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.

Augmented reality (AR) can represent a contextualised scientific inquiry environment in which students may explore the real world and develop science process skills via interacting with rich information from virtual systems. However, it remains a challenge for most students to complete scientific inquiry tasks without proper support. Research evidence has suggested the potential of reflective scaffolding when applying scientific inquiry. Accordingly, we designed a contextualised reflective mechanism-based AR learning model to assist students in completing scientific inquiry tasks. Guided by the proposed model, we designed four stages of scientific inquiry learning: conceptual understanding, reflective cognition, in-depth inquiry, and knowledge building. A quasi-experiment and lag sequential analysis were conducted by recruiting 81 sixth-grade students to examine the effects of the proposed model on their scientific inquiry learning performances, higher order thinking, and behavioural patterns. The experimental results reveal that the proposed approach improved students’ inquiry learning performances and higher order thinking tendency (problem-solving tendency and metacognitive awareness). Moreover, the evidence from this study also suggests that the students who learned with the proposed approach exhibited more observation, comparison, exploration, and reflection behavioural patterns in the field trip than those who learned without the contextualised reflective mechanism. Implications are discussed.

Close links between students' conceptions of and approaches to learning were established in the past research. However, only a few quantitative studies investigated this relationship particularly with regard to mobile learning (m‐learning). The correlation between learners' conceptions and approaches to m‐learning was analysed using a partial least squares analysis applied to data obtained from a sample of 971 undergraduate students in China. The results indicated that students' conceptions of m‐learning could be classified into reproductive, transitional, and constructive levels. Students may hold multiple m‐learning applications than a predominant one; hence, examining m‐learning as one monolithic entity may provide limited information. Latent profile analysis identified four learning profiles based on students' preferred m‐learning applications: passive, mixed, surface‐supportive, and high‐engagement.. Moreover, a general trend was observed, whereby students with reproductive and surface‐supportive learning profiles showed a tendency to adopt surface approaches, whereas those expressing constructive and mixed learning profiles were more inclined to adopt deep approaches. Interestingly, students with transitional conceptions and high‐engagement learning profiles tended to take both surface and deep approaches.