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ChatGPT provides quick access to information, research support, and study materials, but concerns remain regarding its reliability, accuracy, and inability to offer personalized care principles essential in nursing. Although previous studies show its high accuracy in clinical responses, over-reliance on AI-generated medical information necessitates cautious use. The study will explore both the benefits and limitations of ChatGPT in nursing education, particularly in hypertension learning.
The use of artificial intelligence (AI) tools in education is increasing rapidly. ChatGPT, developed by OpenAI, is an AI-based chatbot that provides an interactive learning environment. It generates fluent and knowledge-based responses based on books, online sources, and articles published until 2021. The widespread adoption of ChatGPT across various fields has sparked debates about its role and limitations. In nursing education, students frequently use ChatGPT for quick access to information, research support, and exam preparation. However, concerns regarding its reliability arise due to the unknown sources of its responses and the potential for misinformation. ChatGPT also has limitations in interpreting complex, context-dependent answers and lacks the ability to apply the principle of individualized care, which is fundamental in nursing practice.
Studies have demonstrated ChatGPT's varying performance in the healthcare field. Hypertension, a chronic disease affecting over a billion people worldwide, is a critical topic for nursing students, as their understanding of the condition can positively impact patient care. Previous research has shown that ChatGPT provides clinically appropriate answers to hypertension-related questions with a high accuracy rate of 92.5%. Additionally, the GPT-4 version of ChatGPT correctly answered over 86% of the questions in the United States Medical Licensing Examination (USMLE).
This randomized controlled study aims to assess the effectiveness of ChatGPT in teaching hypertension to nursing students while also evaluating their levels of AI-related anxiety and cognitive load. Given the increasing presence of AI tools in education, understanding both their advantages and limitations is crucial for their optimal integration into nursing education.
The study population consists of students enrolled in the nursing program at a private university. The study aims to reach the entire population, specifically 96 students who have completed the Internal Medicine Nursing course. Students meeting the inclusion criteria will be informed about the study and invited to participate. Volunteers will complete an Introductory Information Form and be randomly assigned to intervention (ChatGPT) or control groups in a 1:1 ratio using computer-based randomization (48 students per group). The intervention group will answer questions from the Hypertension Prevention Attitude Scale using ChatGPT, while the control group will use traditional methods. Afterward, both groups will complete the Artificial Intelligence Anxiety Scale and Cognitive Load Scale, concluding data collection. All collected data will be analyzed using the SPSS for Windows 22.0 statistical software package.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| ChatGPT Group | Experimental | Students in the intervention group will answer the questions from the Hypertension Prevention Attitude Scale using ChatGPT. |
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| Control Group | No Intervention | In the control group students will respond Hypertension Prevention Attitude Scale using traditional methods. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| ChatGPT | Other | Students in the intervention group will answer the questions from the Hypertension Prevention Attitude Scale using ChatGPT. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Hypertension Prevention Attitudes Scale | The scale consists of 26 items and subdimensions, including protection and control, habits and lifestyle, nutrition attitudes, mental state and physical activity, and disease and risk knowledge. The items are rated on a five-point Likert scale, ranging from "Strongly Disagree" to "Strongly Agree." The scale scores can range from 26 to 130. There is a positive relationship between the scale scores and attitudes toward hypertension prevention. The Cronbach's alpha value of the scale is 0.91. | At the beginning of the study first admission. |
| Artificial Intelligence Anxiety Scale | The Artificial Intelligence Anxiety Scale (AIAS) was developed by Wang and Wang (2019) and adapted into Turkish by Akkaya et al. (2021). The scale is a 5-point Likert type, consisting of 21 items and 4 factors. These factors are: Learning, Job Change, Socio-technical Blindness, and Artificial Intelligence Structuring. The minimum score that can be obtained from the scale is 21, and the maximum score is 105. A higher score indicates a higher level of AI anxiety. The Cronbach's alpha coefficient of the scale is reported to be 0.95. | Immediately after the intervention (answering the scale questions) |
| Cognitive Load Scale | The scale developed by Paas and Van Merriënboer (1993) aims to measure the cognitive load of students during individual study processes. It was adapted into Turkish by Kılıç and Karadeniz (2004). The scale is a symmetric, Likert-type scale with scores ranging from 1 to 9. It allows the assessment of the effort a student exerts during their individual learning process. According to the scale, cognitive load increases from 1 to 9. Scores between 1-4 are considered low cognitive load, while scores between 5-9 are considered high cognitive load. Paas and Van Merriënboer (1993) reported an internal consistency coefficient of 0.82 for the scale, while Kılıç and Karadeniz (2004) calculated an internal consistency coefficient of 0.90 for the Turkish version. | Immediately after the intervention (answering the scale questions) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Nursemin Unal, Assoc. Prof. | Contact | +905077433629 | nurse_unal@hotmail.com | |
| Nilay Bektaş Akpınar, Assist.Prof. | Contact | +905319920260 | nilaybektas88@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Nursemin UNAL, Assoc. Prof. | Ankara University | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | Alkhaqani, A. L. (2023). Can ChatGPT help researchers with scientific research writing. Journal of Medical Research and Reviews, 1(1), 9-12. https://doi.org/10.5455/JMRR.20230626013424 | ||
| 37130197 | Background | Branum C, Schiavenato M. Can ChatGPT Accurately Answer a PICOT Question? Assessing AI Response to a Clinical Question. Nurse Educ. 2023 Sep-Oct 01;48(5):231-233. doi: 10.1097/NNE.0000000000001436. Epub 2023 Apr 28. | |
| 37101311 |
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| ID | Term |
|---|---|
| D006973 | Hypertension |
| ID | Term |
|---|---|
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
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| ID | Term |
|---|---|
| D035061 | Control Groups |
| ID | Term |
|---|---|
| D015340 | Epidemiologic Research Design |
| D004812 | Epidemiologic Methods |
| D008919 | Investigative Techniques |
| D012107 | Research Design |
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Students will be randomly assigned to the intervention (ChatGPT) and control groups in a 1:1 allocation ratio. Group assignments will be determined using a computer-based randomization table, with 48 students in the intervention group and 48 in the control group, placed in separate classrooms. Students in the intervention group will answer the questions from the Hypertension Prevention Attitude Scale using ChatGPT, while those in the control group will respond using traditional methods.
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Outcome assessor blind
| Background |
| Abdulai AF, Hung L. Will ChatGPT undermine ethical values in nursing education, research, and practice? Nurs Inq. 2023 Jul;30(3):e12556. doi: 10.1111/nin.12556. Epub 2023 Apr 26. No abstract available. |
| Background | Goktas, P., Kucukkaya, A., & Karacay, P. (2024). Utilizing GPT 4.0 with prompt learning in nursing education: A case study approach based on Benner's theory. Teaching and Learning in Nursing, 19(2), e358-e367. |
| 36981544 | Background | Sallam M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare (Basel). 2023 Mar 19;11(6):887. doi: 10.3390/healthcare11060887. |
| Background | Wang, Y. Y. & Wang, Y. S. (2019). Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments, 1-16. https://doi.org/10.1080/10494820.2019.1674887 |
| D008722 | Methods |