On May 27, the final round of the 8th Shantou University Young Faculty Teaching Competition came to a successful conclusion. Faculty members Wang Chuanlin, Wang Dongliang, and Lin Meiai from the College of Engineering were awarded Second Prize, while Zhang Yue and Zhang Jiayang received Third Prize.
The competition was jointly organized by the Center for Faculty Development and Educational Assessment, the University Trade Union, and the Academic Affairs Office. It was divided into two groups: Group 1 (Humanities and Social Sciences, and Ideological and Political Theory Courses) and Group 2 (Natural Sciences, Engineering, and Medical Sciences). Each group featured one Special Prize, two First Prizes, three Second Prizes, and nine Third Prizes. These achievements fully demonstrate the solid teaching competence, innovative educational concepts, and strong sense of educational responsibility of the College of Engineering's young faculty members.
These awards represent the dedicated efforts of the College's faculty in their commitment to teaching excellence and exploratory innovation. They are not only a strong affirmation of the individual awardees' teaching abilities and innovative spirit but also a reflection of the College's long-standing commitment to continuously advancing classroom reform and enhancing the quality of talent cultivation. The College will continue to foster a supportive environment that values teaching and encourages innovation, deepen teaching reforms, elevate educational standards, and cultivate more outstanding professionals for society.
Wang Chuanlin
Structural Mechanics I
Second Prize Winner
Structural Mechanics I, taught by Professor Wang Chuanlin, is one of the core foundational courses in the Civil Engineering program, serving as a crucial bridge within the overall talent cultivation system. Built upon the foundations of Advanced Mathematics, Theoretical Mechanics, and Mechanics of Materials, the course helps students systematically master the basic concepts, principles, and methods of framed structures, with a focus on the strength, stiffness, and stability of planar framed structures. Students gain an understanding of the mechanical behavior and compositional rules of various structures, and develop skills in structural analysis and computation, laying a solid mechanical foundation for subsequent specialized courses, structural design, and scientific research.
The course deeply integrates AI technology into teaching resources, classroom instruction, and project-based practice. Professor Wang has developed a structural mechanics intelligent agent and a visual mini-program that support personalized learning and 24/7 Q&A services, encouraging students to critically evaluate AI-generated results and gradually develop the ability to solve engineering problems through human-machine collaboration. The course employs a blended online-offline teaching model, incorporates real-world engineering cases to naturally embed patriotism and craftsmanship spirit, and utilizes the CIPP model for quantitative evaluation of ideological and political education outcomes. The practical component aligns with the National College Structural Design Competition, guiding students through the entire process from design to loading in accordance with the CDIO framework, with a focus on enhancing engineering practice capabilities, innovative awareness, and research exploration skills.
"Exchanges with outstanding young faculty colleagues have given me a profound appreciation for the vitality and warmth of teaching innovation," said Professor Wang Chuanlin. He indicated that he will continue to adhere to an outcome-oriented approach and leverage learning analytics data to drive precise instruction, further deepen the integration of AI into the curriculum, and refine blended teaching and competition-based practical components. While strengthening students' foundational mechanics knowledge, he aims to cultivate critical thinking and human-machine collaboration skills, strive to enhance the educational effectiveness of the classroom, and contribute to the cultivation of outstanding engineers for the new era.
Wang Dongliang
Artificial Intelligence and Robotics
Second Prize Winner
Artificial Intelligence and Robotics, taught by Professor Wang Dongliang, is a core compulsory course in the field of Electronic Information Engineering, serving as a vital bridge between foundational theories and cutting-edge applications within the talent cultivation system. The course systematically traces the grand evolutionary trajectory of robotics from traditional mechanical control to intelligent systems, with a focus on key technologies including computer vision, machine hearing, speech recognition and synthesis, robot planning, and motion control. It helps students build a comprehensive and forward-looking knowledge framework for intelligent robotics, laying a solid foundation for further advanced studies.
Integrating cutting-edge theories with industry practices, the course leverages the Rain Classroom smart teaching platform to conduct in-depth analyses of disruptive application cases such as Baidu's Apollo autonomous driving, intelligent surgical robots, and personalized rehabilitation robots. These cases vividly demonstrate how large AI models empower robots to achieve seamless closed-loop perception, decision-making, and execution. With keen contemporary insight, Professor Wang brings the latest waves of artificial intelligence into the classroom, designing a tripartite teaching model of "theoretical lectures — case studies — project practice." The course features multiple hands-on projects including handwritten digit recognition, face detection, and intelligent robot motion control simulation, guiding students to apply classroom knowledge to real or simulated engineering problems, thus achieving a complete cycle from knowledge acquisition to capability output. By guiding students to investigate global mainstream autonomous driving technology pathways and envision the revolutionary transformations of "large models + robotics" in future education, healthcare, and daily life, the course rigorously cultivates students' critical thinking, interdisciplinary integration capabilities, and engineering innovation competencies for leading the future.
Professor Wang Dongliang noted that participating in the Young Faculty Teaching Competition was a valuable opportunity for teaching reflection and professional growth. Through exchanges with outstanding peers, he gained a deeper understanding that "student-centered" instructional design requires more cutting-edge content support and more refined classroom organization. In the future, he will continue to deepen the integration of AI and robotics courses, strengthen case-based teaching and project-driven approaches, and strive to cultivate high-quality engineering talent capable of adapting to the demands of the intelligent era.
Lin Meiai
Introduction to Biomechanics
Second Prize Winner
Introduction to Biomechanics, taught by Professor Lin Meiai, is a core bilingual compulsory course in the Biomedical Engineering program. Centered on mechanical analysis and integrating physiology, medicine, and biology, the course systematically explores the intrinsic relationships among human motion, structure, and mechanical environments. Aligned with the "New Engineering" talent cultivation goals, it strengthens the intersection of engineering sciences and life sciences, helping students build professional competence in applying engineering technologies to serve human health. The curriculum covers statics, the musculoskeletal system, dynamics, and mechanics of materials, with each chapter incorporating discussions on the latest academic papers. As students solidify their foundational theories, they are exposed to real-world frontier scientific questions. This research-oriented learning approach not only enhances the depth and rigor of the course but also stimulates students' innovative awareness.
Professor Lin Meiai closely follows the development of AI technologies, deeply embedding AI tools throughout the entire teaching process. Leveraging the Chaoxing Learning Platform, the course has established a knowledge graph and an intelligent teaching assistant, integrating rich resources such as micro-lecture videos, lecture notes, online quizzes, and academic paper-based reflection questions. These tools help students accurately identify their weaknesses while also improving teaching preparation efficiency. Classroom instruction combines case-based discussions and interactive experiments, encouraging students to use AI for information synthesis, literature analysis, and PowerPoint creation and optimization. Equal emphasis is placed on cultivating students' ability to verify, revise, and re-optimize AI-generated content, gradually fostering healthy human-machine collaborative learning habits. The course also incorporates rich ideological and political elements — from the inspiring story of "Father of Biomechanics" Yuan-Cheng Fung, to the perseverance of Olympic athletes and the dedication of astronauts, to the resilience of disabled artists — guiding students to cultivate a sense of patriotic commitment to serving the nation through science and technology, develop a rigorous and truth-seeking scientific spirit, and foster a sense of care for others and responsibility to society.
Professor Lin Meiai stated that participating in the teaching competition provided a valuable opportunity for peer exchange, from which she gained many insights from exceptional educators. In the future, she will continue to center her work on student growth, continuously iterate course content and teaching strategies, and strive to cultivate more high-quality biomedical engineering talent capable of adapting to the AI era.
Zhang Yue
Analog Circuit Design
Third Prize Winner
Analog Circuit Design, taught by Professor Zhang Yue, is a core course in Electronic Information majors. The course systematically covers power amplifiers, amplifier circuit design, and other core topics, with a focus on cultivating students' rigorous engineering thinking, circuit analysis skills, parameter calculation abilities, and capacity to apply analog electronics to solve practical system design problems. While consolidating foundational knowledge of device characteristics and circuit topologies, the course emphasizes students' understanding of the inherent engineering trade-offs among "energy conversion — efficiency optimization — distortion control." It guides students to appreciate the trade-offs in analog circuit design from multiple perspectives — device physics, circuit behavior, and system specifications — laying a foundation for subsequent specialized courses such as RF circuits and communication systems.
Professor Zhang Yue selected "Power Amplifiers" as his teaching topic, drawing inspiration from the novel The Three-Body Problem, in which the "Red Coast Base" sends signals to the universe using high-power radio transmitters. This engaging introduction leads into the challenges of efficiency and distortion in wireless communication system power amplifiers, progressively transitioning to the definition, classification, and core design objectives of power amplifiers. The lecture sequentially covers the conduction characteristics of Class A, Class B, and Class AB amplifiers, the push-pull operating principle of Class B complementary symmetry circuits, and the mechanisms and suppression methods of crossover distortion. The instructional design emphasizes a progressive journey from ideal characteristics to engineering reality, deconstructing the quantitative relationships among output power, power dissipation, supply power, and efficiency through graphical analysis, while incorporating AI interactive scenarios to help students understand the fundamental design dilemma that "high efficiency and low distortion cannot be achieved simultaneously" in power amplifiers. The course balances rigorous theoretical derivation with practical engineering feasibility, guiding students to appreciate important engineering principles such as "standby power versus efficiency trade-offs" and "bias design versus distortion suppression" through concrete problems, thereby effectively enhancing students' system-level analog circuit design capabilities and engineering problem-solving skills.
Professor Zhang Yue used the competition as an opportunity for in-depth teaching reflection. He believes that analog circuit design instruction should not stop at circuit structure explanations and formula derivations, but should help students build clear cognitive bridges among device physical characteristics, circuit behavioral performance, and system specification requirements. Therefore, classroom design should place greater emphasis on "problem-chain guidance + engineering history immersion + interdisciplinary contexts," enabling students to appreciate the importance of analog circuits in technology and thereby stimulating learning interest and exploratory drive. Through this competition, he further optimized his teaching framework in terms of teaching objective alignment, engineering case integration, interactive activity design, and adaptation to student cognitive patterns. In the future, grounded in the needs of New Engineering talent development, he will maintain the rigor of circuit theory while deepening the integrated perspective of "device — circuit — system," enhancing the classroom's engineering relevance, inspiration, and interactivity, and effectively strengthening students' engineering thinking and innovative practical abilities.
Zhang Jiayang
Fundamentals of Mechanical Manufacturing
Third Prize Winner
Fundamentals of Mechanical Manufacturing, taught by Professor Zhang Jiayang, is a core senior-year course in the Mechanical Design, Manufacturing, and Automation program. Centered on subtractive manufacturing processes, primarily turning operations, the course systematically covers the principles of cutting, the conceptual framework of process flows, methods for achieving machining precision, and the coordination of process equipment to minimize machining errors. In conjunction with supporting CDIO Level-2 practical projects, the course focuses on cultivating students' quantitative analysis capabilities for achieving part machining precision and their ability to design engineering solutions, laying a solid foundation for subsequent specialized courses and engineering practice.
Professor Zhang Jiayang selected "Analysis of Factors Affecting Machining Precision" as his teaching topic, beginning with the basic definitions of machining precision and machining error. Through a progressive pedagogical logic of "concept differentiation — classification analysis — case guidance — technology outlook," he systematically deconstructs the conceptual system of machining precision, error classification methods, sources of process system errors, and their mechanisms of influence. The course uses "error-sensitive direction" as a key pedagogical breakthrough, employing comparative demonstrations of typical turning and grinding cases to guide students in understanding the relationship between the direction of original errors and the direction of process dimensions, and to master methods for determining conditions that maximize error impact.
"This competition served as both a crucible for teaching philosophy and an accelerator for young faculty growth," said Professor Zhang Jiayang. Through introspection and reflection during the competition process, as well as exchanges with other outstanding educators, he gained a profound appreciation that "student-centered" instructional design requires more refined classroom implementation strategies. In his teaching reflection, he noted that the instructional challenge of Fundamentals of Mechanical Manufacturing lies in the translation of abstract concepts into concrete machining scenarios — whether students can flexibly apply theoretical knowledge such as "error-sensitive direction" to the complex combinations of different machine tools, different cutting tools, and different workpieces directly determines their depth of understanding of the essence of precision control. In the future, he will further strengthen the three-dimensional integration of "theory — case study — practice," attempt to combine AI and virtual simulation technologies to build interactive process system error models, enabling students to intuitively perceive the processes of error generation and propagation in dynamic visual scenarios, thereby effectively enhancing their comprehensive ability to solve complex engineering problems.
