Scenario-Based Forecasting of ChatGPT's Role as a Research Assistant in Nursing by 2030
- PMID: 40640994
- DOI: 10.1111/inr.70055
Scenario-Based Forecasting of ChatGPT's Role as a Research Assistant in Nursing by 2030
Abstract
Aim: To explore the future role of ChatGPT in nursing research by 2030 and provide a strategic framework for its responsible integration, addressing key opportunities and challenges.
Background: Artificial intelligence (AI) tools, such as ChatGPT, offer significant potential to enhance productivity, foster innovation, and facilitate interdisciplinary collaboration in nursing research. However, they also pose challenges related to ethical governance, equitable access, and risks of overreliance. With AI technologies becoming more prevalent, a proactive approach is essential for ensuring responsible adoption and maximizing their potential benefits.
Method: A structured five-phase scenario planning approach was used: (1) defining the scenario field, (2) identifying key drivers, (3) generating scenarios, (4) evaluating scenarios, and (5) transferring actionable insights. Eleven experts were recruited, of whom almost 50% were from Saudi Arabia. Expert input was gathered to prioritize critical drivers based on impact and uncertainty. Two key drivers, ethical accountability and cross-disciplinary collaboration, were used to construct a 2 × 2 matrix, resulting in four distinct scenarios.
Findings: The four scenarios include: (1) Ethical innovation, where robust governance and high collaboration foster trust and innovation; (2) Fragmented progress, where weak accountability reduces credibility despite high collaboration; (3) Ethical fortress, where strong governance but low collaboration slows progress; and (4) Stagnant patchwork, where weak governance and low collaboration hinder growth. Ethical innovation was identified as the most desirable scenario. Three cross-cutting themes-ethical authorship, trust, and AI overreliance-emerged as critical for evaluating scenario implications.
Implications for nursing and/or health policy: This study provides actionable strategies for nursing researchers and policymakers, including strengthening governance, fostering collaboration, promoting AI literacy, and ensuring equitable access. Addressing concerns around authorship transparency, trust in AI, and overreliance is essential for leveraging AI's potential while safeguarding research integrity and equity.
Keywords: AI; ethical; guidelines; nursing; planning; research; scenario.
© 2025 International Council of Nurses.
Similar articles
-
Stench of Errors or the Shine of Potential: The Challenge of (Ir)Responsible Use of ChatGPT in Speech-Language Pathology.Int J Lang Commun Disord. 2025 Jul-Aug;60(4):e70088. doi: 10.1111/1460-6984.70088. Int J Lang Commun Disord. 2025. PMID: 40627744 Review.
-
Response to "Letter to the Editor-Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation".Aesthetic Plast Surg. 2025 May;49(9):2638-2639. doi: 10.1007/s00266-024-04210-y. Epub 2024 Jul 8. Aesthetic Plast Surg. 2025. PMID: 38977450
-
Redefining Mentorship in Medical Education with Artificial Intelligence: A Delphi Study on the Feasibility and Implications.Teach Learn Med. 2025 Jun 18:1-11. doi: 10.1080/10401334.2025.2521001. Online ahead of print. Teach Learn Med. 2025. PMID: 40534163
-
Ethical Implications of Artificial Intelligence in Vaccine Equity: Protocol for Exploring Vaccine Distribution Planning and Scheduling in Pandemics in Low- and Middle-Income Countries.JMIR Res Protoc. 2025 Jul 9;14:e76634. doi: 10.2196/76634. JMIR Res Protoc. 2025. PMID: 40633920 Free PMC article.
-
Navigating ethical considerations in the use of artificial intelligence for patient care: A systematic review.Int Nurs Rev. 2025 Sep;72(3):e13059. doi: 10.1111/inr.13059. Epub 2024 Nov 15. Int Nurs Rev. 2025. PMID: 39545614
References
-
- Abràmoff, M. D., C. Roehrenbeck, and S. Trujillo. 2022. “A Reimbursement Framework for Artificial Intelligence in Healthcare.” NPJ Digital Medicine 5, no. 1: 1–9. https://doi.org/10.1038/s41746‐022‐00621‐w.
-
- Akins, R. B., H. Tolson, and B. R. Cole. 2005. “Stability of Response Characteristics of a Delphi Panel: Application of Bootstrap Data Expansion.” BMC Medical Research Methodology 5: 1–12. https://doi.org/10.1186/1471‐2288‐5‐37.
-
- Akinrinola, O., C. C. Okoye, O. C. Ofodile, and C. E. Ugochukwu. 2024. “Navigating and Reviewing Ethical Dilemmas in AI Development: Strategies for Transparency, Fairness, and Accountability.” GSC Advanced Research and Reviews 18, no. 3: 050–058. http://doi.org/10.30574/gscarr.2024.18.3.0088.
-
- Ashok, M., R. Madan, A. Joha, and U. Sivarajah. 2022. “Ethical Framework for Artificial Intelligence and Digital Technologies.” International Journal of Information Management 64: 102524. https://doi.org/10.1016/j.ijinfomgt.2021.102524.
-
- Cantero Gamito, M., and C. T. Marsden. 2024. “Artificial Intelligence Co‐regulation? The Role of Standards in the EU AI Act.” International Journal of Law and Information Technology 32, no. 1: eaae011. https://doi.org/10.1093/ijlit/eaae011.
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources