The Experimental Class of Artificial Intelligence at Nanchang University was established in 2019, leveraging the university's high-quality teaching resources against the backdrop of the National New Generation Artificial Intelligence Development Plan, the Ministry of Education's Higher Education Artificial Intelligence Innovation Action Plan, and the promotion of new engineering disciplines. Adhering to the principles of student-centeredness, individualized instruction, academic excellence, and personalized development, the class upholds the principles of openness, fairness, and impartiality, combining students' college entrance examination scores, comprehensive qualities, and subject-specific strengths. Following the "new engineering" talent cultivation goal of "solid foundation, broad scope, emphasis on moral character, and strong practical application", and addressing the national strategic development needs of next-generation artificial intelligence, the class constructs a new talent cultivation model guided by government and industry, integrating multidisciplinary collaboration, teaching with practice, university-enterprise cooperation, and innovation-driven development. The class aims to cultivate students with a solid grasp of fundamental theories, basic methods, applied engineering, and technologies in artificial intelligence; familiarity with interdisciplinary knowledge in artificial intelligence; and a high degree of social responsibility, scientific literacy, practical ability, innovative ability, systems thinking, industry perspective, and international vision. Graduates are expected to become top-tier innovative talents with the potential to grow into leading engineers, scientists, and entrepreneurs in my country, playing a pivotal role in the development of artificial intelligence disciplines and industries.

    The Experimental Class of Artificial Intelligence adheres to a personalized, small-class, and internationalized training model, implementing a comprehensive management system encompassing mentorship, academy, and credit systems. Within the scope of the first science and engineering department, outstanding teachers with high teaching and research levels and holding associate professor or higher titles or doctoral degrees will be selected to serve as lecturers and form a mentor pool. High-quality faculty, experimental platforms, and a "three-pronged" teaching model (i.e., case analysis, real-world training, and practical testing) from leading companies in the higher education field are introduced, organically combining core courses, self-directed learning, interactive teaching, knowledge application, learning assessment, and innovative applications. Under the guidance of on-campus and industry mentors, students design personalized AI learning plans, participate in research training within research teams, and engage in AI project development at practical training bases, focusing on cultivating students' self-directed, research-oriented, and practical learning abilities. The experimental class fully utilizes high-quality domestic and international teaching resources, inviting domestic and international AI academic and industry experts to give academic reports, holding forums and academic salons, and strengthening student exchange and cooperation both domestically and internationally, as well as between the university and enterprises, striving to cultivate students' international perspectives.

    The first cohort of the 2019 Artificial Intelligence Experimental Class graduated in 2023, delivering impressive results. Among them, 26 students were successfully recommended for postgraduate studies, with 24 recommended to other universities and 2 to this university. Four students directly pursued doctoral degrees, and 22 pursued master's degrees. Fifteen students received recommendations for admission to 985 universities, and seven received recommendations for admission to 211 universities. These students achieved excellent results in various academic competitions and scientific research. According to incomplete statistics, they won over 70 national (international) awards and 39 provincial and university-level awards; published 5 journal papers and 6 conference papers; applied for and were granted 6 patents and 8 computer software copyrights; and led or participated in 8 national or provincial-level undergraduate innovation and entrepreneurship projects.