Sustainable development (SD) is an approach that aims to meet the needs of the present generation without compromising the ability of future generations to meet their own needs. Education for sustainable development (ESD) is a key component in achieving this goal, as it equips young people with the knowledge, skills, and values needed to make sustainable decisions. This study investigated how preschool teachers in Saudi Arabia understood (SD) and the state of (ESD) practices. A survey was used to collect data from 230 Saudi preschool teachers. The findings revealed that 90% of teachers lacked awareness regarding SD. The overall evaluation of ESD practices among participants indicated a weak subpar status, with an average score of 2.49 out of 4. Notably, in ascending order, the following three dimensions had weak mean scores: the content aspect (2.38) had the lowest score, followed by the practice aspect (2.54) and the competencies aspect (2.58). Meanwhile, the values aspect (2.63) had an average outcome. Analysing the mean scores of ESD practices based on teachers’ qualifications and school types revealed significant differences, although no variations were observed based on experience. The primary obstacle to ESD implementation in pre-schools was the lack of awareness regarding SD/ESD. The study underscores the significance of expanding teacher training to promote ESD effectively in pre-school settings. The results highlight the need for professional development opportunities to improve ESD implementation in classrooms, educate Saudi preschool teachers about SD, and create instructional materials that align with the principles of ESD.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.