0 Introduction
In recent years, the development of artificial intelligence has attracted widespread attention from the scientific and business communities. China has also positioned the development of the AI industry as an important support direction. The “Notice of the State Council on Printing and Distributing the New Generation Artificial Intelligence Development Plan” clearly states that the development of AI technology should be regarded as an important strategic plan for national development and an important measure to promote innovative development in China [1]. In 2018, the Ministry of Education released the “Action Plan for AI Innovation in Higher Education” (hereinafter referred to as the “Plan”), which includes three categories, 18 key tasks, and three detailed columns. The three categories are optimizing the technological innovation system in the field of AI in universities, improving the talent cultivation system in the AI field, and promoting the transformation and demonstration application of scientific and technological achievements in AI in universities. The Plan also emphasizes improving the talent cultivation system in the AI field as a key task, guiding universities to further enhance their capabilities in technological innovation, talent cultivation, and serving national needs in the AI field [2].
In March 2019, the Ministry of Education approved 35 universities to add undergraduate programs in AI, marking the first large-scale approval of AI undergraduate programs in China. In March 2020, 180 universities passed the record approval for new AI programs in the list announced by the Ministry of Education. Among them, the AI program proposed by China University of Mining and Technology was also included in the list of newly added programs. Currently, encouraged by national policies, universities are actively establishing AI programs, which is very necessary to fill the gap in AI talent in China. However, how to deepen the connotation of AI, explore new models of discipline construction and talent cultivation through deep integration, and focus on improving the level of talent cultivation in the AI field still requires further exploration.
To build a good AI program under the context of “New Engineering”, the following issues need to be considered when designing the AI program construction plan: talent cultivation goals and graduation requirements, professional direction settings, curriculum design, and practical ability cultivation [3]. To cultivate innovative talents with a “thick foundation and strong ability”, the AI program at China University of Mining and Technology is mainly planned and set from the following three aspects: a “thick foundation” theoretical course system, a “strong ability” practical course system, and a teaching quality assurance system that integrates “competition, innovation, and training”.
1 Construction of the “Thick Foundation” Theoretical Course System
In the AI program cultivation plan at China University of Mining and Technology, the theoretical course system mainly constructs three course groups: Mathematics and Electronic Fundamentals Course Group, Computer Science and Technology Course Group, and AI Course Group, as shown in Figure 1.
The Mathematics and Electronic Fundamentals Course Group supports the Computer Science and Technology Course Group and the AI Course Group. This course group mainly includes the following foundational courses: “Engineering Mathematics Analysis”, “Linear Algebra”, “Probability Theory and Mathematical Statistics”, “Engineering Mathematics”, “Modern Electronic Technology”, “Signals and Systems”, etc. To address the high mathematical foundation requirements for the AI program, the course “Engineering Mathematics Analysis” is introduced to replace the “Advanced Mathematics” course, laying a solid mathematical foundation for students to learn subsequent AI program courses. A solid mathematical foundation not only facilitates students’ understanding of abstract algorithms in AI but also provides inspiration for further algorithm innovation [4].
The Computer Science and Technology Course Group mainly includes the following foundational courses: “Advanced Language Programming”, “Discrete Mathematics”, “Data Structures”, “Computer Organization Principles”, and elective foundational courses such as “Operating Systems”, “Computer Networks”, “Algorithm Design and Analysis”, etc. This course group includes foundational courses for the Computer Science and Technology major, primarily cultivating students’ computational thinking and programming abilities, while also providing essential course foundations for those pursuing graduate studies in related computer fields.
The AI Course Group mainly includes professional courses such as “Introduction to AI”, “Big Data Technology”, “Cloud Computing Technology”, “Principles of AI”, “Optimization Theory and Methods”, “Information Acquisition Technology”, “Basics of Machine Learning”, “Neural Networks and Deep Learning”, “Image Processing and Visual Perception”, etc. The teaching of the AI Course Group needs to fully utilize the Mathematics and Electronic Fundamentals Course Group and the Computer Science and Technology Course Group to enable students to deeply understand the mathematical derivation processes and efficiency analysis of AI algorithms.
In addition to the aforementioned courses, the AI program also offers a professional elective course module and a cross-professional elective course module. The professional elective course module is used for further in-depth study of professional courses and expansion of professional directions, mainly including courses such as “Embedded System Design and Applications”, “Basics of Data Mining”, “Knowledge Graph”, “Intelligent Robotics”, “Natural Language Processing”, and “Virtual Reality and Augmented Reality”. The cross-professional elective course module allows students to select 2-3 courses from other majors to achieve interdisciplinary integration between the AI major and other fields, providing AI students with opportunities to understand potential interdisciplinary application areas and related challenges [5].
2 Construction of the “Strong Ability” Practical Course System
The AI program cultivation plan constructs a “strong ability” practical course system, as shown in Figure 2, which mainly includes practical courses, innovation and entrepreneurship practices, graduation internships, and graduation design, etc. The six practical courses are evenly distributed from the 2nd to the 6th semester (with two practical courses scheduled in the 5th semester). The “strong ability” practical course system of the AI program completes a progressive system capability cultivation from basic to intermediate to advanced levels, with increasing difficulty, gradually achieving simple AI demonstration systems, AI learning systems with learning abilities, and comprehensive application systems of AI for specific fields.
In the practical course system, the expected goals of each level of practical links are clearly defined, as well as the ability levels that students can achieve upon completing the course. Additionally, efforts are made to establish dependencies between preceding and subsequent practical courses. For example, the “Comprehensive Programming Practice” course is based on “Python Programming Practice” to solve practical application problems using Python. The “AI Tools and Platforms Practice” can provide necessary platform support for “Machine Learning Application Practice” and “AI Comprehensive Application Practice”.
Moreover, in response to the rapid updates in AI technology, China University of Mining and Technology plans to hold an annual syllabus seminar, inviting professors from well-known universities engaged in AI education and senior engineers from AI companies to discuss the updates of knowledge and tools in practical courses. At the same time, full-time teachers are encouraged to actively participate in teaching seminars and practical training sessions related to AI, such as training on AI platforms from companies like Baidu, Huawei, and Tencent.
To introduce engineering concepts into practical courses, it is proposed to consider co-designing syllabuses with IT companies for comprehensive practical links, such as “Comprehensive Programming Practice”, “Machine Learning Application Practice”, and “AI Comprehensive Application Practice”. If conditions permit, IT companies can also introduce project development cases for these practical courses, using AI platforms and real-world data provided by companies, allowing students to use the models learned in class to solve such practical problems.
3 Construction of the Teaching Quality Assurance System
To ensure the smooth implementation of the 2020 AI program cultivation plan, China University of Mining and Technology mainly conducts teaching quality assurance work from aspects such as the undergraduate tutor system, innovation projects and extracurricular competition-driven mechanisms, and comprehensive cooperation between schools and enterprises, thus forming a three-dimensional teaching quality assurance system.
Regarding the undergraduate tutor system, implementing the undergraduate tutor system is an important measure to promote the construction of “Double First-Class” and cultivate application-oriented professional technical talents with a certain spirit of innovation [6]. Based on the actual situation of the AI program at China University of Mining and Technology, different types of tutors are arranged each academic year to guide students according to the differences in knowledge structures among students of different grades. In the first year, 20 teachers engaged in AI-related fields are selected every October from the AI program, with each tutor guiding up to three students. They mainly guide students in foundational course learning and help students connect theory with practice to complete the design and development of simple intelligent systems. In the second year, several teachers from the School of Computer Science and Technology who undertake AI-related topics are selected to set up sub-topics based on tutor topics, guiding students to design existing intelligent models and conduct unique applications. In the third year, tutors are invited from “Double First-Class” universities and IT companies to set up special innovative training projects, guiding third-year students in designing models or applications with a certain degree of innovation, thus promoting students’ further education (graduate school or examination) and employment.
In terms of the innovation projects and extracurricular competition-driven mechanism, deepening the reform of innovation and entrepreneurship education in higher education and enhancing students’ innovative qualities are current key focuses of continuous improvement in China’s higher education [7]. To prevent students from falling into the dilemma of pure theoretical learning in AI while being disconnected from practical applications, the teaching emphasizes improving students’ extracurricular practical application abilities, incorporating discipline competitions, innovation projects, and extracurricular training (referred to as “competition, innovation, training”) into the AI cultivation system, and establishing a corresponding teaching quality assurance system. Specific measures include: ① China University of Mining and Technology, based on the school’s actual situation, identifies influential AI discipline competitions and actively applies to include them in the school’s competition guidance directory. Through the competition guidance projects established by the school, a tiered competition system from school-level to provincial and national levels is gradually established, cultivating students’ interest in participating in competitions in a targeted and hierarchical manner, thereby truly prompting students to apply what they have learned and deeply appreciate the importance of learning such courses, cultivating their autonomous learning abilities [8]. ② At this stage, students’ enthusiasm for applying for various innovation projects still needs to be improved, and some students are hesitant to independently undertake the design and development of innovation projects. Therefore, teachers are encouraged to design tiered innovation projects, such as school-level innovation projects close to course applications and provincial and national innovation projects involving comprehensive application capabilities. Additionally, further promoting the importance of innovation projects in the context of further education and job interviews to students. ③ Fully leveraging the roles of the AI club and robotics club in the school, regularly organizing basic training and comprehensive training aimed at competitions, cultivating students’ awareness of AI thinking, and encouraging students to proactively master the ability to discover potential knowledge in data using AI models.
In terms of comprehensive cooperation between schools and enterprises, employment-oriented education is more conducive to breaking communication barriers between society and students, leveraging each other’s resource advantages, and addressing the issues of employment difficulties and low employment quality [9]. In December 2019, China University of Mining and Technology signed a strategic cooperation agreement with Huawei, which stipulates that the main courses in the 2020 version of the AI program cultivation plan will carry out comprehensive cooperation with Huawei ICT Academy, utilizing Huawei’s cloud resources and Ascend processor architecture to incorporate the ModelArts one-stop AI development platform, MindSpore open-source AI computing framework, and development platforms based on Ascend AI processors into the curriculum of the 2020 version of the cultivation plan. Currently, courses such as “Python Programming Practice”, “Big Data Technology”, “Cloud Computing Technology”, “Basics of Machine Learning”, “Neural Networks and Deep Learning”, and “Image Processing and Visual Perception” from the cultivation plan have been included in Huawei’s “Intelligent Base” program cooperation scheme. Huawei’s “Intelligent Base” program can provide essential cloud resources and hardware and software development platforms for each course, and for some courses, a joint teaching talent cultivation model between schools and enterprises is adopted to fully cultivate and enhance students’ comprehensive practical abilities [10]. Future cooperation models will also be expanded in areas such as revising the teaching syllabus for experimental courses, discipline competitions, and research projects.
After nearly three years of teaching practice, supervisory experts and employers generally report that students in the AI program have a solid theoretical foundation and strong practical abilities, capable of solving practical problems in the AI field. School-level undergraduate teaching supervisory experts believe that the AI program cultivation plan emphasizes both theory and practice, focusing on core issues in the AI field, and the students cultivated have strong comprehensive qualities. Through the aforementioned teaching quality assurance measures, students’ practical abilities have greatly improved. Currently, most students have participated in university innovation training projects guided by tutors, and some students have participated in national-level innovation competitions, such as the National College Student Intelligent Car Competition, China College Student Computer Design Competition, “China Software Cup” College Student Software Design Competition, and China Engineering Robot Competition, winning multiple national first and second prizes. Feedback from the teaching of courses under Huawei’s “Intelligent Base” program, as well as participation in extracurricular competitions and training organized by companies like Baidu, has led to the recognition of students’ comprehensive abilities by employers such as Huawei and Baidu, laying a solid foundation for graduates’ employment.
4 Conclusion
The AI program cultivation plan at China University of Mining and Technology has been implemented starting from the 2020 cohort of AI students. Through the construction of a “thick foundation” theoretical course system, students have laid a solid mathematical foundation, developed good computational thinking and programming abilities, and gained a deep understanding of the principles and applications of AI, possessing a certain degree of intelligent thinking. Through the construction of a “strong ability” practical course system, students have received progressive system capability cultivation from basic to intermediate to advanced levels, enabling them to independently complete AI application systems for specific fields. Through the construction of a teaching quality assurance system, students can complete various levels of innovation projects and competitions under the guidance of undergraduate tutors, effectively enhancing their practical innovation abilities through enterprise training. The results of nearly three years of practice indicate that the AI program cultivation plan formulated by China University of Mining and Technology is effective and beneficial for cultivating innovative talents.