AI-Driven “Spintronics Industry + Law” Curriculum Reconstruction

Jinling Liu; Bingbin Liu; Benhao Huang

doi:10.26689/jcer.v9i10.12679

Jinling Liu Guangzhou Southern College Business School, Guangzhou 510970, Guangdong, China
Bingbin Liu School of Computer Science and Engineering, Central South University, Changsha 410083, Hunan, China
Benhao Huang Guangzhou Southern College Business School, Guangzhou 510970, Guangdong, China

DOI: https://doi.org/10.26689/jcer.v9i10.12679

Keywords: Artificial intelligence education, Spintronics, Neuromorphic computing, Intellectual property, Industry-education integration

Abstract

With the cross-fertilization of artificial intelligence (AI) technology and spintronics, the traditional AI teaching system has revealed its limitations in terms of industrial adaptability and interdisciplinary integration. In order to cope with this challenge, this study takes Introduction to Artificial Intelligence as the basis, and proposes a conceptual framework of “technical-legal” double helix teaching model, aiming at reconstructing the existing curriculum through three-dimensional teaching design innovation: (1) In the technical level, adding the cutting-edge topic of “Spintronics and Neuromorphic Computing,” through simulation and literature study, students are guided to explore the principle of brain-like computation based on STT-MRAM; (2) at the legal level, the teaching paradigm of “integrating the awareness of legal compliance into technological research and development” is constructed, and it is planned to develop a library of legal science and technology seminars containing cases such as analysis of intelligent contracts; (3) at the practical level, the establishment of an “industry-academia-research” program is explored and improve the comprehensive practical ability of students by simulating the cooperation projects between schools and enterprises. The expected goal of this teaching reform program is to enhance students’ technological innovation thinking and legal risk prevention awareness, and to provide a teaching reform idea with reference value for exploring the cultivation path of “ AI + Law ” composite talents.

References

Walter Y, 2024, Embracing the Future of Artificial Intelligence in the Classroom: the Relevance of AI Literacy, Prompt Engineering, and Critical Thinking in Modern Education. International Journal of Educational Technology in Higher Education, 21: 15.

Schuman CD, Kulkarni SR, Parsa M, et al., 2022, Opportunities for Neuromorphic Computing Algorithms and Applications. Nature Computational Science, 2: 10–19.

Usher M, Barak M, 2024, Unpacking the Role of AI Ethics Online Education for Science and Engineering Students. International Journal of STEM Education, 11: 35.

Roy K, Wang C, Roy S, et al., 2024, Spintronic Neural Systems. Nature Reviews Electrical Engineering, 1(11): 714–729.

Jaiswal K, Kuzminykh I, Modgil S, 2025, Understanding the Skills Gap Between Higher Education and Industry in the UK in Artificial Intelligence Sector. Industry and Higher Education, 39(2): 234–246.

State Council, 2017, Development Plan for New-Generation Artificial Intelligence, Guo Fa [2017] No. 35.

Yang X, 2019, Accelerated Move for AI Education in China. ECNU Review of Education, 2(3): 347–352.