The Integrating AI in teaching factories to advance indonesian vocational schools - Sintha Wahjusaputri; Tashia Indah Nastiti; Bunyamin Bunyamin; Johan Johan (Jurnal Pendidikan Vokasi, 2026)
This study aims to develop and evaluate an Artificial Intelligence (AI)-based teaching factory model to enhance students’ vocational competencies in alignment with industry standards. It addresses a gap in the existing literature, where teaching factory models and AI applications in education have largely been investigated separately, with limited empirically validated frameworks integrating AI to support adaptive learning and real-time performance assessment in vocational education. In this study, an AI-based teaching factory is operationally defined as a learning environment that utilizes AI to provide personalized instruction, real-time performance analytics, and industry-simulated production workflows. A mixed-methods research approach was employed, comprising needs analysis, system design and development, field trials, and effectiveness evaluation. Instrument validity was assessed using Aiken’s V, while reliability was evaluated using Cronbach’s alpha. Qualitative data collected through interviews, observations, and document analysis were analyzed thematically to identify patterns related to learning outcomes, implementation processes, and operational challenges. The study was conducted at SMKN 1 Pacet Cianjur and SMKN 11 Bandung in West Java Province, Indonesia. The findings indicate that the proposed AI-based teaching factory model significantly enhances students’ problem-solving skills, critical thinking, and readiness for technology-driven workplaces. This study contributes a validated and replicable implementation framework for integrating AI into vocational teaching factories and provides practical recommendations for teacher capacity building, curriculum alignment, and infrastructure development to support sustainable, industry-relevant vocational education.