Research on Personalized Cultivation Paths of Vocational College Students’ Digital Literacy in the Artificial Intelligence Era
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Abstract
The in-depth application of artificial intelligence has reshaped vocational education and made digital literacy a key component of students’ core competitiveness. Vocational colleges currently face prominent problems in digital-literacy cultivation, including large differences in students’ basic foundations, homogenized training models, and single evaluation systems. Constructing a personalized cultivation path requires starting from learner characteristics, integrating intelligent technology empowerment, hierarchical and classified teaching, and dynamic evaluation and feedback, and forming a closed-loop mechanism of “diagnosis-design-implementation-evaluation.” This paper analyzes the theoretical basis and core elements of digital literacy in vocational education, identifies current cultivation dilemmas, and proposes personalized training programs based on learner profiles. By diagnosing students’ digital-competence baselines, customizing differentiated learning paths, and improving continuous feedback mechanisms, the proposed approach can enhance students’ digital cognition, digital operation, and digital application capabilities. The study provides a practical reference for cultivating high-quality technical talents adapted to intelligent industrial environments.
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References
Y. C. Zhang, “AI-Driven Transformation of Vocational Education: Opportunities, Challenges, and Future Paths,” International Journal of Knowledge Management (IJKM), vol. 21, no. 1, pp. 1-20, 2025, doi: 10.4018/IJKM.394819.
H. Shi, “Adaptive Learning in Vocational Education: AI-Powered Content Recommendations,” International Journal of High Speed Electronics and Systems, 2025, doi: 10.1142/S0129156425408319.
L. Mao, “Research on the Mechanism of Digitalization Empowering Vocational Education to Develop with High Quality,” New Explorations in Education and Teaching, pp. 3(7), 2025, doi: 10.70711/NEET.V3I7.7230.
Z. Ning and S. Q. Huang, “Strategies and Practices of Curriculum Reform in Vocational Education under the Background of Digital Transformation,” The Frontiers of Society, Science and Technology, pp. 6(9), 2024, doi: 10.25236/FSST.2024.060907.
R. C. Jiang, Y. H. Chen, Y. Pi, et al., “Opportunities and Challenges of AI in Vocational Education,” International Journal of Learning and Teaching, vol. 10, no. 5, pp. 590-596, 2024, doi: 10.18178/IJLT.10.5.590-596.
M. Almansour and M. F. Alfhaid, “Generative artificial intelligence and the personalization of health professional education: A narrative review,” Medicine, vol. 103, no. 31, e38955, 2024, doi: 10.1097/MD.0000000000038955.
J. Wang, “Optimization of Higher Vocational Artificial Intelligence Talent Cultivation Driven by New Quality Productive Forces,” Journal of Technology Innovation and Engineering, pp. 1(6), 2025, doi: 10.63887/JTIE.2025.1.6.10.
L. Mao, “Innovative Pathways and Paradigm Innovation for the Digital-Intelligent Transformation of Practical Training Teaching in Vocational Education,” Region - Educational Research and Reviews, vol. 7, no. 8, 2025, doi: 10.32629/RERR.V7I8.4476.
Y. W. Leong, “Artificial Intelligence, Automation, and Technical and Vocational Education and Training: Transforming Vocational Training in Digital Era,” Engineering Proceedings, vol. 103, no. 1, p. 9, 2025, doi: 10.3390/ENGPROC2025103009.
Y. Miao, Z. Xiao, and Y. Zhang, “Impact of talent cultivation model for industry education integration in vocational education by artificial intelligence and BPNN,” Scientific Reports, vol. 15, no. 1, 38019, 2025, doi: 10.1038/S41598-025-21935-1.
J. Sun, H. Wang, X. Wang, et al., “Digital Empowerment of Vocational Education: Opportunities, Challenges and Strategies,” Exploration of Educational Management, pp. 3(10), 2025, doi: 10.12417/3029-2328.25.10.025.
Z. Shi, J. Li, and F. Lu, “Exploration and Practice of the “Cultivation– Growth–Incubation” Talent Training Model in the Master Skills Studio,” Journal of Contemporary Educational Research, vol. 9, no. 9, pp. 156-162, 2025, doi: 10.26689/JCER.V9I9.12451.
PS, “Artificial intelligence in health professions education,” Archives of Medicine and Health Sciences, vol. 10, no. 2, pp. 256-261, 2022, doi: 10.4103/AMHS.AMHS_234_22.
A. Wang, “The Upgrade Form and Innovation Path of Vocational Education Talent Training Under the Background of Artificial Intelligence,” Advances in Computer and Communication, pp. 6(3), 2025, doi: 10.26855/ACC.2025.07.004.
H. Zhou and D. Zhou, “Transformation of Vocational Education Based on Generative Artificial Intelligence: Impact, Opportunity and Countermeasures,” in Hunan Chemical Vocational Technology College, editor. Proceedings of the Conference, 2024, doi: 10.4108/EAI.24-11-2023.2343636.