Implementing Augmented Reality for Enhanced Technical Skills Training in Vocational Education

Thesis Abstract

The rapid advancement of digital technologies has transformed vocational education, necessitating innovative instructional techniques to enhance the acquisition of technical skills. Despite the proliferation of traditional training methods, there remains a significant gap in engaging, interactive, and practical learning experiences that effectively prepare students for real-world technical challenges. This study aims to investigate the implementation of augmented reality (AR) as a pedagogical tool to improve technical skills training in vocational education institutions. The specific objectives include evaluating the effectiveness of AR-based training modules on students’ skill acquisition, examining students’ and instructors’ perceptions of AR integration, and identifying the factors influencing the successful deployment of AR in vocational settings. Employing a mixed-methods research design, the study integrates quantitative and qualitative approaches to provide comprehensive insights. The quantitative component involves a quasi-experimental pretest-posttest control group design, with a sample size of 200 vocational students from technical colleges, evenly divided into experimental and control groups. The experimental group interacts with AR-enhanced training modules, while the control group uses conventional instructional methods. Data collection instruments include standardized skill assessment tests, questionnaires measuring perceived usefulness and ease of use based on the Technology Acceptance Model (TAM), and structured observation checklists. The qualitative component comprises focus group discussions and semi-structured interviews with 20 students and 10 instructors, aimed at exploring perceptions, challenges, and facilitators of AR integration. Data analyses involve descriptive statistics, independent t-tests, and analysis of covariance (ANCOVA) to assess differences in skill improvement across groups, while thematic analysis is employed for qualitative data to interpret emergent themes. Expected findings indicate that students in the AR-based training group will demonstrate significantly higher skill acquisition levels compared to those in the conventional training group, supported by statistically significant differences (p < 0.05). Additionally, the study anticipates positive perceptions of AR’s usability and engagement potential among both students and instructors, though challenges such as technical literacy and resource availability may influence adoption rates. The findings are expected to validate the Technology Acceptance Model (TAM) in the context of AR adoption in vocational training, with perceived ease of use and perceived usefulness emerging as key determinants. This research contributes to knowledge by providing empirical evidence on the effectiveness of AR technology in vocational skill development, extending existing learning theories such as constructivism and the Cognitive Load Theory within a practical setting. The integration of the TAM framework provides a theoretical underpinning for understanding technology acceptance barriers and facilitators in vocational education environments. The study also offers a conceptual model illustrating the relationships among AR intervention, learner engagement, skill acquisition, and acceptance factors. The main conclusion underscores the potential of AR to revolutionize technical training by fostering experiential, immersive, and learner-centered environments, thereby increasing motivation and improving competency outcomes. The study recommends that vocational institutions adopt AR technologies, emphasizing the need for targeted training programs for instructors, investment in technological infrastructure, and ongoing evaluation of implementation practices. Limitations such as resource constraints and technological literacy gaps are acknowledged, and suggestions for future research include longitudinal studies to assess long-term impacts and scalability considerations across diverse vocational contexts. Overall, this research provides a foundational framework for integrating AR into vocational education strategies, aiming to bridge the gap between conventional training methods and the evolving demands of industry 4.0.