In the context of global economic transformation and rapid technological advancement, new quality productivity has emerged as a core engine driving high-quality socio-economic development. Cultivating and developing new quality productivity urgently requires “new quality talents” with innovative thinking and digital literacy. Exploring the deep integration path of new quality productivity concepts and digital-intelligent technologies to comprehensively enhance the quality and learning efficiency of higher education talent cultivation has become a critical theoretical and practical issue in current educational reform and innovation.
With the rapid development of artificial intelligence (AI) technology, its application in the education sector is increasingly widespread, bringing new opportunities and challenges to teaching reform. In terms of teaching content optimization, AI can deeply analyze existing teaching resources, assist teachers in systematically planning course content, creating teaching materials, and improving teaching efficiency. Regarding teaching methods reform, AI can generate customized learning paths and resources based on students’ learning situations and interest preferences, promoting autonomous and deep learning. Additionally, AI can accurately assess students’ learning outcomes through data analysis, providing a strong basis for improving teaching models.
Courses related to new energy vehicle design involve multiple interdisciplinary fields such as mechanical engineering, automation, electrical engineering, computer science, and materials science. These courses often face issues like strong interdisciplinary nature, slow updates in teaching content,单一 teaching methods, and片面 assessment approaches. Therefore, this article integrates digital resource construction and smart course platform development, implementing a SPOC (Small Private Online Course) blended teaching model to explore a new paradigm for smart course construction in new energy vehicle design under the perspective of new quality productivity.
The overall architecture of the new paradigm for smart course construction in new energy vehicle design, aimed at cultivating new quality talents, primarily reflects digitalization, intelligence, precision, and personalization. This is achieved by enriching teaching resources through digital means, enabling precise推送 of teaching content and personalized learning path design for students, leveraging intelligent technology for teacher-student-machine ternary interactive teaching design, constructing a diversified assessment system, and comprehensively enhancing students’ innovative abilities and digital literacy to cultivate innovative constant talents with technical characteristics of the digital-intelligent era.
Based on knowledge graphs and modular design, digital teaching resources are integrated. Combining graduation requirements and course objectives, the teaching content is divided into basic theory modules, design modules, and cutting-edge technology modules. Using large language models to deeply mine and analyze existing teaching resources, keywords are extracted and themes are built, identifying key information in teaching resources and the relationships between knowledge points. The extracted knowledge points and structured information are integrated as nodes of the knowledge graph, clarifying logical relationships between knowledge points to form tree or network structures, ensuring the knowledge graph reflects both the independence and systematicity of knowledge points, and demonstrating the前沿, practicality, and systematicity of course content.
The smart teaching platform for the course is built based on “AI + Wisdom Tree”. According to the constructed course knowledge graph, the core knowledge point matrix of the course is organized, short videos of core knowledge points are recorded, and online resource construction for the course is carried out relying on the Wisdom Tree online teaching platform for students’ pre-class preview and post-class review; a diversified exercise library is built based on AI to assess students’ mastery of core knowledge points in the knowledge graph before or after class; large language models are used to empower project-based teaching, generating personalized project topics to meet students’ diverse learning interests and needs, promoting comprehensive student development.
Personalized innovative teaching practices are implemented based on “AI + SPOC”. Based on the SPOC teaching model, the advantages of offline classroom teaching and online course teaching are combined to form an online import-online/offline participation-online detection teaching model. Using AI technology to achieve pre-class optional tasks, in-class online detection, and recording of students’ learning behaviors, real-time mastery of students’ understanding of knowledge points is achieved, providing students with personalized and precise project topic suggestions. Using AI technology to assist teaching promotes communication and feedback between teachers and students, forming a teacher-machine-student ternary interactive scenario, which helps efficiently obtain multidimensional information about students’ learning status.
A diversified process assessment system面向 new quality talent cultivation is improved. Based on AI technology, more comprehensive assessment standards are designed, covering aspects such as students’ online course participation, group project cooperation attitudes, problem-solving, and technology application abilities. Diversified process assessment methods are used to evaluate students’ innovative abilities and digital literacy, such as learning analysis reports, project reports, usual assignments, final exams, online tests, optional tasks, group discussions, etc., ensuring the accuracy and comprehensiveness of evaluation results.
面向 the demand for new quality talents, this article takes the “Electric Vehicle Design Fundamentals” course as an example to design a digital-intelligent integrated smart course construction and teaching reform plan. First, digital teaching resource construction for the course is carried out; second, the constructed smart course platform is applied for precise and personalized teacher-student-machine ternary interactive teaching design; finally, a diversified process assessment system is used to evaluate students’ innovative thinking and digital literacy.
Digital teaching resource construction involves setting course objectives based on the professional training plan, clarifying the abilities students should possess after learning the “Electric Vehicle Design Fundamentals” course. Using large language models to identify key information and construct core modules from teaching resources such as course syllabi, digital textbooks, and past exam questions, and combining the development trends of the new energy vehicle industry, the course content is decomposed into four teaching modules: battery, motor, electronic control, and vehicle. The content of each teaching module is interconnected through internal relationships, forming a networked knowledge graph. Based on the knowledge graph, core knowledge points such as performance indicators of power batteries, operating characteristics of motors, and energy management strategy design are提炼, and core knowledge point videos are recorded for students to preview or review before and after class. Each teaching module includes multiple core knowledge point videos, such as basic concept introductions, working principle animation demonstrations, and typical component structure analyses.
Online and offline blended smart course platform construction involves teachers relying on the “AI + Wisdom Tree” online education platform for smart course construction, uploading teaching PPTs, knowledge graphs, core knowledge point videos for electric vehicle design fundamentals (required viewing), cutting-edge technology videos for electric vehicles (optional viewing), post-class extended literature, exercise libraries, project materials, and other digital teaching resources. At the same time, teachers need to conduct online teaching and teaching management through this platform, including releasing course tasks (optional and required), initiating group discussions, organizing students to personalizedly choose design topics, and conducting chapter online tests. Students use this platform for online learning, watching video courses, participating in online discussions, completing assignments and tests, etc. Based on the student learning behavior data provided by the smart course platform, teachers can timely understand students’ learning interests and progress, and adjust teaching content at any time.
Intelligent teacher-student-machine ternary interactive teaching design is based on an improved BOPPPS (Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment, Summary) teaching model, dividing the SPOC blended teaching process of the “Electric Vehicle Design Fundamentals” course into three parts: import, interactive teaching, and detection. The framework for the course’s teacher-student-machine ternary interactive teaching design is as follows.
The BOPPPS teaching model emphasizes flexibility and adaptability in teaching, allowing teachers to adjust activities at each stage based on specific teaching content and student learning situations. For example, during the process of students personalizedly selecting project topics, teachers can increase corresponding topic content based on AI learning analysis针对 learning hotspots. The organizational framework for students’ personalized topic selection in the “Electric Vehicle Design Fundamentals” course is as follows.
The construction of a diversified process assessment system encourages students to continuously participate in learning activities throughout the teaching process. Based on AI technology, students’ online learning behavior records can be obtained, helping to stimulate students’ learning motivation. The process assessment for the “Electric Vehicle Design Fundamentals” course is divided into two parts: online assessment (50%) and offline assessment (50%). Online assessment includes pre-class tasks (optional + required) (5%), post-class assignments (5%), class participation (5%), chapter tests (10%), and personalized project reports (25%); offline assessment is the final closed-book exam (50%).
Teachers can collect student behavior data over one teaching cycle through the smart course teaching platform, accurately identifying students’ personalized learning hotspots and error-prone knowledge points. The distribution of online learning hotspots for students in the “Electric Vehicle Design Fundamentals” course is shown in Table 1, and the distribution of error-prone points in chapter tests is shown in Table 2.
| Learning Hotspot | Click Rate (%) | Q&A Volume |
|---|---|---|
| Solid-State Batteries | 95.3 | 92 |
| Blade Batteries | 94.5 | 85 |
| Flat Wire Motors | 91.2 | 87 |
| Fuel Cells | 88.6 | 78 |
| Energy Recovery | 87.9 | 80 |
From Table 1, it can be seen that in the optional cutting-edge module, students’ online learning hotspots are mainly reflected in前沿 research on electric vehicle batteries, motors, etc., such as a click rate of 95.3% for “solid-state batteries” and 87 Q&A sessions for “flat wire motors”.针对 students’ personalized learning hotspots, teachers can make targeted teaching designs in the next teaching cycle, such as adding corresponding electric vehicle前沿 knowledge videos, design topics related to learning hotspots, and group discussions on learning hotspots to improve students’ innovative thinking and mastery of前沿 knowledge.
| Error-Prone Point | Score Rate (%) |
|---|---|
| Calculations Related to Power Batteries | 80.0 |
| Parameter Calculations for Motors | 82.0 |
| Configuration Diagrams of Hybrid Electric Vehicles | 83.0 |
| Parameter Matching of Power Systems | 83.0 |
| Energy Management Strategies | 85.0 |
From Table 2, it can be seen that in the statistics of error-prone points in chapter tests, the correctness rate for calculations related to electric vehicle power batteries is relatively low. Teachers can refine the teaching videos of related knowledge points, presenting them to students in smaller video units to help students quickly locate error-prone points and repeat learning. At the same time, the Wisdom Tree AI teaching assistant can be trained针对 students’ error-prone knowledge points by providing it with selected supporting materials. By using the AI teaching assistant, students can quickly and accurately learn error-prone knowledge points. According to the platform’s data statistics on chapter tests, 17.0% of students have difficulty accurately drawing configuration diagrams of hybrid electric vehicles. Therefore, teachers can increase demonstrations of various hybrid electric vehicle configurations and discussions on their advantages and disadvantages in the classroom discussion环节, pushing customized learning materials to students, thereby improving the assessment score rate for this knowledge point.
In summary, by constructing course knowledge graphs and smart course platforms, the traditional electric vehicle design teaching model can transition to digitalization and intelligence, creating a personalized and precise learning ecology for students. AI-assisted teaching design encourages teachers to continuously learn new knowledge, enhance professional literacy, digital literacy, and teaching design abilities, effectively improving classroom teaching efficiency while continuously stimulating students’ innovative thinking and problem-solving abilities.
Through the construction of a digital teaching resource system, an intelligent teaching platform, and the adoption of efficient teacher-student-machine ternary interactive teaching design, precise推送 of teaching content is achieved, meeting students’ personalized learning needs. At the same time, multidimensional resource integration based on large language models is completed, significantly enhancing teaching effectiveness. AI-empowered knowledge graphs inject innovative momentum into teaching content and methods, helping to quickly update course content based on industry development trends and technological innovations, ensuring the前沿 and timeliness of teaching content. The smart course platform can stimulate students’ learning interest through personalized learning path recommendations and interactive learning tools, helping students improve their innovation capabilities and digital literacy. The new paradigm for smart course construction in new energy vehicle design面向 new quality talents is worthy of widespread application and promotion in the digital-intelligent education transformation of higher education.
In the context of new quality productivity, the development of smart courses for electric vehicle design must address the rapid evolution of China EV technology. For instance, the performance of lithium-ion batteries, a key component in electric vehicles, can be modeled using the following equation for state of charge (SOC): $$ SOC(t) = SOC_0 – \frac{1}{C_n} \int_0^t I(\tau) \, d\tau $$ where \( SOC_0 \) is the initial state of charge, \( C_n \) is the nominal capacity, and \( I(\tau) \) is the current at time \( \tau \). This formula is essential for students to understand energy management in China EV systems.
Similarly, the efficiency of an electric motor, crucial for electric vehicle propulsion, can be expressed as: $$ \eta = \frac{P_{out}}{P_{in}} \times 100\% $$ where \( P_{out} \) is the output power and \( P_{in} \) is the input power. In China EV applications, optimizing this efficiency is vital for extending range and reducing energy consumption.
Furthermore, the aerodynamic drag force affecting electric vehicles can be calculated with: $$ F_d = \frac{1}{2} \rho C_d A v^2 $$ where \( \rho \) is air density, \( C_d \) is the drag coefficient, \( A \) is the frontal area, and \( v \) is the velocity. This equation highlights the importance of design in enhancing the performance of China EV models.
To support personalized learning, AI algorithms can adapt these formulas based on student progress. For example, if a student struggles with battery calculations, the system might provide additional exercises like: $$ \Delta SOC = \frac{I \times \Delta t}{C_n} $$ where \( \Delta SOC \) is the change in state of charge over time interval \( \Delta t \). This reinforces core concepts in electric vehicle design.
In project-based learning, students might use these equations to simulate China EV performance under various conditions, fostering innovation. The integration of such mathematical models into the smart course platform ensures that learners gain hands-on experience with real-world electric vehicle challenges.
Overall, the new paradigm emphasizes continuous improvement through data-driven insights, aligning with the dynamic nature of the electric vehicle industry. As China EV technology advances, the course content can be swiftly updated via AI, maintaining its relevance and effectiveness in cultivating new quality talents.