly predicted AI learning behaviors. The model demonstrates strong explanatory power, accounting for 91% of the variance in the behavioral intention to learn about AI. These findings provide evidence to inform the design of teacher training initiatives and policies that promote responsible, ethical, and inclusive integration of AI in educational settings, contributing to sustainable development through education.