Guangdong First-Class Course Foundation Engineering: Immersive Teaching with "AI Learning Companion" and the Practice of Integrating Science, Industry and Education for Talent Cultivation
From the Editor
A new round of technological revolution and industrial transformation represented by artificial intelligence is profoundly reshaping the global innovation landscape and industrial ecosystem, posing unprecedented systemic challenges to the connotation, models and goals of talent cultivation in universities. University education must proactively adapt to this transformation. It should not only impart knowledge but also focus on fostering students’ innovative spirit, critical thinking, advanced problem-solving abilities and lifelong learning literacy. Relying on the construction of emerging engineering education, the College of Engineering has launched a special series on "Frontiers in Teaching".
Featured in this issue is Foundation Engineering, a provincial first-class undergraduate course in Guangdong Province, together with its related course Tunnel Engineering. Led by Professor Wang Yanning from the Department of Civil and Smart Construction Engineering, College of Engineering, these two courses have supported a series of teaching projects, including the Guangdong Provincial Practice Teaching Base for the Integration of Science, Industry and Education in Intelligent Construction of Underground Space, the Joint Laboratory for Intelligent Operation and Maintenance of Infrastructure, and the development of a new edition of Tunnel Engineering integrated with AI literacy. In recent years, the team has deeply integrated artificial intelligence (AI) technology into the core of the courses, established a seamless talent training pathway connecting undergraduate and postgraduate education, and achieved remarkable and fruitful results in AI empowerment, interdisciplinary integration and solving complex engineering problems.
1. Following the Digital-Intelligent Trend: Curriculum Restructuring from "Knowledge Imparting" to "AI Empowerment"
Faced with the intelligent transformation of the civil engineering industry, Professor Wang Yanning’s team has keenly upgraded its educational philosophy, vigorously promoted the "AI +" reform of higher education teaching, and built a professional curriculum group matrix of "AI + Geotechnical Engineering".
Customized "AI Learning Companion" for Immersive Teaching
The team took the lead in innovatively building a matrix-based AI intelligent learning companion in China. "Brother Gang, the Geotechnical Master" was introduced into Foundation Engineering and related courses, while "Brother Ming, the Tunnel Master" was applied in Tunnel Engineering. By implementing the thematic teaching method and role-based immersive interaction, students conduct scheme comparison and data deduction with the assistance of the two "senior brothers", greatly improving their engineering decision-making and independent learning abilities.
Reconstructing the Curriculum Knowledge System
The pure "information transmission" function of traditional classrooms has been removed, and the courses have been transformed into challenging tasks integrated with AI literacy improvement. Students not only master solid traditional theories but also develop emerging engineering literacy in using intelligent tools to solve pain points in geotechnical engineering.
Innovative Teaching Methods
Guided by interest and supported by PBL (Project-Based Learning), the teaching model advocates learning by doing and doing by learning, which greatly stimulates students’ initiative and learning outcomes. The total number of classroom activities for the two core courses has reached nearly 30,000, with 160,000 visits.
2. Focusing on Challenging Projects: Authentic Practice to Solve Real-World Engineering Problems
The courses have completely broken the traditional passive lecturing mode. Taking major practical engineering projects as the starting point, a closed-loop system of integrating science, industry and education featuring "joint goal-setting, co-management of process and collaborative evaluation of outcomes" has been established. Professor Wang Yanning’s team has transformed several successfully implemented cutting-edge research projects into "inquiry-based learning tasks" in the classroom, enabling students to tackle real problems with real projects.
Relying on Major Engineering Projects to Address Industrial Pain Points
Real-world implemented projects such as "Development of Digital Twin Key Technologies for the Expansion of the Existing Shantou Railway Station Building" and "Development of Key Technologies for Intelligent Shield Tunneling in Water-Rich Karst Strata" have been introduced into the classroom, guiding students to conduct design of foundation and underground engineering in complex environments.
Research Feeding Teaching to Refine Advanced Thinking
Drawing on the team’s profound expertise in "Deterioration Mechanism of Granite Residual Soil Slope Stability Under Coastal Extreme Climates and Microbial (MICP) Reinforcement Technology" and "Multi-Field Coupling Disaster Mechanism and Intelligent Control of Shield Tunnel Segment Floating in Water-Rich Karst Strata", students are guided to explore special soil treatment and multi-field coupling disaster mechanisms, greatly inspiring their innovative thinking and practical ability to solve complex engineering problems.
3. Seamless Talent Cultivation: A "Cultivation Matrix" for Undergraduate, Postgraduate and Doctoral Collaborative Innovation
Relying on platforms such as the Guangdong Provincial Engineering Technology Research Center for Smart Construction and Operation & Maintenance of Coastal Urban Facilities, the team has established a three-level research echelon of "supervisor – postgraduate – undergraduate", forming a virtuous cycle where "junior students lay foundations and senior students take leading roles".
High-Level Academic and Patent Outputs
Driven by both teaching research and scientific research in recent years, the team has obtained a large number of high-value intellectual property rights and authorized multiple national invention patents. Under the supervision of the team, students have published dozens of high-level papers indexed by SCI/EI (many of which involve or are led by undergraduates).
Outstanding Innovation Achievements by Undergraduates
Making full use of research-based teaching to expand students’ academic interests, undergraduates have not only deeply participated in many national and provincial college students’ innovation and entrepreneurship training programs but also achieved remarkable results in discipline competitions, winning top awards such as the First Prize in the National College Student BIM-CIM Innovation Competition.
In-Depth Interdisciplinary Teaching and Research Integration
The team focuses on guiding postgraduates to conduct cutting-edge interdisciplinary research on engineering AI literacy and explore interdisciplinary evaluation systems for engineering AI literacy, producing highly forward-looking tutorial interaction outcomes.
4. Diversified Assessment and Self-Driven Growth: A Generative Guidance-Oriented OBE Closed Loop
In terms of teaching quality monitoring, the courses have constructed a continuous improvement closed loop based on the OBE (Outcomes-Based Education) concept and implemented a "three-dimension and nine-index" evaluation model.
Focusing on Process and Dynamic Feedback
A "three-stage and four-assessment" mechanism including daily electronic logs, module node assessments and three-party defense meetings is implemented through an intelligent platform. With interactive data from AI learning companions, the system can adjust chapter difficulty in real time to realize personalized guidance.
Reinventing the Assessment System
The single mode of traditional written examinations has been completely broken, and a diversified assessment system including AI-assisted design, comprehensive case analysis, data thinking training and ethical reflection has been introduced. This promotes continuous improvement and overall progress of students, achieving a complete PDCA cycle in course learning.
Conclusion
With its forward-looking "AI +" teaching reform design, rigorous engineering practice content and seamless undergraduate-postgraduate-doctoral training model, the Foundation Engineering course has injected new vitality into traditional engineering majors. Riding the tide of educational digitalization, this course has not only cultivated a large number of new-era interdisciplinary top talents proficient in engineering and AI application but also provided a valuable "Shantou University Model" for the reform and high-quality development of emerging engineering courses in universities.
