Developing talented students in robotics, AI, and coding with Work-Integrated Learning

Background and Context As automation and digitalization transform industries globally, educational systems must cultivate students with competencies in Robotics, Artificial Intelligence, and Coding (RAC) to drive technological innovation. This study focuses on talented secondary students as the unit of analysis, examining how integrated learning approaches can effectively develop RAC competencies within Thailand’s educational ecosystem through industry-academic partnerships.Objective This study examined the effectiveness of four pedagogical models: Work-Integrated Learning (WIL), Project-Based Learning (PBL), Blended Learning (BLN), and Accelerated Learning (ACL). It also investigated how intrapersonal and environmental factors influence these models and identified the main elements supporting students’ perceived RAC competency development.Method A sequential explanatory mixed-methods design was applied with 3,359 applicants, from which 86 high-achieving students were selected through a multi-tiered process. Quantitative analyses using repeated measures ANOVA and multiple regression assessed factor influences, while qualitative data from interviews, reflective journals, and projects were analyzed thematically.Findings The results demonstrated that WIL was the most effective approach, followed by PBL, BLN, and ACL. Intrapersonal factors exerted stronger effects on WIL, PBL, and BLN, whereas environmental factors were more influential for ACL. Five drivers of students’ perceived success in RAC learning emerged: skill acquisition, hands-on learning, industry exposure, confidence and motivation, and collaboration.Implications The study highlights the importance of prioritizing WIL and PBL in RAC education. For the CS Education community, the findings provide insights into how pedagogical approaches interact with learner characteristics and contexts, informing the design of effective frameworks for technology talent development.