Author(s)

JiaQi Zhang

Affiliation(s)

Xinjiang Agricultural Reclamation Academy, Shihezi 832000, Xinjiang, China.

Corresponding Author

JiaQi Zhang

ABSTRACT

Artificial intelligence (AI) is profoundly transforming industries and society, placing new demands on the competency structure of technology professionals. This paper explores the urgent need and pathways for reshaping the competencies of technology professionals and transforming their cultivation models in the AI era. The research indicates that the traditional mono-disciplinary competency structure is no longer suited to the complex innovation environment driven by AI, necessitating the construction of a composite competency system encompassing data thinking, algorithm application, interdisciplinary integration, innovation capability, and lifelong learning. Existing cultivation models exhibit limitations in interdisciplinary focus, innovation cultivation, knowledge update speed, and ethical awareness. To address this, this paper constructs a model comprising six core competency elements: professional technical skills, data processing, innovation, interdisciplinary integration, learning adaptation, and team collaboration. It proposes pathways for competency reshaping from four levels: government, higher education institutions, enterprises, and individuals. Key to transforming the cultivation model are: updating educational philosophies (emphasizing lifelong learning and innovation), innovating teaching methods (e.g., PBL, flipped classrooms), optimizing curriculum systems (integrating cutting-edge knowledge and strengthening interdisciplinary elements), and deepening industry-education integration. Analysis of domestic and international case studies validates the effectiveness of the proposed strategies. Successful transformation is strategically significant for enhancing individual competitiveness and driving national scientific and technological innovation and high-quality development.

KEYWORDS

Artificial intelligence era; Technology professionals; Competency restructuring; Cultivation model transformation; Interdisciplinary integration; Innovation capability

CITE THIS PAPER

JiaQi Zhang. Reshaping the competency structure and transforming the cultivation model of scientific and technological talent in the era of artificial intelligence. Social Science and Management. 2025, 2(3): 31-41. DOI: https://doi.org/10.61784/ssm3056.

REFERENCES

[1] Christopher A. Pissarides. Equilibrium Unemployment Theory. MIT Press, 2000: 22.

[2] Jing Bi. Research on New Models of Information Literacy Education for Digital Transformation. Information and Computer (Theoretical Edition), 2025, 37(04): 212-214.

[3] Lei Li, Yue Li, Feifei Zhang. Innovative Teaching Practice of Securities Investment Course in Application-Oriented Undergraduate Education. Journal of Higher Education, 2022, (3): 35-38.

[4] Jiahui Yang. Textual Research on China's AI Education Policies. Hubei Normal University, 2023.

[5] Zhongqiang Luo, Xingzhong Xiong. Reflections on Talent Cultivation Models for Electronic Information Majors Under Industry 4.0. Journal of Higher Education, 2024, 10(27): 167-170.

[6] Feng Wang, Shaojiang Liu, Zhiming Xu. Constructing a Talent Training System for Electronic Information Majors Empowered by "Intelligent Manufacturing + Artificial Intelligence". Henan Education (Higher Education), 2024, (4): 55-57.

[7] Yuguo Xia, Jingjing Sha, Shuirong Ju. Research on Cultivating Interdisciplinary Talent in Higher Vocational Electronic Information Majors Under Artificial Intelligence Background. Modern Vocational Education, 2023, (6): 97-100.

[8] Chunyan Wang, Tian Zhang. Research on Innovative Talent Training Model for Computer Majors Under Artificial Intelligence Background. Journal of Changchun Normal University, 2023, 42(10): 135-139.

[9] Baocun Liu, Xuejin Yi. Research on Policy Evolution of Artificial Intelligence Talent Cultivation in the UK. Journal of Comparative Education, 2023, (6): 12-23.

[10] Charles C. Johnson, Steven Harold, James Chestnut, et al. Addressing the Artificial Intelligence (AI) Talent Gap: Outcomes of the First U.S. Nationally Registered AI Apprenticeship Program. Education Sciences, 2025, 15(3): 324-332.

[11] Zufeng Wu, Ruiting Dai, Dandan Li, et al. Cultivating Innovative Talents for Frontier Fields of Artificial Intelligence. Research in Higher Education of Engineering, 2023, (05): 48-53.

[12] Julia S. Roppelt, Nina S. Greimel, Dominik K. Kanbach, et al. Artificial Intelligence in Talent Acquisition: A Multiple Case Study on Multinational Corporations. Management Decision, 2024, 62(10): 2986-3007.

[13] Francisco T. J. Franca, Henrique S. Mamede, Joao M. P. Barroso, et al. Artificial Intelligence Applied to Potential Assessment and Talent Identification in an Organisational Context. Heliyon, 2023, 9(4): e14694.

[14] Yutian Dang, Zhendong Lei, Fei Ye, et al. Architectural Education Response to Industry Transformation: Exploration of Pilot Reform in the Training Program of Architecture Major at Xi'an University of Architecture and Technology. Architectural Journal, 2023, (6): 109-114.

[15] Yaning Zhang. Exploration of Cultivating Intelligent Accounting Talents in Application-Oriented Universities Under the Background of Artificial Intelligence Era. Commercial Accounting, 2024, (12): 119-122.

[16] Christian Terwiesch. Would ChatGPT Get a Wharton MBA? A Prediction Based on Its Performance in the Operations Management Course. Mack Institute, Wharton School, University of Pennsylvania, 2023.

[17] Ana Isabel Canhoto, Fintan Clear. Artificial Intelligence and Machine Learning as Business Tools: A Framework for Diagnosing Value Destruction Potential. Business Horizons, 2020, 63(2): 183-193.