The use of gamification and AR technology is found to have the capacity to enhance students’ interest. In this study, the researchers chose a class in a vocational college in China as a specific case that used the gamified AR mobile application, called XploreRAFE+. This study aimed to explore the use of XploreRAFE+ in the formation of learners’ interest in order to answer the research question: What is the process of interest formation among vocational college students in a gamified AR environment, and how does it sustain students’ motivation in learning English? The data for the study were collected via observations, interviews, and document analysis. The findings of the study indicated that in a gamified AR environment, vocational college students’ interest was triggered by piquing curiosity. This then led to optimal learning experiences, where eventually the students began to have positive perceptions of English lessons and were motivated to extend learning experience on their own. These three components of interest formation formed a dynamic loop where interest formation was developed in a cyclical process. It is proposed that a cyclical formation of interest developed through a gamified AR environment over time could sustain learners’ motivation.

This study examines how artificial intelligence (AI), Personalization, problem-solving, and augmented reality (AR) technology affect educational outcomes. The rising use of digital technology in education requires understanding how it affects learning to design successful teaching practices. Analyzing student and instructor data using structural equation modelling creates a strong framework for exploring key construct interrelationships. The study focuses on six primary constructs: AI Personalization (AIP), AI Problem Solving (AIPS), Augmented Reality Creativity Enhancement (ARCE), Augmented Reality Engagement (ARE), Augmented Reality Social Interaction (ARSI), and Learning Outcomes (LOS). These three dimensions are positively connected, showing that strategic AI and AR applications in education could transform the experience. These associations were assessed using path analysis on 357 preschool instructors' survey responses. Results show a favourable association between AI personalization, AR engagement, ARCE, and ARSI (t = 7.947, p < 0.001). AIPS, ARCE, and ARSI have significant beta values (p < 0.001): β = 0.331, β = 0.559, and β = 0.227, indicating that LOS directly impacts these variables. Interaction effects show that LOS moderates the connection between AIP, AIPS, are-ARCE, and ARSI, but not ARSI (β = -0.116, p <0.001; β = 0.106, p= 0.026; β = 0.082, p = 0.086. This study has implications for educators, policymakers, and developers who want to learn how to use AI-AR to engage and delight children in learning. These findings feed training and resources to improve early learning with these technologies.