It has become essential for current learners to gain basic literacy and competencies for artificial intelligence (AI). While educators and education authorities are beginning to design AI curricula, empirical studies on students’ perceptions of learning AI are still rare. This study examined a research model that synthesized the theory of planned behavior and the self-determination theory. The model explains students’ behavioral intention to learn AI. The model depicts the interrelationships among the factors of AI knowledge, programming efficacy, autonomy, AI for social good, and learning resources. The participants were 509 secondary school students who completed a series of AI lessons and a survey. The factor analyses revealed that our proposed instrument in the survey possesses construct validity and good reliability. Our further analysis supported that design of learning resources, autonomy, and AI for social good predicted behavioral intention to learn AI. However, unexpected findings were presented (i.e., AI knowledge failed to predict social good and programming efficacy negatively influenced autonomy). The findings serve as a reference for the future development of AI education in schools by noting that the design of the AI curriculum should take students’ needs and satisfaction into account to facilitate their continuous development of AI competencies.

In the process of future sustainable development, human society faces problems such as severe population load, economic transition, and a lack of educational measures. One of the root causes of these problems is the shortage of innovative talents. Therefore, how to cultivate learners with multidisciplinary integration and innovation ability is a key point that should be paid attention to when promoting the concept of quality education and coping with the future sustainable development process. This paper uses the questionnaire survey and literature analysis methods to analyze the development of many educational institutions with the Science, Technology, Engineering, Art, and Mathematics (STEAM) concept as the core in China since 2017. It is found that the existing STEAM educational institutions generally have the following problems: a lack of team composition, difficulty in independent research and development, single course content, and an insufficient validation of course effectiveness. In order to more effectively promote the sustainable development of China’s future course, STEAM education institutions should focus on strengthening the following development strategies: gradient team building, continuous independent research and development, cutting-edge projects, mechanisms of curriculum transformation, and multi-angle course effectiveness verification.