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Application prospects of DeepSeek in nursing theoretical teaching
Open accessAbstract
This article mainly discusses the application prospects and suggestions of DeepSeek in theoretical teaching of nursing. DeepSeek, with core technologies such as natural language processing, machine learning, and deep learning, can provide personalized learning plans, real-time feedback, and curriculum generation, bringing innovation to nursing education. The article proposes application suggestions such as using DeepSeek to create customized learning materials, optimizing teaching content and presentation methods, enhancing teaching interactivity, and emphasizes the importance of protecting student privacy and following ethical norms. At the same time, the article also points out strategies that combine traditional teaching methods, such as integrating online and offline teaching and leveraging the leading role of teachers.
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Research works printed but not linked: 5
- Zhao, S. (2023). The challenges and coping strategies of ChatGPT on human language ability and language education. Changjiang Academic, 2023(4), 114-118.
- Li, P., Chen, J., & Ning, N. (2023). Progress and Reflection on the Application of ChatGPT in the Medical Field. West China Medicine, 38(10), 1456-1460.
- Li, J., Xiao, Y., & Xie, L. (2022). The current application status of artificial intelligence in medical humanities education. China Medical Education Technology, 36(3), 286-291.
- Wang, Y., Chu, H., Liang, J. J., Lu, Z. N., & Xiao, Z. M. (2022). The Current Situation and Exploration of Higher Medical Education Reform under the Background of Artificial Intelligence. China Medical News, 19(13), 64-67.
- Zhang, F., & Chen, W. (2023). ChatGPT and Higher Education: How Artificial Intelligence Drives Learning Transformation. Journal of Chongqing University of Technology (Social Sciences), 37(5), 26-33.
Cited by
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Yang, M., Long, J., He, Y., Meng, R., Ling, C., Long, Q., & Wen, J. (2026) · BMC Nursing · doi:10.1186/s12912-026-05132-7
“Studies by Cheng [10] and Lv et al. [33] suggest that DeepSeek may support nursing theory instruction and documentation processes, while comparative studies by Gurbuz [26], Wang and Chen [50], Yan et al. [51], and Yao et al. [52] show that the model performs competitively in selected educational…”
Metadata
- Authors
- Cheng, L.
- Affiliations
- Trinity University of Asia (PH)
- Corresponding author
- Cheng, Lin
- ORCID
- Cheng, Lin
- Venue
- Advances in Engineering Innovation
- Publisher
- EWA Publishing
- Published
- 2025-03-17
- Volume
- 16
- Issue
- 2
- Pages
- 32-36
- ISSN
- 2977-3903 (ISSN-L)
- 2977-3911
- OpenAlex
- W4408533794
- Cited by
- 1
- References
- 0
- Citations by year
- 2026: 1
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Advances in Engineering Innovation (publishedVersion, cc-by)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Educational Technology and Pedagogy
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- DeepSeek, large language models, nursing education, individualized learning plans, real-time feedback, student privacy, AI ethics in education, blended online and offline teaching, teacher role
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- Cheng, L. (2025). Application prospects of DeepSeek in nursing theoretical teaching. Advances in Engineering Innovation. https://doi.org/10.54254/2977-3903/2025.21625
