Against the backdrop of the rapidly developing digital economy, new-generation digital intelligence technologies such as big data, cloud computing, and artificial intelligence are profoundly reshaping industrial structures, development models, and governance systems. The traditional finance sector is also undergoing significant transformation. Corporate demand for finance professionals has shifted from requiring basic financial knowledge to seeking comprehensive abilities in data mining, risk prediction, and decision support. Traditional teaching methods result in knowledge structures and professional skills that struggle to meet the digital transformation needs of modern enterprises.
In response to this trend, universities should actively advance reforms in cultivating new finance talent. By optimizing curriculum systems, strengthening practical innovation, and applying digital intelligence technologies, they can enhance the quality of finance course instruction from multiple perspectives.
Restructuring the professional curriculum system to align with industry development needs is essential. Building a digital intelligence-oriented curriculum is not merely about mechanically combining traditional finance courses with digital technologies; it requires deeply integrating digital intelligence concepts throughout the entire finance talent cultivation process. University instructors should actively develop finance teaching content that meets market demands, shifting the focus from knowledge transmission to capability development, thereby achieving synchronization between talent cultivation and industry needs.
Streamlining traditional course content, merging overlapping material, and appropriately reducing purely finance-focused elements can redirect teaching emphasis toward deep analysis of economic business substance and its consequences. This approach guides students to understand capital flow, value transfer, and the integration of business and information flows. Using real corporate cases and complex business scenarios as vehicles, students can be immersed in intricate business environments for analysis, judgment, and solution design, enhancing their knowledge transfer and problem-solving skills.
Courses such as big data financial analysis, big data and financial decision-making, intelligent taxation, and big data auditing should be introduced to strengthen training in data processing, model application, and system operation. Establishing a dynamic curriculum adjustment mechanism allows for embedding digital intelligence content—like data collection, cleaning, visualization, and intelligent prediction—into traditional finance courses. Rational planning of course sequences, credit hours, and credit structures enables progressive cultivation from foundational to comprehensive, and from theoretical to practical learning.
Introducing digital intelligence teaching methods aids in improving education quality. Innovating teaching and practical models is key to creating interactive classrooms. Instructors should leverage technological tools to empower course instruction, promoting the digital transformation of both teaching and learning. Deploying wireless networks that cover teaching, office, and living scenarios provides stable, high-speed connectivity for high-definition live streaming and remote interactive classes in courses like financial statement analysis, intelligent accounting, and tax training. Upgrading traditional classrooms into smart classrooms equipped with live broadcasting, recording, and interactive functions, along with systems that capture full-process classroom data, enables big data analysis to generate personalized student learning profiles and identify knowledge gaps, offering scientific guidance for cultivating new finance talent.
Implementing intelligent learning systems allows instructors to build hierarchical, categorized teaching resource libraries based on finance course knowledge graphs. These systems dynamically assess each student's knowledge mastery and capability levels through entrance tests and daily learning data, automatically generating and adjusting learning paths to form a complete loop from data collection and learning analysis to precise teaching and personalized assignment distribution. Promoting blended teaching models with预习任务单 oriented toward real corporate financial issues or cases requires students to watch financial report videos and read annual reports via online platforms before submitting financial mind maps or预习疑问. Instructors can analyze预习数据 to identify common difficulties and individual differences in areas like tax compliance planning and consolidated statements, informing the focus of offline instruction. Class time should concentrate on resolving accounting doubts, discussing merger cases, and collaborating on financial modeling projects. Flipped classroom models, where students present预习成果 and instructors facilitate debates, Q&A, and practical exercises, can stimulate independent learning motivation.
Developing digital intelligence practice platforms driven by technology enhances effectiveness. Universities should expand industry-academia collaboration, utilizing specialized practical scenarios to comprehensively improve students' application and innovation capabilities. Co-establishing financial sharing centers or big data analysis platforms with enterprises enables students to complete end-to-end practices—from document processing and intelligent accounting to financial analysis—using desensitized real business data, helping them grasp the operational logic of business-finance integration. Leveraging virtual reality, augmented reality, and digital twin technologies to create highly simulated experimental environments for scenarios like financial budgeting, tax collection, and risk management supports virtual simulation teaching, mitigating real data risks while improving practical skills.
Collaborating with financial management authorities, tax departments, and leading industry firms to establish finance-oriented订单班 or industry academies allows for designing joint training plans based on actual job requirements. Inviting practical experts to teach on campus and arranging for students to participate in corporate projects ensures close alignment between learning content and practical financial roles. Incorporating high-level finance competitions and authoritative industry certifications into teaching plans, with credit recognition for achievements, effectively motivates students and enhances employability. Organizing or participating in digital finance innovation contests, supported by promotional events and expert guidance, encourages students to address real industry issues, integrating competition projects with course design and graduation projects to foster learning through competition and strengthen digital intelligence application skills.
Enhancing instructors' digital intelligence literacy is fundamental to talent cultivation. Universities should recognize that digital intelligence technology represents more than just upgraded teaching methods; transforming instructor mindsets and thinking is crucial to全面提升教师的数智化教学能力. Scientifically designing recruitment plans to prioritize candidates with interdisciplinary backgrounds in accounting, computer science, or data science, or experience in corporate financial digitalization projects, is essential. Recruiting financial big data experts and intelligent tax system architects from leading enterprises to join teaching teams in full-time or part-time roles, and appointing corporate technical骨干 as resident industry mentors to conduct workshops, advise on graduation projects, and co-develop teaching cases, enriches practical expertise.
Regularly organizing skill training and advanced study opportunities for instructors, through线上线下 platforms offering courses in AI technology application, digital teaching tools, and data analysis, along with specialized programs for improving digital literacy and intelligent teaching capabilities, is vital. Encouraging instructors to undertake corporate placements or participate in digital projects, linked to promotion and appointment criteria, helps accumulate practical teaching experience. Including digital teaching outcomes, applied project development, and industry-academia case creation in faculty evaluation and performance metrics, with increased weighting, incentivizes engagement in long-term valuable teaching innovations. Establishing special funds to support interdisciplinary research in finance and digital intelligence technology, and creating awards for contributions to digital teaching, with recognition and monetary rewards for outstanding teams and individuals, fosters a culture of innovation.
In the digital intelligence era, finance professionals are not only liberated from tedious tasks but also play critical roles in decision-making and value creation. Universities should pursue demand-driven educational reforms, constructing teaching systems that dynamically respond to societal needs, continuously improving education quality and talent cultivation standards. By supplying the finance industry with versatile talent capable of driving digital transformation, they can contribute to the high-quality development of China's financial sector.