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AI or nay? Evaluating the potential use of ChatGPT (Open AI) and Perplexity AI in undergraduate nursing research: An exploratory case study

Guardado en:
Udgivet i:Nurse Education in Practice vol. 87 (Aug 2025), p. 104488-104499
Hovedforfatter: Ng, Jamie Qiao Xin
Andre forfattere: Chua, Joelle Yan Xin, Choolani, Mahesh, Li, Sarah W.L., Foo, Lin, Pereira, Travis Lanz-Brian, Shorey, Shefaly
Udgivet:
Elsevier Limited
Fag:
OpenAI
Students
Blooms taxonomy
Evidence-based practice
Cognitive ability
Performance evaluation
Chatbots
Cognition & reasoning
Medical education
Validity
Classification
Hypotheses
Teaching
Critical thinking
Learning
Nursing education
Nursing
Answers
Case studies
Skills
Research methodology
Artificial intelligence
Evidence-based nursing
Large language models
Literacy
College students
Research
Student attitudes
Questions
Human-computer interaction
Nurses
Academic achievement
Concepts
Learning outcomes
Language modeling
Researchers
Nursing Research
Taxonomy
Thinking Skills
Evidence Based Practice
Evaluative Thinking
Content Validity
Undergraduate Students
Research Skills
Outcomes of Treatment
Interrater Reliability
Learning Experience
Nursing Students
Reference Materials
Feedback (Response)
Outcomes of Education
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Resumen:Aims This study aimed to evaluate the performance of publicly available large language models (LLMs), ChatGPT-4o, ChatGPT-4o Mini and Perplexity AI, in responding to research-related questions at the undergraduate nursing level. The evaluation was conducted across different platforms and prompt structures. The research questions were categorized according to Bloom’s taxonomy, to compare the quality of AI-generated responses across cognitive levels. Additionally, the study explored the perspectives of research members on using AI tools in teaching foundational research concepts to undergraduate nursing students. Background Large Language Models (LLMs) could help nursing students learn foundational research concepts but their performance in answering research-related questions has not been explored. Design An exploratory case study was conducted to evaluate the performance of ChatGPT-4o, ChatGPT-4o Mini and Perplexity AI in answering 41 research-related questions. Methods Three different prompts (Prompt-1: Unstructured with no context; Prompt-2: Structured from professor’s perspective; Prompt-3: Structured from student’s perspective) were tested. A 5-point Likert-type valid author-developed scale was used to assess all AI-generated responses across six domains: Accuracy, Relevance, Clarity & Structure, Examples Provided, Critical Thinking and Referencing. Results All three AI models generated higher-quality responses when structured prompts were used compared with unstructured prompts and responded well across the different Bloom’s taxonomy levels. ChatGPT-4o and ChatGPT-4o Mini performed better at answering research-related questions than Perplexity AI. Conclusion AI models hold promise as supplementary tools for enhancing undergraduate nursing students’ understanding of foundational research concepts. Further studies are warranted to evaluate their impact on specific research-related learning outcomes within nursing education.
ISSN:1471-5953
1873-5223
DOI:10.1016/j.nepr.2025.104488
Fuente:Sociology Database