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Cross-modal deep learning enhanced mixed reality accelerates construction skill transfer from experts to students
Chenyu Wang1,2, Meng Zhu2, Sharifah Akman Syed Zakaria3
1School of Civil Engineering, University Sains Malaysia, Trankarian Nibong Tebal, 100032, Penang, Malaysia.
Scientific Reports
|October 2, 2025
Summary
This study introduces a mixed reality training system using cross-modal deep learning to transfer expert tacit knowledge to construction students. The novel approach enhances skill acquisition, accuracy, and knowledge retention in construction education.
Area of Science:
- Construction Education
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Traditional construction training struggles to effectively transfer implicit expert knowledge.
- Experienced practitioners' tacit knowledge is difficult to capture and transmit.
- Workforce development and knowledge preservation are key industry challenges.
Purpose of the Study:
- To develop a novel system for transferring expert tacit knowledge to construction students.
- To address limitations in traditional construction training methods.
- To leverage mixed reality and cross-modal deep learning for enhanced education.
Main Methods:
- Developed a framework integrating multimodal data collection from construction experts.
- Utilized cross-modal deep learning algorithms to identify and represent critical knowledge patterns.
- Implemented an immersive mixed reality training environment.
Main Results:
- Students using the system showed 32.4% faster skill acquisition and 22.4% higher accuracy.
- Significant improvements in knowledge retention, situational awareness, and adaptive problem-solving were observed.
- Experimental validation involved 80 participants comparing the system to traditional methods.
Conclusions:
- Cross-modal deep learning can formalize expert tacit knowledge into learnable representations.
- The mixed reality system offers a scalable solution for construction education.
- The approach effectively addresses workforce development and knowledge preservation needs in the construction industry.
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