Research on the Construction Path of Digital-Intelligence Literacy for Science and Technology Talents in the Era of LLMs

Qian Cao; Meng Li

doi:10.54691/thtsam65

Authors

Qian Cao
Meng Li

DOI:

https://doi.org/10.54691/thtsam65

Keywords:

Large Language Models, Science and technology Talents, Digital-Intelligence Literacy, Human-AI Collaboration, Competency Framework.

Abstract

When large language models become more and more powerful, the study aims at the gap trouble of science and technology talent education, and proposes a four-dimensional "Digital-Intelligence Literacy" framework. This framework includes four parts: Cognitive & Mindset, Core Skills, Science and Technology Ethics, and Collaborative Innovation. It will help humans make a shift from technology users to human-AI collaborators. We analyzed the gap between the troubles and typical examples, and built a path of a mix of education reform, dynamic evaluation, and institutional protection. The study will be a guide to meet the demand of cultivating new quality productive forces, and provides theoretical insights and practical guidance.

Downloads

Download data is not yet available.

References

[1] Qiu, J., et al. (2024). Digital-intelligence literacy for generative artificial intelligence: Connotation, competency framework, and cultivation path. Information Studies: Theory & Application.

[2] Pang, J., et al. (2026). Core literacy of science and technology talents based on the DIKW theory: Conceptual evolution, framework construction, and enhancement path. Science & Technology Progress and Policy.

[3] Luo, X., & Lin, Y. (2024). Exploration of the enhancement path for graduate student digital literacy education driven by AIGC. Journal of Library and Information Science in Agriculture, 36(9), 70–77.

[4] Zhu, Z., Dai, L., & Hu, J. (2023). New approaches for AIGC technology empowering the digital transformation of higher education. China Higher Education Research, (6), 12–19.

[5] Bai, C., et al. (2025). Towards a human-machine symbiotic future: The logic and evidence of AI-driven management research. Nankai Business Review.

[6] Yu, Y., et al. (2026). Exploration of the talent cultivation model for polymer materials and engineering majors in the digital-intelligence era. Polymer Bulletin.

[7] Zhao, Y. (2026, April 16). Cultivating digital-intelligence talents to meet the development needs of new quality productive forces. Guangming Daily.

[8] Organisation for Economic Co-operation and Development. (2022). Digital competence framework for citizens (DigComp 2.2).

[9] United Nations Educational, Scientific and Cultural Organization. (2023). AI and education: Guidance for policy-makers.

[10] European Commission. (2019). Ethics guidelines for trustworthy AI.