The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Prompt Engineering for Nurse Educators
Abstract
BACKGROUND: The integration of generative artificial intelligence (AI) tools like OpenAI's ChatGPT into nursing education marks a transformative advance in personalized learning and interactive engagement. PROBLEM: Variability in faculty's experience with AI outputs highlights the need for well-crafted prompts that align with educational objectives, maximize learning outcomes, and ensure contextual relevance. Effective prompting is a key to eliciting accurate, relevant responses from AI, fostering a dynamic learning environment that bolsters student comprehension of complex topics. APPROACH: This article examines the critical role of prompt engineering in optimizing AI-generated content's effectiveness within academic settings. With a detailed guide and strategies specifically designed for nursing education, the article prepares faculty to proficiently use generative AI. CONCLUSIONS: By mastering prompt engineering, educators can leverage AI tools as powerful aids, potentially significantly enhancing teaching effectiveness, work efficiency, and student learning outcomes.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The key conceptual gap was insufficient integration with established pedagogical frameworks and unclear definitions of GenAI roles in education.
“The key con ceptual gap was insufficient integration with established pedagogical frameworks and unclear definitions of GenAI roles in education”
From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Sun, 2024
Assessment tools progressed from simple questions to context-specific evaluation instruments with enhanced feedback mechanisms and explanation quality.
“assessment tools progressed from simple ques tions to context-specific evaluation instruments with enhanced feedback mechanisms and explanation quality”
From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Sun, 2024
Citations
Cites
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133
Prompt Engineering with ChatGPT: A Guide for Academic Writers
Giray, L. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03272-4 · Cited by 776
Unlocking the Power of ChatGPT: A Framework for Applying Generative AI in Education
Su (苏嘉红), J., & Yang (杨伟鹏), W. (2023) · ECNU Review of Education · doi:10.1177/20965311231168423 · Cited by 625
The CLEAR path: A framework for enhancing information literacy through prompt engineering
Lo, L. S. (2023) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2023.102720 · Cited by 485
The ChatGPT Storm and What Faculty Can Do
Sun, G. H., & Hoelscher, S. H. (2023) · Nurse Educator · doi:10.1097/nne.0000000000001390 · Cited by 305
Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice
Eager, B., & Brunton, R. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.5.02 · Cited by 287
Prompt Engineering in Medical Education
Heston, T. F., & Khun, C. (2023) · International Medical Education · doi:10.3390/ime2030019 · Cited by 267
Wang, M., Wang, M., Xu, X., Yang, L., Cai, D., & Yin, M. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3324714 · Cited by 199
Learning Preferences and Engagement Level of Generation Z Nursing Students
Hampton, D. C., Welsh, D., & Wiggins, A. T. (2019) · Nurse Educator · doi:10.1097/nne.0000000000000710 · Cited by 131
Tan, K., Chong, M. C., Subramaniam, P., & Wong, L. P. (2018) · Nurse Education Today · doi:10.1016/j.nedt.2017.12.030 · Cited by 115
The application of Chat Generative Pre-trained Transformer in nursing education
Liu, J., Liu, F., Fang, J., & Liu, S. (2023) · Nursing Outlook · doi:10.1016/j.outlook.2023.102064 · Cited by 103
Using Audience Response Technology for Pretest Reviews in an Undergraduate Nursing Course
Stein, P. S., Challman, S. D., & Brueckner, J. K. (2006) · Journal of Nursing Education · doi:10.3928/01484834-20061101-08 · Cited by 75
Teaching Excellence in Nursing Education: A Caring Framework
Sawatzky, J. V., Enns, C., Ashcroft, T., Davis, P. L., & Harder, N. (2009) · Journal of Professional Nursing · doi:10.1016/j.profnurs.2009.01.017 · Cited by 63
Human Intelligence Analysis through Perception of AI in Teaching and Learning
Kshirsagar, P. R., Jagannadham, D. B. V., Alqahtani, H., Naveed, Q. N., Islam, S., Thangamani, M., & Dejene, M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/9160727 · Cited by 55
Prompt engineering when using generative AI in nursing education
O’Connor, S., Peltonen, L.-M., Topaz, M., Chen, L.-Y. A., Michalowski, M., Ronquillo, C., Stiglic, G., Chu, C. H., Hui, V., & Denis-Lalonde, D. (2024) · Nurse Education in Practice · doi:10.1016/j.nepr.2023.103825 · Cited by 42
Renner, B., Prilla, M., Creß, U., & Kimmerle, J. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00820 · Cited by 38
Towers, A., Williams, M. N., Hill, S., Philipp, M. C., & Flett, R. A. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01941 · Cited by 27
Juan, S. (2020) · Nurse Education Today · doi:10.1016/j.nedt.2020.104710 · Cited by 27
Preiksaitis, C. M., Nash, C. J., Gottlieb, M. A., Chan, T. M., Alvarez, A., & Landry, A. I. (2023) · AEM Education and Training · doi:10.1002/aet2.10924 · Cited by 22
InnovationCENTER: Engaging Nursing Students Through Integrationof theAudience Response System
Lee, S. T., & Dapremont, J. (2012) · Nursing Education Perspectives · doi:10.5480/1536-5026-33.1.55 · Cited by 16
Flagged first, then by citations
Research works printed but not linked: 1
- Heston, 2023, Prompt engineering in medical education, Int Med Educ, 2, 198, 10.3390/ime2030019 · DOI
Cited by
Sorted by citations
Shen, M., Shen, Y., Liu, F., & Jin, J. (2025) · BMC Nursing · doi:10.1186/s12912-025-03115-8 · Cited by 76
“Previous studies [34–35] indicate that GenAI can enhance learning efficiency, broaden analytical perspectives, and facilitate personalized learning for nursing students.”
Reliability of ChatGPT in automated essay scoring for dental undergraduate examinations
Quah, B., Zheng, L., Sng, T. J. H., Yong, C. W., & Islam, I. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05881-6 · Cited by 87
“The authors thus recommend that to circumvent this, the use of ChatGPT for essay scoring should be restricted to questions that are less broad (e.g. shorter essays), or by breaking the task into multiple prompts for each individual criterion to reduce variations in interpretation [27, 35].”
BackgroundMethodAlanazi, M., Soh, B., Samra, H. E., & Li, A. (2025) · Computers · doi:10.3390/computers14050185 · Cited by 52
“These differentiated support mechanisms directly impact students’ learning experiences, with real-time error correction and context-sensitive explanations being among the most valued features.”
Amankwaa, I., Ekpor, E., Cudjoe, D., Kobiah, E., Fuseini, A.-K. J., Diebieri, M., Gyamfi, S., & Brownie, S. (2025) · Nurse Education Today · doi:10.1016/j.nedt.2025.106822 · Cited by 20
Large Language Models in Nursing Education: Concept Analysis
Harrington, J. E., Booth, R. G., & Jackson, K. T. (2025) · JMIR Nursing · doi:10.2196/77948 · Cited by 22
“…methodology [9,12,17,26-31], 11 used quantitative designs [6,7,32-40], 1 used a mixed methods approach [41], 2 were educational evaluations or frameworks [16,42], 6 were narrative or discussion papers [11,19,43-46], and 7 were systematic, literature, scoping, or rapid reviews [2,5,8,18,20,47,48].”
InfluentialFrom academics to Aidemics: Unpacking the human–AI symbiosis in higher education
Yurdunkulu, A., Bulut, M. A., & Göçen, A. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105796 · Cited by 25
How Can Nursing Faculty Create Case Studies Using AI and Educational Technology?
Silvestri-Elmore, A., & Burton, C. W. (2024) · Nurse Educator · doi:10.1097/nne.0000000000001734 · Cited by 14
Chen, J., Liu, Y., Liu, P., Zhao, Y., Zuo, Y., & Duan, H. (2025) · Journal of Medical Internet Research · doi:10.2196/70789 · Cited by 16
“Similar observations in existing literature emphasize that the cognitive and time demands of prompt engineering are significant barriers to the efficient use of AI tools [23,45,46].”
Jang, A., Oh, M., & Song, M. O. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104541 · Cited by 13
Cao, Y., Li, H., Cao, X., & Peng, J. (2025) · BMC Nursing · doi:10.1186/s12912-025-03010-2 · Cited by 12
“The quality of generated content is partly influenced by the quality of the prompts, which are the input texts or questions provided when interacting with LLMs. Designing effective prompts is crucial for obtaining desired outcomes [14].”
Kulintang, M. B. M., Ngo, A. L., Salas, C. J., Sumaoy, K., & Alquwez, N. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107050 · Cited by 7
Jiang, X., Liu, J., Dai, S., Mao, X., Cha, R., & Wu, L. (2025) · JMIR Formative Research · doi:10.2196/78816 · Cited by 5
“…Generated Content Mentor-level certificate from the Ministry of Industry and Information Technology, along with a health promotion nurse in health education, collaboratively developed the structured prompts [26] and optimized the prompts through follow-up questions, examples, and other methods.”
Teaching Nursing Students Effective Artificial Intelligence Prompt Engineering
Bester, M. E., & Zeigler, K. (2025) · Nurse Educator · doi:10.1097/nne.0000000000001969 · Cited by 4
Mastering the Art of Prompting
Ravikumar, R. N., Aarthi, S., Yulduz, U., Atamuratova, Z., Moxinur, S., & Shaturaev, J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0250-8.ch001 · Cited by 4
Sun, G. H. (2025) · Journal of the American Association of Nurse Practitioners · doi:10.1097/jxx.0000000000001170 · Cited by 4
Artificial Intelligence Meets Accreditation to Modernize Nursing Education
Chicca, J., Shellenbarger, T., & Chicca, D. (2025) · Nurse Educator · doi:10.1097/nne.0000000000001975 · Cited by 3
Robison, E. S., Cooney, T. A., Schwaab, T., & Rahman, S. (2025) · Clinical Simulation in Nursing · doi:10.1016/j.ecns.2025.101822 · Cited by 3
Creativity in Health Sciences Education
Matahela, V. E. (2025) · Advances in medical education, research, and ethics (AMERE) book series · doi:10.4018/979-8-3693-4334-0.ch002 · Cited by 3
Qi, S., & Qiu, H. (2024) · Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence · doi:10.1145/3724504.3724605 · Cited by 2
“[ 13 ][ 14 ]”
Navigating nursing student use and suspected misuse of artificial intelligence
Chicca, J., & Shellenbarger, T. (2025) · Teaching and Learning in Nursing · doi:10.1016/j.teln.2025.11.020 · Cited by 2
Gotlib, J., Cieślak, I., Jaworski, M., & Pańczyk, M. (2025) · Pielegniarstwo XXI wieku / Nursing in the 21st Century · doi:10.12923/pielxxiw-2025-0002 · Cited by 1
Impact of generative artificial intelligence on workload, efficiency and labour productivity
Caamaño-Gordillo, D., Mula, J., & de la Torre, R. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.09.237 · Cited by 3
Jiménez, C. R., Robles, A. S., Lorenzo-Martín, M. E., & Fernández-Fernández, C. R. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-7922-7.ch006 · Cited by 1
Application of Artificial Intelligence in Nursing: A Bibliometric Analysis of Global Research Trends
Suen, L. K. P., Zhou, J., Chen, S., He, Q., Tsang, M. C.-M., Leung, W. K. C., & Lam, S. C. (2026) · Healthcare · doi:10.3390/healthcare14040460 · Cited by 2
“Secondly, the interdisciplinary nature of AI research means that it often intersects with multiple fields, such as engineering [45], health informatics [38], psychology [46] and public health [47], leading to a broad audience and great citation potential.”
Prompt Engineering in Higher Education: A Systematic Review of Current Applications and Challenges
Zhang, A., & Tang, W. F. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-95-4423-3_55 · Cited by 1
Using Artificial Intelligence to Enhance Evidence Search and Synthesis
Bell, S., Ackerman, M., Buck, J., Frueh, A., Maitland, J., Piotrowski, K., Sokol, M., Tussing, T. E., Zellefrow, C. G., & McNett, M. (2026) · AJN American Journal of Nursing · doi:10.1097/ajn.0000000000000288 · Cited by 1
“2,6,7 Several academic search platforms such as PubMed and EBSCO have incorporated AI-enhanced search features to reduce these problems.”
Amankwaa, I., Ekpor, E., Cudjoe, D., Kobiah, E., Fuseini, A.-K. J., Diebieri, M., Gyamfi, S., & Brownie, S. M. (2025) · Research Square · doi:10.21203/rs.3.rs-6493164/v1
Bridging Pedagogy and Practice: Case-Based Learning and Mind Maps
Kleinhans, K. A., Richards, C., & Desomma, A. (2025) · Perspectives of the ASHA Special Interest Groups · doi:10.1044/2025_persp-24-00137
Wu, L., Jiang, X., Liu, J., Dai, S., Mao, X., & Cha, R. (2025) · doi:10.2196/preprints.78816
When Assessment Theory Meets Generative AI: Reimagining SBA Design in Medical Education
Al-Shawee, N., McElvaney, G., Strawbridge, J., & Spooner, M. (2026) · Perspectives on Medical Education · doi:10.5334/pme.2033 · Cited by 1
AI as a Creative Partner in Instructional Design: Strategies for Effective Course Development
Huck, C., & Cisneroz, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-09905-1_20
A Framework for Creating Effective Prompts Using Prompt Engineering
Choi, J., Hicks, D., Shakya, S., Woo, S., Chen, Y.-J., & Kim, Y. (2026) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001532
Importance of AI Chatbot in Nursing Education: A Systematic Review
Ghaffar, T., Balakrishnan, R., & Javaid, M. A. A. (2026) · Biological and Clinical Sciences Research Journal · doi:10.54112/bcsrj.v7i4.2271
Rutherford-Hemming, T. (2026) · Journal of Professional Nursing · doi:10.1016/j.profnurs.2026.08.027
Synthesising Generative Artificial Intelligence Competency Domains for Medical Education Faculty
Meyers, P., & Miller, C. (2026) · The Clinical Teacher · doi:10.1111/tct.70532 · Cited by 1
Prompt Engineering as Distributed Cognition and Epistemic Infrastructure in Higher Education
Alam, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8332-3.ch004
Metadata
- Authors
- Sun, G. H.
- Affiliations
- Texas Tech University (US)
- Corresponding author
- Sun, Grace H.
- Venue
- Nurse Educator
- Published
- 2024-07-05
- Volume
- 49
- Issue
- 6
- Pages
- 293-299
- ISSN
- 0363-3624 (ISSN-L)
- 1538-9855
- OpenAlex
- W4400348106
- Cited by
- 36
- References
- 20
- Citations by year
- 2026: 11 · 2025: 22 · 2024: 3
- FWCI
- 3.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nurse Educator (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- prompt engineering, generative AI, ChatGPT, nursing education, nursing faculty, AI in teaching, personalized learning, learning outcomes
- MeSH terms
- Artificial Intelligence, Education, Nursing, Faculty, Nursing, Humans, Learning, Nursing Education Research, Nursing Evaluation Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
