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Abstract
Artificial intelligence (AI)-enhanced virtual simulation has emerged as a transformative approach for advancing vocational engineering education by addressing limitations of traditional workshop-based training. This study develops and evaluates an AI-enhanced virtual simulation system that integrates pedagogical, functional, and technical alignment under the pedagogical-functional-technical mapping framework. A six-week quasi-experimental study involving 80 students in a cement process control course was conducted. The proposed system combines intelligent tutoring, adaptive task sequencing, and real-time performance analytics to personalize learning pathways according to individual skill levels and learning progress. By simulating authentic industrial scenarios, the platform enables learners to practice complex engineering operations safely and repeatedly, while receiving immediate, data-driven feedback on procedural accuracy, efficiency, and decision-making. The framework is pedagogically grounded in experiential and mastery-based learning, functionally aligned with occupational standards, and technically supported by AI models for learner modeling and assessment. The findings highlight the potential of AI-enhanced virtual simulation to improve skill acquisition, learning engagement, and training scalability, offering a robust digital solution for future-ready vocational engineering education. Results indicate statistically significant improvements in learning performance and operational efficiency for the experimental group. These outcomes should be interpreted cautiously given the study’s contextual and methodological constraints.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EURASIA J Math Sci Tech Ed, Volume 22, Issue 6, June 2026, Article No: em2847
https://doi.org/10.29333/ejmste/18709
Publication date: 04 Jun 2026
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