Exploring artificial intelligence literacy and the use of ChatGPT and Copilot in instruction on nursing academic report writing
Abstract
Background Nursing education increasingly emphasizes academic writing and communication, critical for delivering quality patient care and professional advancement. Rapidly emerging artificial intelligence (AI) tools such as ChatGPT and Copilot are transforming educational methodologies, and a focus is being placed on embedding AI literacy to effectively bridge the gap between theoretical knowledge and clinical practice. These technologies have the potential to reshape nursing education in a technology-driven health-care landscape.Aim This study investigated the effectiveness of AI literacy and the application of ChatGPT and Copilot in academic nursing report writing. It assessed the level of AI literacy of nursing students, examined the integration of basic AI concepts into a curriculum, and analyzed the impact of these tools compared with traditional teaching methods.Methods The study adopted a sample of 203 senior nursing students from Southern Taiwan to compare an AI-enhanced teaching approach using ChatGPT and Copilot with conventional methods. The curriculum, centered on the "Writing Case Reports and Seminars" course, employed the Analyze, Design, Develop, Implement, Evaluate model and incorporated scaffolding techniques to synergistically integrate clinical skills with academic learning. AI literacy was measured using the Meta AI Literacy Scale (MAILS). Summative assessments, adhering to the Taiwan Nursing Association standards, focused on individual and group case report evaluations.Findings Following an 18-week AI intervention, the experimental group demonstrated significant improvements in all dimensions of the MAILS. A ChatGPT usage of 100 % was found, with a notable enhancement discovered in the "Nursing Plan" section of case reports. Although the experimental group outperformed the control group in overall case report evaluations, the connections between identified problems and proposed plans were weaker and nursing interventions tended to be less individualized for the experimental group.Conclusions The incorporation of AI tools such as ChatGPT and Copilot into a scaffolding teaching framework significantly boosted students' AI literacy and performance in summative assessments. Effective AI training for students, supervised use of these tools, and continuous professional development for educators are paramount to successful implementation. Addressing the current limitations of AI has the potential to further improve academic writing, foster critical thinking, and ensure responsible application in patient care, ultimately leading to higher-quality and more effective nursing education.
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Existing AI tools have been utilized to facilitate personalized, self-paced learning, uncover insights for targeted interventions, strengthen decision-making and clinical reasoning in nursing students, provide AI-assisted diagnostics, improve academic writing, and apply advancements in patient care.
“For instance, existing AI tools have been utilized to facilitate personalized, self-paced learning within courses, uncover new insights for targeted interventions, strengthen decision-making processes and clinical reasoning skills in nursing students, provide AI-assisted diagnostics, improve academic writing, and apply these advancements in patient care [26–31].”
From Exploring the integration of AI and national quality courses in China: a study on teaching practices in nursing smart education (2025) · cites this paper as [29]
Another study found that AI-assisted students produced nursing care plans of lower individualisation and weaker logical coherence between identified clinical problems and proposed nursing interventions, despite achieving higher overall assessment scores.
“another found that AI-assisted students produced nursing care plans of lower individualisation and weaker logical coherence between identified clinical J o u r n a l P r e -p r o o f problems and proposed nursing interventions, despite achieving higher overall assessment scores (Tseng et al., 2025).”
From When the algorithm enters the classroom: A critical integrative review of large language models, nursing education structural gaps, and the reconstitution of professional identity (2026) · cites this paper as Tseng et al., 2025
The pattern reported by Tseng et al. (2025), higher overall assessment scores accompanied by less individualised clinical reasoning, matches the performance-learning dissociation closely.
“the pattern reported by Tseng et al. (2025)-higher overall assessment scores accompanied by less individualised clinical reasoning-matches this dissociation closely”
From When the algorithm enters the classroom: A critical integrative review of large language models, nursing education structural gaps, and the reconstitution of professional identity (2026) · cites this paper as Tseng et al., 2025
AI integration raised measurable scores while individualised clinical reasoning declined.
“The complementary findings of Tseng et al. (2025), already described in Section 3.2.3,demonstrate that AI integration raised measurable scores while individualised clinical reasoning declined.”
From When the algorithm enters the classroom: A critical integrative review of large language models, nursing education structural gaps, and the reconstitution of professional identity (2026) · cites this paper as Tseng et al. (2025)
The same tool produces opposite effects on nursing competency development depending on implementation context: implementations in which the displaced cognitive work is extraneous to clinical judgement are associated with competency gains, while implementations in which the displaced work is constitutive of that judgement are associated with measurable deficits in ethical reasoning, critical思维, and
“The same tool produces opposite effects on nursing competency development depending on implementation context, consistent with the criterion developed in J o u r n a l P r e -p r o o f Section 1.4: implementations in which the displaced cognitive work is extraneous to clinical judgement are associated with competency gains, while implementations in which the displaced work is constitutive of that”
From When the algorithm enters the classroom: A critical integrative review of large language models, nursing education structural gaps, and the reconstitution of professional identity (2026) · cites this paper as Tseng et al., 2025
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“The scale was originally developed by Carolus (17), and its Chinese translation and cultural adaptation were completed by Tseng et al. (18).”
Wan, W., Yu, X., Guo, Q., Zhang, Z., Huang, M., Liu, G., & Zhou, H. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1916153
“Additionally, GenAI can assist with nursing academic writing, literature retrieval, and case analysis (9).”
Wang, Q., Lü, N., Li, Q., Qi, J., Zhang, H., hu, X., & shi, H. (2026) · Research Square · doi:10.21203/rs.3.rs-11042340/v1
Ellis, M., Jakimowicz, S., Rose, L., & Fealy, S. M. (2026) · Nurse Education in Practice · doi:10.1016/j.nepr.2026.104980
Artificial Intelligence Supported Teaching Approaches in Nursing Education: A Scoping Review
GÜNGÖR, D. C., & Orgun, F. (2026) · Journal of Nursing Education · doi:10.3928/01484834-20260827-04
Mulya, A. P., Kosassy, S. M., Yamin, A., Lukman, M., & Susanti, R. D. (2026) · Research Square · doi:10.21203/rs.3.rs-11142928/v1
Yingqi, D., Zhao, X., Pan, X., & Wei, J. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1997701
“(40, 41).”
Luo, J., Ma, L., 李昌秀, Wang, Y., Feng, D., Liu, R., Luo, L. W., Chen, Y., Ran, H., Hu, Q., et al. (2026) · Nursing Open · doi:10.1002/nop2.70951
“Accordingly, the educational use of AIGC should extend beyond operational competence with AI tools to include the ability to critically analyse and evaluate AI-generated information (Tseng et al. 2025).”
Self-citationAli, K. A. Y. A., Yurtseven, C. N., Öz, F., Çamiçi, F., Bayansalduz, M., Ekinci, S., & Turhal, S. N. (2026) · Behavioral Sciences · doi:10.3390/bs16112032
Nydahl, P., Krotsetis, S., Langer, G., & Krüger, L. (2026) · Nursing in Critical Care · doi:10.1111/nicc.70753
- Authors
- Tseng, L.-P., Huang, L.-P., & Chen, W.-R.
- Affiliations
- St. Martin de Porres Hospital (TW)
- Chaoyang University of Technology (TW)
- Corresponding author
- Chen, Wei-Ru
- Venue
- Nurse Education Today
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 147
- Pages
- 106570
- ISSN
- 0260-6917 (ISSN-L)
- OpenAlex
- W4406358173
- Cited by
- 75
- References
- 23
- Citations by year
- 2026: 56 · 2025: 19
- FWCI
- 18.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nurse Education Today (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Keywords
- AI literacy, ChatGPT, Copilot, case report writing, nursing students, instructional scaffolding, nursing education, nursing care planning, critical thinking
- MeSH terms
- Generative Artificial Intelligence, Adult, Artificial Intelligence, Curriculum, Education, Nursing, Baccalaureate, Female, Humans, Male, Students, Nursing, Taiwan, Writing
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