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The Digital Intelligent Accounting Talent Training Model and Government-Industry-Academia Collaborative Education: A Perspective from Triple Helix Theory

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DOI: 10.23977/aetp.2023.071014 | Downloads: 23 | Views: 378

Author(s)

Feimei Liao 1, Ze Yang 1

Affiliation(s)

1 School of Finance, Jiangxi Normal University, Nanchang, 330022, China

Corresponding Author

Feimei Liao

ABSTRACT

The continuous progress of artificial intelligence technology in China has impacted the original pattern of many traditional industries, and the long-established accounting industry is also facing the dilemma of being replaced. In this paper, from the perspective of triple helix theory, on the basis of elaborating the meaning of digital intelligence and the inner mechanism of action, and with the cultivation of composite digital intelligence accounting talents with data analysis and processing ability, original innovation ability and efficient collaboration ability as the cultivation goal, we focus on the tripartite collaborative education model of government-industry-university, and find that the cultivation of new digital intelligence accounting talents in the era of big data can make use of the tripartite government-industry-university. It is found that the cultivation of new digital intelligent accounting talents in the era of big data can make use of the collaborative education platform jointly constructed by government, industry and university, and the interaction of the three main forces in the platform can accomplish the cultivation goal of accounting talents more efficiently and improve the cultivation ability. This paper hopes to provide useful reference for solving the real dilemma.

KEYWORDS

Triple helix theory, digital intelligence, accounting talent training, collaborative education

CITE THIS PAPER

Feimei Liao, Ze Yang, The Digital Intelligent Accounting Talent Training Model and Government-Industry-Academia Collaborative Education: A Perspective from Triple Helix Theory. Advances in Educational Technology and Psychology (2023) Vol. 7: 81-87. DOI: http://dx.doi.org/10.23977/aetp.2023.071014.

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