Artificial Intelligence (AI) is currently changing how people live and work. Its importance has prompted educators to begin teaching AI in secondary schools. This study examined how Chinese secondary school students’ intention to learn AI were associated with eight other relevant psychological factors. Five hundred and forty-five secondary school students who have completed at least one cycle of AI course were recruited to participate in this study. Based on the theory of planned behavior, the students’ AI literacy, subjective norms, and anxiety were identified as background factors. These background factors were hypothesized to influence the students’ attitudes towards AI, their perceived behavioral control, and their intention to learn AI. To provide more nuanced understanding, the students’ attitude towards AI was further delineated as constituted by their perception of the usefulness of AI, the potential of AI technology to promote social good, and their attitude towards using AI technology. Similarly, the perceived behavioral control was operationalized as students’ confidence towards learning AI knowledge and optimistic outlook of an AI infused world. Relationships between the factors were theoretically illustrated as a model that depicts how students’ intention to learn AI was constituted. Two research questions were then formulated. Confirmatory factor analysis was employed to validate that multi-factor survey, followed by structural equational modelling to ascertain the significant associations between the factors. The confirmatory factor analysis supports the construct validity of the questionnaire. Twenty-five out of the thirty-three hypotheses were supported through structural equation modelling. The model helps researchers and educators to understand the factors that shape students’ intention to learn AI. These factors should be considered for the design of AI curriculum.

Energy crops are a basic material in the bioenergy industry, and they can also mitigate carbon emissions and have environmental benefits when planted on marginal lands. The aim of this study was to evaluate the potential productivity of energy crops on marginal lands in China. A mechanistic model, combined with energy crop and land use characteristics, and meteorological and soil parameters, was used to simulate the potential productivity of energy crops. There were three main results. 1) The total marginal land in China was determined to be 104.78 × 10⁶ ha. The 400-mm precipitation boundary line, which is the dividing line between the semi-humid and semi-arid zones in China, also divided the marginal land into shrub land and sparse forest land in the southeast and bare land, bare rock land, and saline alkali land in the northeast. 2) The total area of the marginal land suitable for planting energy crops was determined to be 55.82 × 10⁶ ha, with Xanthoceras sorbifolia and Cerasus humilis mainly grown in the northern China, Jatropha curcas and Cornus wilsoniana mainly grown in the southwest and southeast, and Pistacia chinensis mainly grown in the central area, while also having a northeast-southwest zonal distribution. 3) Taking the highest yield in overlapping areas, the potential productivity of target energy crops was determined to be 32.63 × 10⁶ t/yr. Without considering the overlapping areas, the potential productivity was 6.81 × 10⁶ t/yr from X. sorbifolia, 8.86 × 10⁶ t/yr from C. humilis, 7.18 × 10⁶ t/yr from J. curcas, 9.55 × 10⁶ t/yr from P. chinensis, and 7.78 × 10⁶ t/yr from C. wilsoniana.