Integrating Generative Artificial Intelligence into Work-Based Learning: A Review of the Automotive Engineering Program in Malaysia
List of Authors
Nelidya Md. Yusoff, Nur Ayuni Shamsul Bahrin, Rozlinda Othman
Keyword
Work-Based Learning, Generative Artificial Intelligence, Automotive Engineering Programs, Industry 4.0, Curriculum Co-Design, Simulation and Virtual Prototyping, Malaysia
Abstract
This study explores the integration of generative artificial intelligence (AI) into work-based learning (WBL) to address the persistent mismatch between university curricula and industry expectations in Malaysia’s automotive engineering programs. The review examines scholarly works from 2020 and 2025 to identify how AI-supported approaches could strengthen competency-based education. Using a structured narrative literature review, this paper analyses global WBL models and synthesizes findings into a conceptual framework aligning curriculum design, industry needs, and AI capabilities. Results reveal that Malaysia’s WBL practices remain short-term and fragmented compared to international standards, limiting graduate readiness for Industry 4.0. The proposed framework positions generative AI as an enabler for simulation-based learning, adaptive feedback, and data-driven curriculum refinement. This integration benefits universities, industries, and students by enhancing employability, promoting equitable access to digital learning, and advancing Malaysia’s transition toward sustainable, innovation-driven engineering education.