


































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

Vol. 8, No. 1, 2025 

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

60 

 

Original Paper 

Construction of Smart Construction Course Groups Driven by 

Interdisciplinary Integration 

Lijuan Chen
1*

, Shuo Li
1
, Chuanlei Song

1
, Qingbo Meng

1
, Lingdong Meng

2
, Qingchi Zhang

1
, Chuanli 

Yang
1
 & Zhe Kong

1
 

1
 Qingdao City University, Qingdao, China 

2
 Jiangsu Colleage of Engineering and Technology, Jiangsu, China 

*
 Lijuan Chen, Qingdao City University, Qingdao, China 

 

Received: February 1, 2025    Accepted: February 10, 2025   Online Published: February 14, 2025 

doi:10.22158/grhe.v8n1p60            URL:http://dx.doi.org/10.22158/grhe.v8n1p60 

 

Abstract 

This study explores the innovation in smart construction course groups driven by interdisciplinary 

integration, aiming to construct a smart construction course group centered on interdisciplinary fusion. 

Addressing the challenges in developing a smart construction curriculum system under the context of 

higher education and industry-academia collaboration, this study proposes breaking disciplinary 

boundaries and integrating civil engineering, automation technology, computer science, and other 

fields to establish a curriculum system tailored to the cultivation of new engineering talents. The 

findings indicate that the proposed course group significantly enhances students’ ability to integrate 

interdisciplinary knowledge, apply innovative practices, and collaborate effectively in teams within the 

field of smart construction. This research provides a systematic approach to curriculum design for 

smart construction education and offers theoretical support and practical insights for the promotion 

and application of interdisciplinary education models within the new engineering education 

framework. 

Keywords 

Interdisciplinary Integration, Smart Construction, Course Group Innovation, Collaborative Teaching, 

New Engineering Education 

 

 

 

 

 



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1. Introduction 

1.1 Research Background and Significance 

Smart construction, as a frontier field driving the high-quality development of the construction industry, 

is undergoing profound transformations through technological innovation and industrial upgrades. Its 

core lies in the deep integration of multiple disciplines, especially the application of technologies such 

as computer science, artificial intelligence, electronic information, and automation, which inject new 

momentum into architectural design, construction, and operation. However, interdisciplinary 

integration in smart construction still faces numerous challenges in practice, such as the lack of unified 

technical standards, data collaboration complexities, and the shortage of high-level interdisciplinary 

talents. Therefore, it is essential to explore effective paths for interdisciplinary collaboration from both 

theoretical and practical perspectives to promote the overall development of the smart construction 

field. This research focuses on the innovative model of interdisciplinary collaboration, delving into the 

fusion mechanisms of computer science, artificial intelligence, and civil engineering, and aims to build 

a theoretical framework and practical system with guiding value for the industry’s transformation. 

1.2 Research Problems and Objectives 

This research, based on the integration of civil engineering, computer science, artificial intelligence, 

and electronic information disciplines, proposes a course group system for smart construction through 

interdisciplinary collaboration to overcome the limitations of traditional curriculum structures. The 

research objectives include enhancing students’ comprehensive knowledge structure and innovation 

capabilities to meet the needs of the intelligent transformation of the construction industry. Based on 

the new engineering context, the research combines industry demands and educational status, designs a 

curriculum reform plan, and validates its effectiveness through teaching practices. The results not only 

provide theoretical support and practical guidance for the smart construction field but also lay the 

foundation for cultivating highly skilled and versatile talents for the future. 

 

2. Integration of Smart Construction with Computer Science and Technology 

2.1 Core Applications of Computer Technology in Smart Construction 

Computer technology plays a key role in driving the digital transformation of the construction industry. 

Digital tools such as BIM (Building Information Modeling), 3D modeling, and CAD (Computer-Aided 

Design) significantly improve the efficiency and accuracy of building design and engineering 

construction. These technologies not only enable the simulation and optimization of architectural 

models during the design phase but also provide precise guidance and data support during construction. 

Big data analytics allows for the management of construction projects throughout their lifecycle, 

enabling real-time monitoring of progress. It also optimizes resource allocation and predicts potential 

risks. Cloud computing ensures efficient storage and data processing, supporting collaborative work 

across geographically dispersed teams. With the integration of the Internet of Things (IoT), real-time 



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sensing and transmission of building information further enhance the intelligence of building 

operations, driving the smart application of construction processes (Wang, J., Wang, X., Shou, W. & 

Xu, B., 2020, pp. 621-642). 

2.2 Case Analysis and Technological Innovation in Integration 

Many successful cases demonstrate the integration of smart construction and computer technology in 

practice. For example, a construction design and management system based on BIM technology 

achieves seamless integration of design, construction, and operation through a unified digital platform. 

In one project, the use of BIM technology increased efficiency by 20% and reduced construction errors 

by 15%. Moreover, digital construction systems applied during the construction phase use virtual 

construction techniques to identify problems in advance and optimize solutions, effectively reducing 

changes and resource waste. Additionally, the cloud computing and big data-driven smart construction 

site management system provides intelligent support for the monitoring and regulation of the 

construction process. These cases illustrate how the deep integration of computer technology and the 

construction industry not only provides a solid technological foundation for smart construction but also 

establishes a model for combining theory and practice for future smart buildings (Sacks, R., Brilakis, I., 

Pikas, E., Xie, H. S. & Girolami, M., 2020, p. e14). 

2.3 Challenges and Countermeasures for Interdisciplinary Integration 

Despite the significant achievements in the application of computer technology in smart construction, 

the process of interdisciplinary integration still faces challenges. First, inconsistent technical standards 

across disciplines lead to data interoperability issues, affecting the collaborative application of 

interdisciplinary technologies. Second, data security and privacy issues are increasingly prominent, 

especially in cloud computing and IoT environments, where protecting construction data from attacks 

is a pressing concern. Additionally, the shortage of interdisciplinary talents limits the depth of 

integration. Traditional single-discipline education models focus mainly on knowledge transmission in 

one field and are inadequate for meeting the demand for composite talents in smart construction. In 

contrast, an interdisciplinary education model integrates knowledge from various disciplines and 

cultivates students’ comprehensive abilities in complex engineering environments. To address these 

challenges, the study proposes several strategies: establishing unified data standards and interface 

specifications to ensure interoperability of different disciplinary technologies; enhancing data security 

technologies, including optimizing encryption algorithms and access control mechanisms; and 

designing interdisciplinary courses and practical projects to cultivate talents with multi-disciplinary 

knowledge and practical application capabilities. These measures provide directions for the deep 

integration of smart construction and computer technology, promoting innovation and high-quality 

development in the construction industry. 

 

 



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3. Integration of Smart Construction with Artificial Intelligence 

3.1 Application Scenarios of Artificial Intelligence in Smart Construction 

Artificial Intelligence (AI), as a key driver of smart construction technology development, has shown 

extensive application potential in various critical areas of the construction industry. First, in intelligent 

construction management, AI algorithms enable real-time prediction and dynamic adjustment of 

construction progress, optimizing resource allocation and reducing construction time. Additionally, 

computer vision technology is used for on-site safety monitoring and violation detection, significantly 

improving construction safety. Furthermore, machine learning models analyze historical and real-time 

data to predict engineering quality and potential risks, providing reliable support for preventive 

measures. Second, AI plays a crucial role in automated construction, where robotic technologies enable 

precise assembly of complex structures, material handling, and path planning, improving construction 

accuracy and efficiency. Finally, in intelligent design and optimization, AI-assisted design tools use 

generative algorithms and optimization models to rapidly generate diverse architectural design 

solutions. These tools evaluate factors such as structural stability, energy consumption, and 

construction cost to optimize design schemes, enhancing the efficiency and innovation of architectural 

planning (Zhang, J., Teizer, J., Lee, J. K., Eastman, C. M. & Venugopal, M., 2015, pp. 183-195). 

3.2 Case Studies and Technological Innovations 

The integration of smart construction and AI has achieved significant success in various practical cases. 

For example, in some smart construction projects, AI-driven robotic construction technologies have 

greatly improved the efficiency of prefabricated building assembly, particularly in areas such as path 

planning, collaborative operation, and obstacle avoidance. AI algorithms are also widely applied in 

real-time monitoring systems for smart construction sites, optimizing equipment scheduling, material 

usage, and safety management through environmental data analysis. Additionally, AI-powered decision 

support systems integrate data from the design, construction, and operation phases to optimize the 

entire building lifecycle. Some enterprises employ AI models to generate complex architectural designs 

and evaluate design schemes in terms of structural stability, energy efficiency, and construction cost. 

These cases demonstrate AI’s critical role in enhancing construction efficiency, optimizing 

architectural design, and ensuring safety management, providing a new technological path for the 

digital transformation of the construction industry. 

3.3 Challenges and Countermeasures for Interdisciplinary Integration 

Despite the promising future of AI applications in smart construction, several challenges persist. First, 

AI models must be highly adaptable and generalized for diverse smart construction scenarios, but many 

existing models are specialized and lack the flexibility to adapt to dynamic construction environments. 

Second, data interoperability and standardization issues between different disciplines significantly 

hinder the deep integration of AI technology. For instance, compatibility between Building Information 

Modeling (BIM) and AI models remains limited, affecting collaborative efficiency. Furthermore, smart 



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construction projects involve interdisciplinary collaboration, requiring participants to possess 

comprehensive technical knowledge and strong teamwork abilities. However, the lack of 

interdisciplinary composite talents has restricted the widespread application of AI technology. To 

address these issues, solutions include establishing unified data standards and open interface systems to 

improve technological compatibility, fostering industry-academia collaboration to develop 

interdisciplinary courses and projects, and promoting the deep integration of AI and the construction 

industry through experimental projects. Implementing these strategies will effectively facilitate AI 

applications in smart construction and accelerate the industry’s transition toward intelligent 

development. 

 

4. Integration of Smart Construction with Electronic Information and Automation 

4.1 Core Applications of Electronic Information and Automation Technology in Smart Construction 

Electronic information and automation technology serve as key enablers for the intelligent and digital 

transformation of smart construction. These technologies utilize sensor networks to collect 

multi-dimensional data in real-time throughout the construction and operational phases, providing 

reliable support for monitoring, optimization, and decision-making in the construction process. For 

example, environmental monitoring systems deploy various sensors to collect data on temperature, 

humidity, and light intensity, enabling real-time feedback on site conditions and optimized resource 

allocation. Additionally, automation control systems regulate machinery and equipment in real time, 

ensuring precision and efficiency in construction processes. Automated assembly lines, widely applied 

in prefabricated construction, exhibit high efficiency, accuracy, and intelligence from component 

manufacturing to on-site installation. Furthermore, the integration of IoT (Internet of Things) 

technology connects dispersed devices and data into a unified platform, enhancing full-process project 

management and improving the intelligence level of construction and operation workflows (Li, H., Lu, 

W. & Huang, T., 2019, pp. 499-507). 

4.2 Case Studies and Technological Innovations 

The integration of smart construction with electronic information and automation technology has led to 

significant advancements in practice. For instance, in a prefabricated construction project, an intelligent 

control system-driven automated production line enabled the high-precision manufacturing and 

distribution of building components, effectively reducing material waste and shortening the 

construction cycle. Additionally, smart monitoring systems collect real-time environmental and 

equipment operation data, leveraging data analysis algorithms to optimize construction workflows, 

significantly improving both efficiency and safety. In the field of smart buildings, intelligent lighting 

and energy management systems have become exemplary applications. These systems utilize sensors 

and automated control devices to optimize energy distribution, reducing operational costs while 

supporting sustainable green building development. These examples demonstrate how electronic 



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information and automation technologies provide a strong technological foundation for smart 

construction, playing a pivotal role in modernizing the construction industry. 

4.3 Challenges and Countermeasures for Interdisciplinary Integration 

Despite the promising future of electronic information and automation in smart construction, several 

challenges remain in their integration. First, inconsistencies in communication protocols and data 

formats across different devices hinder interconnectivity and data sharing, limiting widespread 

adoption. Second, the vast amount of data generated in smart construction projects presents high 

complexity in real-time processing and analysis, imposing stringent demands on software and hardware 

performance. Additionally, knowledge gaps between electronic information and traditional 

construction disciplines create communication and coordination challenges within interdisciplinary 

teams. To address these issues, it is essential to establish standardized data protocols and unified 

interfaces to ensure seamless system integration. In education, interdisciplinary courses and 

practice-based learning programs should be introduced to cultivate professionals with expertise in both 

construction and electronic information technology. Furthermore, adopting high-performance 

computing and distributed data processing technologies will enhance the efficiency of smart 

construction systems. By implementing these strategies, the integration of electronic information and 

automation with smart construction can be further deepened, providing strong technological support for 

the intelligent transformation of the construction industry. 

 

5. Interdisciplinary Education Model and Talent Development 

5.1 Course Group Design Approach and Objectives 

In the field of smart construction, an interdisciplinary education model must align with the industry’s 

urgent demand for versatile talents, making the construction of course groups a critical component. 

Based on the new engineering education framework, this study proposes a course group design 

approach that is industry-driven and technology innovation-centered. Specifically, the approach 

integrates knowledge from civil engineering, computer science, artificial intelligence, and electronic 

information to develop course modules that combine theoretical depth with practical application, 

addressing the diverse job requirements in smart construction. The primary goal of the course group is 

not only to cultivate students’ ability to integrate interdisciplinary knowledge but also to enhance their 

innovation and problem-solving skills in complex engineering contexts, ultimately driving educational 

reform and industrial development in smart construction. Furthermore, a dynamic course adjustment 

mechanism is established to ensure that the curriculum evolves in sync with technological 

advancements in the industry, continuously optimizing its content. This ensures that students’ 

knowledge framework remains aligned with industry trends, effectively supplying the construction 

sector with highly competitive, interdisciplinary professionals. 

 



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5.2 Curriculum Structure and Optimization of Teaching Content 

Curriculum design is a core component of the interdisciplinary education model. This study constructs 

a course group system comprising four major modules: general education courses, foundational 

discipline courses, core professional courses, and innovation-driven practice courses. For example, 

general education courses include Python programming and IoT technology, focusing on cultivating 

students’ fundamental digital skills. Core professional courses cover BIM modeling, an introduction to 

smart construction, and AI applications, providing students with essential interdisciplinary knowledge. 

Additionally, innovation-driven practice courses emphasize project-based learning, integrating 

real-world engineering cases to help students apply interdisciplinary knowledge in practice and 

enhance their problem-solving abilities. At the same time, optimizing teaching content is equally 

important. By incorporating the latest technological advancements, strengthening ideological and 

political education in courses, and enhancing industry-academia collaboration, the curriculum improves 

students’ awareness of industry demands and their adaptability. As shown in Table 1, this curriculum 

design not only promotes seamless integration between disciplines but also provides students with a 

multi-dimensional and structured learning pathway. 

 

Table 1. Course Group Construction Modules 

General Education 

Courses Module: 

Python Programming, Internet of Things (IoT) Technology, Digital 

Foundations, etc. 

Foundational Discipline 

Courses Module:  

Fundamentals of Civil Engineering, Fundamentals of Computer Science, 

Introduction to Artificial Intelligence, etc. 

Core Professional 

Courses Module:  

BIM Modeling, Introduction to Smart Construction, Applications of 

Artificial Intelligence, Automation Control, etc. 

Innovation-Driven 

Practice Courses 

Module: 

Project-Based Learning, Interdisciplinary Team Projects, 

Industry-Academia Collaboration Practices, etc. 

 

5.3 Innovative Teaching Methods and Practical Learning Models 

In terms of teaching methods, the interdisciplinary education model emphasizes a student-centered 

approach, utilizing flipped classrooms, project-based learning, and case-based teaching to enhance 

students’ active learning and innovation capabilities, as shown in Table 2. For example, by forming 

interdisciplinary teams to carry out comprehensive projects, students can learn knowledge from 

different disciplines through teamwork while developing the ability to solve complex engineering 

problems. Additionally, this study advocates the establishment of virtual simulation laboratories and 

online learning platforms to provide students with flexible, cross-time-and-space practical learning 

support. Regarding practical learning models, the study strengthens industry-academia collaboration by 



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leveraging smart construction pilot projects to establish an “industry-academia-research integrated” 

training base, ensuring that student development is closely aligned with real-world industry needs. 

Through the innovation of these teaching methods and practical learning models, students not only 

improve their ability to apply knowledge but also significantly enhance their communication and 

collaboration skills in interdisciplinary environments. This approach provides strong support for 

cultivating high-quality talents in the field of smart construction. 

 

Table 2. Innovation in Teaching Methods and Practical Learning Models 

Teaching Methods Practical Learning Models Talent Development Goals 

Flipped Classroom 
Virtual Simulation 

Laboratory 

Interdisciplinary Knowledge 

Integration 

Project-Based Learning Online Learning Platform Innovation and Practical Skills 

Case-Based Teaching 
Industry-Academia 

Collaboration Base 

Teamwork and Communication 

Skills 

 

6. Conclusion and Future Prospects 

This study focuses on the construction of an interdisciplinary smart construction course group system 

within the context of new engineering education, proposing an education reform plan centered on 

technological innovation and practice-oriented learning to meet the industry’s urgent demand for 

interdisciplinary talents. By exploring the integration mechanisms of smart construction with computer 

science, artificial intelligence, electronic information, and automation, the study designs a systematic 

course group framework. Through project-based learning, industry-academia collaboration, and virtual 

simulation experiments, the study enhances students’ ability to integrate interdisciplinary knowledge 

and apply innovative practices. The findings indicate that the interdisciplinary course system 

significantly improves students’ ability to integrate knowledge across disciplines, increases 

employment competitiveness by 15%, and enhances industry adaptability by 20%, providing strong 

support for the promotion of interdisciplinary education models in smart construction. Looking ahead, 

as the smart construction industry continues to evolve, the depth and breadth of interdisciplinary 

integration will further expand. Future research should focus on developing dynamic curriculum 

updating mechanisms, improving practical training platforms, and strengthening in-depth collaboration 

between universities and enterprises, particularly in technological innovation and talent cultivation 

within the smart construction field. By establishing a more efficient 

“industry-academia-research-application” collaborative education model, this study aims to drive the 

digital transformation and high-quality development of the construction industry. This research 

provides a reference framework for the future of interdisciplinary education and industry talent 

development, with a lasting impact on the innovation and advancement of smart construction. 



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Funding 

[Fund Projects] Qingdao City University 2024 Annual Educational Teaching Research Project 

“Research on the Construction of Multidisciplinary Interdisciplinary Intelligent Construction 

Professional Course Group System under the Background of New Engineering” (2024005B). 

 

References 

Li, H., Lu, W., & Huang, T. (2019). Rethinking project management and exploring virtual design and 

construction as a potential solution. Construction Management and Economics, 37(9), 499-507. 

Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with digital twin 

information systems. Data-Centric Engineering, 1, e14. https://doi.org/10.1017/dce.2020.16 

Wang, J., Wang, X., Shou, W., & Xu, B. (2020). Integrating BIM and augmented reality for interactive 

architectural visualisation. Construction Innovation, 20(4), 621-642. 

Zhang, J., Teizer, J., Lee, J. K., Eastman, C. M., & Venugopal, M. (2015). Building Information 

Modeling (BIM) and Safety: Automatic Safety Checking of Construction Models and Schedules. 

Automation in Construction, 29, 183-195. https://doi.org/10.1016/j.autcon.2012.05.006 

 

 

https://doi.org/10.1017/dce.2020.16
https://doi.org/10.1016/j.autcon.2012.05.006

