Abstract: This study examines the development of new-quality productive forces and explores AI-driven digital-intelligent talent cultivation pathways in accounting education. The research begins with an analysis of the background: New-quality productive forces are driving educational digitalization reforms, while AI’s restructuring of accounting functions creates demand for “data-intelligent decision-making” professionals. Current accounting education faces supply-demand gaps including outdated curricula, insufficient faculty, and practical training deficiencies. The core competencies for digital-intelligent accounting professionals are outlined as four pillars——: financial literacy (building a “finance-business-strategy” collaborative framework), technical proficiency (mastering data processing, intelligent tools, and AI models), innovation capability (integrating technologies for real-world solutions), and ethical awareness (ensuring data security, algorithmic fairness, and professional accountability). Implementation strategies and future directions are proposed: A tripartite mechanism—policy coordination for funding and standards, school-enterprise collaboration for practical resources, and dynamic maintenance for technological synchronization—will ensure pathway implementation. Future efforts should focus on deepening financial large-model education applications, promoting interdisciplinary integration, and expanding global training programs to align accounting education with new-quality productive forces demands.
Keywords: New-quality productive forces; AI empowerment; Accounting education; Digital-intelligent talent cultivation
I.Research Background and Theoretical Basis
(I)The development of new productive forces gives rise to new propositions of educational reform
Since its initial proposal by President Xi Jinping in September 2023, the concept of new-quality productivity has become a pivotal driver for high-quality economic development. The 2024 Government Work Report prioritized it as the top of ten key tasks, while the 2025 Politburo study session further emphasized deepening AI integration with industrial innovation. In education, both the “Outline for Building a Strong Education Nation (2024-2035)” and the Ministry of Education’s “Guidelines on Accelerating Digital Transformation in Education” require higher education to fully integrate technologies like AI and big data, cultivating interdisciplinary talents capable of meeting intelligent economy demands. As a core sector bridging economic activities and management decision-making, the reform of accounting talent cultivation models has become crucial in implementing the new-quality productivity strategy, urgently requiring technological empowerment for transformation.
(II)AI reconstructs the financial and accounting functions and leads to the reform of talent structure
Digital transformation has become an irreversible trend in the accounting and finance sector. A joint survey by IMA and Deloitte reveals that AI adoption rates in financial functions are expected to double within the next three to five years, with 16% of enterprises already implementing generative AI and 44% planning to adopt it within five years. AI skills have emerged as the second most essential technical competency for accounting professionals. KPMG’s research further confirms that while 71% of companies have applied AI in financial operations, only 41% have achieved medium-to-high adoption levels, with talent shortages being the core bottleneck. Industry demands have shifted from traditional “accounting-focused” professionals to “data-driven decision-makers” capable of integrating business-finance synergy, data analytics, and intelligent tool applications. However, the knowledge structure of conventional accounting professionals struggles to meet the requirements of emerging scenarios like smart auditing and financial large model applications.
(III)There is a significant gap between accounting education and the demand for digital intelligence
The current accounting education system struggles to meet industry demands, presenting three critical challenges: First, outdated curricula with textbook revisions taking 3-5 years—six times slower than technological advancements (6-12 months)—result in missing cutting-edge topics like RPA and blockchain-based financial applications. Only 37.04% of institutions offer specialized smart accounting programs. Second, faculty shortages plague the field: 63.4% of accounting educators lack digital literacy and dual expertise in both finance and technology, hindering practical guidance for intelligent system operations and data-driven decision-making. Third, limited industry-academia collaboration persists—only 35% of finance universities have established deep integration bases. Insufficient simulation data and outdated teaching software lag behind real-world corporate applications, rendering practical training superficial. This supply-demand mismatch underscores the urgent need to explore AI empowerment pathways.
II.AI-enabled Talent Training for Digital and Intelligent Accounting
(I)“Understanding Finance”: Building a Professional Foundation for Digital and Intelligent Finance
In the digital intelligence era, “financial literacy” has evolved beyond traditional manual bookkeeping and report preparation to encompass a multi-dimensional financial cognition system. To adapt to AI’s profound transformation of the financial sector, finance professionals must achieve dual breakthroughs in capability development: First, they should deeply understand the underlying logic and technical implementation paths of intelligent finance. This involves not only mastering how AI restructures core financial processes—such as smart invoice systems that leverage OCR recognition and algorithmic verification to achieve full-chain automation from “automated invoice extraction → self-generated accounting vouchers → one-click tax data synchronization” —but also comprehending the rule mapping logic behind digital intelligence tools. For instance, when RPA robots execute accounts payable workflows, they must convert rigid financial principles like “three flows consistency” into recognizable and executable code instructions to ensure technical applications preserve their financial essence. Second, it’s crucial to strengthen the synergy between finance, business operations, and strategic planning. Finance professionals should skillfully utilize multi-dimensional data generated by AI to accurately diagnose operational bottlenecks and design actionable financial solutions. More importantly, they need to develop management vision and decision-support capabilities for digital intelligence finance. By leveraging real-time data dashboards from intelligent financial platforms, they can dynamically track cost structures, capital efficiency, and value drivers, providing forward-looking insights for enterprises to achieve “cost reduction, efficiency enhancement, and risk control.” This truly transforms financial data into a core engine that drives business growth and supports strategic implementation.
(II)“Technology Access”: The tool matrix to master AI+ finance
“Techno-Capability” signifies that accounting professionals should not merely use technical tools superficially, but must develop the ability to effectively integrate technology into financial processes and create business value. Specifically, this capability system comprises three key dimensions: First, solid data processing and analytical skills. This includes flexibly applying tools like Python to address real-world financial scenarios—— For instance, when analyzing monthly sales data, one could utilize Pandas to clean abnormal records, employ NumPy to calculate regional gross margin fluctuations, and create sales-profit dual-axis trend charts with Matplotlib to visually present business performance. Additionally, SQL can be used to extract “R&D investment-to-revenue ratios of various product lines over the past three years” from ERP systems, providing data support for tax planning such as R&D expense super deduction. Second, mastery of intelligent financial tool configuration, deployment, and maintenance. When implementing RPA for accounts payable workflows, one should independently design and build robotic logic chains to achieve full automation: “automated invoice-order amount comparison → variance marking → compliant payment triggering.” Furthermore, rapid troubleshooting (e.g., resolution of parsing failures caused by non-standard invoice formats) and rule optimization should be conducted when errors occur or operations pause. Third, familiarity with business-finance mapping configurations in intelligent financial platforms, enabling precise association between business attributes and financial accounts to ensure seamless synchronization of business data into financial information. Finally, understanding the applicable boundaries and logical mechanisms of AI models in financial scenarios is essential. For instance, when applying machine learning models to forecast cash flows, it is crucial to strategically select key variables such as “historical revenue” and “accounts receivable turnover ratio,” while maintaining discernment in interpreting the results—— When significant discrepancies emerge between predicted values and actual figures, it is essential to investigate whether these deviations stem from missing dependent variables or anomalies in training data. This ensures that technical applications consistently serve the accuracy and reliability of financial decision-making.
(III)“Innovation”: Anchoring the pain points and opportunities of digital intelligence scenarios
The “Innovative Capability” requirement for accounting professionals demands closed-loop innovation from problem identification to solution design, enabling proactive integration of technological tools in real business scenarios to achieve breakthroughs in financial processes and decision-making models. Specifically, this capability manifests in three dimensions: First, a problem-oriented innovative mindset that identifies inefficiencies in traditional financial operations to propose technical enhancements—— For instance, addressing the time-consuming nature of audit voucher sampling and risk control challenges, we developed an intelligent voucher analysis system using “NLP voucher parsing + machine learning risk tagging”, reducing sampling cycles from 3 days to 2 hours with 95% accuracy improvement. Similarly, for high inventory costs in manufacturing, we created a dynamic inventory model integrating AI and IoT data, achieving 15% increased inventory turnover rate and 20% reduced capital occupation. Second, scenario-based integration of cutting-edge technologies—— For example, leveraging blockchain’s distributed ledger features in cross-border e-commerce to build an integrated “transaction-settlement-tax reporting” platform that resolves pain points like slow reconciliation and high exchange rate risks. During financial reporting preparation, AI-powered models automatically generate “Management Discussion and Analysis” content, requiring minimal manual adjustments to produce high-quality analyses aligned with strategic contexts. Third, it possesses the execution capability to transform innovative concepts into practical value, driving the entire closed-loop process of “requirement research-solution design-prototype development-iterative testing” —— For instance, when building the “Intelligent Tax Planning System”, we started from business pain points, designed functional logic in line with policy requirements, and continuously optimized through pilot verification to ensure the innovation outcomes are replicable, scalable, and measurable.
(IV)“Ethics”: Establishing a compliance coordinate system for AI financial applications
“Ethical Integrity” serves as the essential value foundation for digital and intelligent accounting professionals. It emphasizes maintaining compliance standards and upholding fairness while developing ethical decision-making capabilities that balance efficiency with accountability. Financial personnel must strictly adhere to data security and privacy protection ethics, complying with regulations such as the Data Security Law and Personal Information Protection Law throughout data processing workflows. For instance, effective desensitization measures must be implemented before training AI models with financial data, including setting tiered access permissions to prevent unauthorized data usage and leakage risks, thereby establishing robust information security defenses from the outset. Algorithmic bias must be vigilantly monitored to safeguard fairness principles. When designing and applying AI-driven financial tools, potential discriminatory logic should be proactively identified and corrected. For example, customer credit rating models should be evaluated for systematic underestimation of small businesses by incorporating non-scale metrics like tax compliance and operational stability into scoring systems. Intelligent tax planning systems must establish compliance thresholds to avoid excessive focus on tax savings at the expense of potential risks, ensuring both efficiency and fairness in outcomes. Professional judgment remains the primary responsibility, with clear ethical boundaries in human-machine collaboration. Accounting professionals must recognize AI’s instrumental nature without diminishing professional judgment through technological automation. For instance, AI-generated financial forecasts require manual verification of critical assumptions and data validity, while high-risk items identified by AI audit systems must undergo substantive review using original vouchers and business context to avoid over-reliance on automated outputs. In addition, we should also take the initiative to follow up industry ethics norms (such as the “Ethics Guidelines for Intelligent Audit” issued by the Chinese Institute of Certified Public Accountants), and continue to improve our ethical awareness and coping ability in a complex technical environment.
III.Conclusion
In this context, cultivating digital and intelligent finance professionals must transcend traditional accounting skill frameworks, shifting toward a three-dimensional capability framework centered on “technical comprehension, business insight, and strategic execution.” Here, “technical proficiency” serves as the foundation, transforming financial personnel from passive tool users into versatile operators who master technologies and optimize processes; “innovation capability” acts as the key driver, elevating them from executors to pioneering practitioners who leverage technology to enhance efficiency and data to support strategies; while “ethical awareness” establishes the baseline, ensuring technological innovation operates within compliant and credible boundaries, becoming guardians of value in building responsible financial systems. Only through the synergistic development of these three capabilities can finance professionals truly become core drivers of corporate digital transformation, advancing steadily through the AI-driven wave.
(I)Safeguard Measures
To ensure the effective implementation of digital and intelligent accounting talent development pathways, a closed-loop support mechanism must be established across three dimensions: policy coordination, resource integration, and technical operations, addressing the bottlenecks of “policy gaps, resource fragmentation, and technological lag” in traditional training models. In terms of policy support, a tripartite collaboration among government, universities, and industry should be promoted. Regional universities and leading enterprises can jointly apply for provincial or national industry-education integration projects to secure special funds for AI financial training platform development. Simultaneously, education authorities and tax departments should collaborate to incorporate “intelligent financial tool application capabilities” into professional evaluation systems, specify course credit requirements, and provide AI practical training subsidies for instructors, encouraging them to participate in real corporate projects to bridge the gap between classroom learning and industry practice. Regarding resource support, the focus lies in deepening school-enterprise cooperation to build an integrated “teaching-practice-employment” platform. This includes establishing digital accounting talent training bases equipped with high-performance hardware and enterprise-level software to create multi-industry virtual financial scenarios. Corporate finance experts can serve as industry mentors to conduct RPA and intelligent tax workshops, while organizing students to engage in authentic corporate projects. Additionally, partnerships with accounting associations and CPA organizations should be strengthened to introduce industry case libraries and professional certifications, enhancing the relevance of talent development and employment competitiveness. For technical support, a “dynamic iteration + secure control” maintenance mechanism should be established. Collaborate with tool manufacturers to regularly optimize AI financial platform features based on teacher and student feedback, ensuring practical training content evolves in sync with technological advancements. Strengthen data security management by implementing desensitization processes and implementing permission-based access controls for real financial data used in training programs, thereby preventing information leakage risks and establishing a reliable technical environment for school-enterprise partnerships.
(II) Research Prospects
1. Deepening the application of financial big models in education. Explore personalized learning path design, virtual simulation training scenario construction, and related educational ethics issues based on large models, driving the transformation of teaching paradigms from “tool utilization” to “intelligent empowerment”.
2. Advancing innovative interdisciplinary integration models. Strengthen curriculum development in composite fields such as “accounting + AI + law” and “accounting + AI + business”, incorporating industry-specific scenarios (e.g., manufacturing supply chains, cross-border e-commerce finance) with tailored content. Collaborate with enterprises to establish competency standards for digital accounting professionals, providing a foundation for tiered training and evaluation.
3. Expanding global-oriented training content and pathways. Focus on integrating International Accounting Standards with AI tools and intelligent cross-border tax planning, cultivating accounting talents with international competitiveness and cross-domain operational capabilities to support corporate participation in global competition.
References
1.Yang Yang, Ren Xiangyang, Song Yujia. Empowering Accounting Education with AIGC under New Productivity: Scenarios, Issues and Countermeasures [J]. Journal of Financial Management Research, 2025, (8).147-154
2.Shen Tianhuan. Research on the Transformation Path of Accounting Majors in the AI Era [J]. China Market, 2025, (20).159-162
3. Huang Yinxuan, Guo Xiaoqin, Yan Fengjie. Research on the Reform of Accounting Talent Training Model Based on Generative Artificial Intelligence [J]. International Business & Accounting, 2025, (12).76-80
4. Xia Siming. Opportunities and Challenges Brought by Artificial Intelligence to the Accounting Field [J]. China Collective Economy, 2025, (20).193-196
