Bridging the Industry 4.0 Skills Gap: Development of Case-Based Learning Model in AI-Powered Teaching Factory for Automotive Vocational Education

Hasan Maksum; Wakhinuddin Simatupang; Dedy Irfan; Martias; Muhibbuddin; Hanapi Hasan

doi:10.3991/ijim.v20i10.61583

Authors

Hasan Maksum Universitas Negeri Padang, Padang, Indonesia https://orcid.org/0000-0003-1321-8888
Wakhinuddin Simatupang Universitas Negeri Padang, Padang, Indonesia https://orcid.org/0009-0005-3617-9596
Dedy Irfan Universitas Negeri Padang, Padang, Indonesia https://orcid.org/0000-0002-8441-1178
Martias Universitas Negeri Padang, Padang, Indonesia
Muhibbuddin Universitas Syiah Kuala, Banda Aceh, Indonesia https://orcid.org/0009-0008-4433-2732
Hanapi Hasan Universitas Negeri Medan, Medan, Indonesia https://orcid.org/0000-0003-2669-463X

DOI:

https://doi.org/10.3991/ijim.v20i10.61583

Keywords:

Case-Based Learning, AI-Powered Teaching Factory, Industry 4.0, Artificial Intelligence

Abstract

This study aimed to develop a case-based learning (CBL) in artificial intelligence (AI)-powered teaching factory (CAI-TEFA) model to improve Industry 4.0 competencies of automotive vocational education students. The model was designed to address the skills gap between vocational education graduates and industry needs, integrating AI, Internet of Things (IoT), and automation systems. The development used the Research and Development (R&D) method with a 4D framework (define, design, develop, and disseminate), followed by testing through a quasi-experimental design on 68 students of the Automotive Engineering Education Study Program. Moreover, the CAI-TEFA has four main components, namely Initial Ability (basic skills), AI-Integrated Teaching Factory (TEFA) (technological infrastructure), CBL Framework with AI Integration (pedagogical mediator), and dual outcomes in the form of Industry 4.0 Competencies and Enhanced Learning Outcomes. The CAI-TEFA learning framework generates a systematic four-stage syntax with AI support, comprising Real Industry Case Analysis, Problem Identification and Diagnosis, Solution Development and Testing, as well as Implementation and Reflection. The results showed that the CAI-TEFA was highly effective with excellent validity, explaining 30.6% of the variance in Industry 4.0 competencies and 24.8% of Enhanced Learning Outcomes. Quasi-experimental testing showed significant effectiveness, with the experimental group achieving a post-test score of 84.25 compared to the control group at 67.35 and a gain score of 37.10 vs. 21.53 points (t-count 8.672 > t-table 2.000, p < 0.001). Furthermore, the model was declared “Highly Practical” with an achievement of 94.58%, showing a transformative contribution to automotive vocational education responsive to Industry 4.0, which could prepare graduates with adaptive skills to face the complexities of modern automotive technology.

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