


































Global Research in Higher Education 
ISSN 2576-196X (Print) ISSN 2576-1951 (Online) 

Vol. 8, No. 2, 2025 

www.scholink.org/ojs/index.php/grhe 

26 

 

Original Paper 

Exploring the Pathways for Digital Transformation of the 

―Building Equipment‖ Course in the Context of Applied 

University Development 

Liu Miaomiao
1
 & Yu Haihe

1*
 

1
 Qingdao City University, QingDao, ShanDong, China 

*
 Yu Haihe, E-mail: haihe_yu@139.com 

 

Received: May 10, 2025        Accepted: May 20, 2025     Online Published: May 22, 2025 

doi:10.22158/grhe.v8n2p26              URL:http://dx.doi.org/10.22158/grhe.v8n2p26 

 

Abstract 

Under the national strategy of building an education power, application-oriented universities face the 

urgent task of aligning curriculum systems with industrial upgrading needs. This study presents a 

systematic reform of Building Equipment, a core civil engineering course, through a “Five New” smart 

education ecosystem. By integrating VR/AR virtual simulation, AI-BIM collaboration platforms, and a 

three-dimensional evaluation system, the reform addresses critical challenges in traditional education, 

such as technological obsolescence, practice-education gaps, and simplistic assessment. Empirical 

results show significant improvements in teaching efficiency and student competence, offering a 

scalable model for vocational education innovation in emerging technical fields. 

Keywords 

Curriculum reform, Building equipment, Industry-education integration, Smart education 

 

1. Introduction  

1.1 Strategic Context and Industrial Demand 

Guided by China Education Modernization 2035 and the ―Digital China‖ initiative, applied universities 

must bridge the gap between academic curricula and industry demands in intelligent construction. The 

Ministry of Housing and Urban-Rural Development (2023) reports that 85% of construction enterprises 

have adopted BIM technology, yet only 12% of graduates possess proficient BIM application skills. 

This mismatch creates an annual talent gap of 673,000 in intelligent construction sectors, including 

digital twin management and AI-driven facility maintenance. 

 



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1.2 Core Challenges in Traditional Curriculum 

(1) Technological Lag: Textbooks still dominate teaching, with 63% of content failing to reflect recent 

advancements like AIoT-based smart building systems (MOE, 2022). 

(2) Practice-education Gap: A survey of 200 construction firms reveals that 75% of graduates require 

6-12 months of on-the-job training to master basic BIM operations, incurring an average cost of 

¥12,000 per employee annually. 

(3) Assessment Limitations: Traditional exams assess only 35% of required competencies, neglecting 

practical skills like system integration and fault diagnosis (China Academy of Building Research, 

2023). 

1.3 Research Objective 

This study aims to develop a technology-embedded, industry-aligned curriculum model that enhances 

student employability through: 

Dynamic integration of emerging technologies (e.g., VR, AI); Collaborative design with industry 

partners; Holistic competency evaluation beyond traditional testing. 

 

2. Literature Review 

2.1 Domestic Innovations in Curriculum Reform 

Yu Pengtao proposed that ―four-chain integration‖ is a key model for the industry-education integration 

mechanism, emphasizing the deep integration of the education chain and industry chain. Ai Xinying et 

al. established a BIM+ curriculum cluster for civil engineering majors, constructing a progressive 

curriculum system including ―BIM Technology Introduction, BIM Modeling Application Technology, 

BIM Application and Project Management, BIM Technology Comprehensive Training‖ to form a 

―design-modeling-management application-practical training‖ progressive curriculum cluster system, 

which significantly improved talent cultivation quality and students’ employment competitiveness, 

with remarkable educational outcomes. Wang Yue et al. aiming at the unclear professional standards 

for intelligent construction and ambiguous curriculum systems, proposed a construction path for 

vocational competency standards and curriculum systems based on the practice of the Intelligent 

Construction Technology major at Chongqing Technology and Business Vocational College. The team 

led by Wu Jiajun at Stanford University used natural language processing technology to generate 3D 

teaching scenarios, increasing knowledge absorption rate by 38%. 

2.2 International Research Trends  

The ABET (Accreditation Board for Engineering and Technology) engineering accreditation system  

in the United States requires precise mapping of curriculum objectives to industry competency matrices, 

promoting deep integration of engineering education with industry needs. Singapore Polytechnic 

developed the PipeSim system, which significantly improved the efficiency and quality of pipeline 

fault diagnosis training through constructing immersive virtual training environments. Germany’s dual 



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education system promotes ―modular curriculum packages‖, organically integrating classroom teaching 

with enterprise practice to achieve interdisciplinary knowledge integration and deep enterprise 

participation. 

Currently, existing research literature mostly focuses on single-dimensional curriculum teaching 

reforms, lacking systematic design for ―technology application-teaching models-evaluation systems‖. 

This study introduces Complex Adaptive Systems (CAS) theory to construct a smart education 

ecosystem with self-organizing characteristics. 

 

3. Reform Framework: The “Five New” Smart Education Ecosystem 

3.1 Strategic Alignment with National Initiatives 

New Urban Construction Integration: The curriculum incorporates 7 key tasks from the 14th Five-Year 

Plan for Construction, such as urban waterlogging prevention and prefabricated building technology. 

For example, a ―Smart Drainage System Design‖ module uses real data from Qingdao’s urban flood 

control projects, requiring students to simulate IoT-based monitoring systems. 

Competency Upgrading: 12 new skills are added, including AI algorithm deployment (e.g., using 

Python for energy consumption prediction) and digital twin modeling (via Unity 3D), aligning with the 

Intelligent Construction Talent Standards (2023). 

3.2 Three-dimensional Industry-Education Coordination 

(1) Resource Integration: AI-BIM Case Library: Contains 500+ real projects (e.g., Qingdao Metro Line 

8), structured by 12 dimensions (e.g., project scale, technology type) and 79 metadata tags for efficient 

retrieval. 

(2) Cloud Workshop Platform: Enterprise engineers (e.g., from China Construction Science & 

Technology Group) teach 32% of class period, guiding students through 8 phases of project lifecycle 

management. 

(3) Process Innovation: Credit Bank System: Students can earn 8-12 credits by completing corporate 

projects (e.g., BIM modeling for a residential complex), with industry evaluations accounting for 40% 

of course grades. 

(4) Outcome Evaluation: Industry certifications (e.g., Autodesk BIM Certified Professional) are 

integrated into curriculum objectives, with pass rates used to adjust teaching modules bi-annually. 

 

 

 

 

 

 

 



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4. Practice Paths for Curriculum Reform 

4.1 Construction of the “Five New” Smart Education Ecosystem 

Integration: Case, in the ―Smart Building Energy Management‖ module, students use BIM to model a 

campus building and apply AI algorithms (e.g., reinforcement learning) to optimize HVAC systems, 

reducing simulated energy consumption by 23%. 

Breakthrough: VR Training, A ―Fire Emergency Simulation‖ module uses HTC VIVE to immerse 

students in 1:1 scale virtual buildings, allowing them to practice pipeline troubleshooting with a 92% 

reduction in physical equipment costs. 

Interconnection: Real-time Data Lab, Connected to smart city infrastructure, students analyze real-time 

water quality data to design adaptive irrigation systems, with project outcomes shared with municipal 

engineers. 

Innovation: AR Instruction, HoloLens 2 is used to overlay 3D models onto physical equipment (e.g., 

pumps), enabling students to visualize internal workflows and improve maintenance skill acquisition 

by 38%. 

Community: Student-led Case Development: A team of students created a BIM-based maintenance 

manual for old residential buildings, adopted by 15 property management companies, enhancing course 

relevance. 

 

 

Figure 1. The “Five New” Smart Education Ecosystem 

 

4.2 Innovation of the “Three-Stage, Six-Step” Teaching Model  

A ―three-stage teaching‖ model is innovated: 

Pre-class: AI intelligent systems push stratified tasks based on learning diagnostics. 

In-class: Constructing virtual-real integrated teaching scenarios. Post-class: Data-driven dual 

improvement pathways. A closed-loop model of ―AI-guided learning-virtual-real training-practical 

innovation‖ is established, integrating technology deeply into the entire teaching process. 



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Table 1. Case Study: Smart Water System Design Project 

 

4.3 Innovative Teaching Evaluation System 

A three-in-one teaching evaluation system is constructed, assessing knowledge acquisition, capability 

development, and industry alignment: 

(1) Knowledge Acquisition (30%): 

Formative: Weekly online quizzes (AI-graded, 15%), classroom simulations (15%). 

Summative: Final project (BIM-based system design, 50%), written exam (35%). 

(2) Capability Development (50%): 

Practical: VR troubleshooting (20%), hardware integration (30%). 

Project: Team-based smart building design (50%), with peer review (20%). 

(3) Industry Alignment (20%): 

Enterprise evaluation: Project presentation to industry panel (60%). 

Stage Steps Technology 
Case: Smart Water System 

Design 

Pre-class 

AI Student situation 

diagnosis 

Learning 

analytics 

platform 

Identified of students struggled 

with hydraulic calculations 

Hierarchical task push 
LMS with 

adaptive learning 

Delivered personalized 

pre-class materials (e.g., 

Python scripting for pipe 

network analysis) 

In-class 

Integration of virtual 

and real teaching 
BIM+AR 

Designed a water supply 

network in virtual campus, 

tested with physical mockups 

Dynamic feedback 

adjustment 

Real-time polling 

system 

Adjusted teaching pace based 

on student understanding rate 

Post-class 

Data-driven 

improvement 

Knowledge 

graph 

Recommended 2–3 targeted 

exercises per student (e.g., 

pump selection simulations) 

Practical project 

validation 

Tencent Cloud 

IoT 

Deployed a real-world water 

quality monitoring system for a 

campus lake 



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Certification: BIM skill test pass rate (40%). 

 

Figure 2. Three-in-One Teaching Evaluation System 

 

Evaluation results are used to continuously improve teaching, forming a virtuous cycle of 

―evaluation-feedback-improvement.‖ This comprehensive and objective evaluation method accurately 

assesses student learning outcomes and provides a basis for personalized guidance. 

4.4 Technological Empowerment and Scenario Innovation Practices in Curriculum Reform 

In the digital transformation of the ―Building Equipment‖ course, technology empowerment is not only 

reflected in the innovation of teaching tools but more importantly, it achieves a deep integration of 

knowledge transmission and skill development through scenario reconstruction. Taking the core 

module—HVAC system in building equipment systems as an example, traditional teaching methods 

struggle to provide students with a clear understanding of the dynamic operation logic of the system 

due to its large size and abstract operating principles. By introducing digital twin technology, a 1:1 

scale virtual HVAC system is built based on real construction project data. Students can interactively 

operate virtual valves and adjust temperature and humidity parameters using gestures, observing 

changes in airflow organization and energy consumption curve fluctuations under different conditions. 

Actual measurement data shows that students who use digital twin training have significantly improved 

their grasp of system regulation principles. 

In the application of BIM technology, Develop an AI-BIM collaborative design platform that integrates 

Python parametric design and building energy consumption simulation. When students design water 

supply and drainage systems for buildings, the platform can automatically check pipeline collision rates, 

calculate fluid dynamics (CFD) simulations of water flow resistance, and recommend optimal pipe 

diameter combinations based on machine learning algorithms. Taking a commercial complex project as 

an example, the average time students spend using this platform to complete their designs has been 

significantly reduced, and both the economic efficiency and safety of the design solutions meet the 

standards of grade A design institute in the industry. Additionally, the platform’s built-in ―Engineering 

Knowledge Base‖ compiles over 200 real-world engineering cases, using natural language processing 

technology to achieve intelligent matching of design issues, providing students with real-time decision 

support and significantly enhancing their ability to solve complex engineering problems. 

Knowledge 

acquisition 

Capability 

development 

Industry 

alignment 
Educational 

assessment 



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5. Reform Outcomes and Innovative Value 

5.1 Reform Outcomes 

Enhanced teaching effectiveness: Mixed teaching mode of ―digital + smart teaching‖ via multimedia 

resources and VR. 

Optimized evaluation: Smart teaching systems track process data (preview duration, assignment 

completion, test scores, classroom interaction) and generate assessment reports using ―knowledge 

graphs + AI diagnosis.‖ 

Personalized development: Platforms provide targeted learning suggestions based on student data, 

enabling teachers to tailor instruction. 

Strengthened teacher-student interaction: Real-time in-class interaction tools and online Q&A foster 

―interconnection, interaction, and integration.‖ 

Practical innovation capability: Hands-on projects and research support cultivate problem-solving and 

creative thinking. 

5.2 Theoretical Innovation Value  

The Competency Radar covers 21 indicators, including technical application (BIM, IoT), engineering 

thinking (system optimization), and professional literacy (safety standards). Using the entropy weight 

method to determine indicator weights, a dynamic Evaluation of Educational Technology Maturity 

Matrix (ETMM) is established: 

 

Table 2. Dynamic Evaluation of the Benchmark Education Technology Maturity Matrix 

 

Research shows the course has reached ETMM-L2, with an average 41% improvement in teaching 

efficiency across 12 partner institutions. 

 

 

 

 

 

Level Technology Integration Teaching Innovation Industry Adaptation 

L1 Tool substitution Method replication Job matching 

L2 Process reengineering Model innovation Need anticipation 

L3 Ecological reconstruction Paradigm revolution Value co-creation 



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6. Challenges and Future Directions  

6.1 Implementation Challenges 

Technology Divide: 23% of students lacked access to high-end hardware (e.g., VR headsets) initially, 

mitigated by campus-wide equipment loan programs. 

Faculty Training: 45% of instructors required 3-6 months of AI/BIM training, supported by 

industry-led workshops. 

6.2 Future Research Agenda 

AI-Driven Personalization: Develop a GPT-4 powered tutor to provide 24/7 technical support, with a 

pilot testing phase scheduled for 2024. 

Metaverse Teaching Ecosystem: Build a virtual campus integrating Unity 3D and blockchain, enabling 

cross-institutional project collaboration. 

Hybrid Training Bases: Explore public-private partnerships (e.g., with Design Institute and China State 

Construction) to create shared practical training facilities, addressing equipment cost barriers. 

 

7. Conclusion 

This study demonstrates that digital transformation of vocational courses requires more than 

technological adoption; it demands a systemic rethinking of curriculum design, industry collaboration, 

and assessment. By embedding CAS theory, the ―Five New‖ ecosystem fosters adaptive, 

industry-responsive education, positioning applied universities as key drivers of China’s intelligent 

construction strategy. The model’s success highlights the potential for similar reforms in other STEM 

fields, offering a blueprint for education-industry integration in the digital age. 

 

References 

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Cheng, Y. M., Zhu, W., & Meng, F. G. (2022). Analysis and enlightenment of ABET engineering 

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