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This study introduces an AI-integrated learning model designed to enhance students’ understanding of cement brick properties in Technical and Vocational Education and Training (TVET). Conventional teaching approaches in construction materials often rely on theoretical explanations and laboratory testing, which can be time-consuming and limit students’ ability to visualise material behaviour.

To address this issue, the innovation integrates artificial intelligence (AI), simulation, and experimental data into a structured learning approach. Students are exposed to real datasets obtained from laboratory testing, including compressive strength, density, and water absorption. These data are then utilised within a conceptual AI-based predictive framework to help students analyse and predict material performance.

The implementation of this model promotes experiential and data-driven learning, enabling students to connect theoretical knowledge with practical application. Preliminary observations indicate improved student engagement, enhanced analytical thinking, and better understanding of material behaviour.

This innovation also supports the integration of digital technologies in TVET education, aligning with Industry 4.0 and Construction 4.0 initiatives. By transforming traditional teaching methods into an interactive and intelligent learning experience, the proposed approach contributes to the development of future-ready graduates in the construction and engineering fields.