Comparative Analysis of Nursing Care Plans Produced by Artificial Intelligence Models (ChatGPT, Gemini, Deepseek) in Terms of Readability, Reliability and Quality

Comparative Analysis of Nursing Care Plans Produced by Artificial Intelligence Models (ChatGPT, Gemini, Deepseek) in Terms of Readability, Reliability and Quality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Analysis of Nursing Care Plans Produced by Artificial Intelligence Models (ChatGPT, Gemini, Deepseek) in Terms of Readability, Reliability and Quality Mucahide GOKCEN GOKALP, Sebnem CINAR YUCEL This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8080438/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background While AI chatbots make healthcare information more accessible, there is still limited research on the readability, trustworthiness, and overall quality of the nursing care plans they generate. Purpose The research aims to investigate how AI-driven chatbots like ChatGPT, Gemini, and DeepSeek generate nursing care plan texts in terms of readability, reliability, and overall quality. Methods A total of 30 nursing diagnoses were randomly selected from the NANDA 2021–2023 taxonomy. For each diagnosis, care plans were generated by three different AI chatbots, yielding 90 texts in total. The generated plans were evaluated through a descriptive criteria form , the DISCERN tool for health information quality, and multiple readability measures (FRES, SMOG, Gunning Fog Index, and Flesch-Kincaid Grade Level). Results The analysis revealed that the nursing care plans generated by ChatGPT, Gemini, and DeepSeek had readability scores significantly above the standard sixth-grade level (P < .001). DISCERN analysis yielded average scores of 57.41 ± 5.9 for ChatGPT, 58.41 ± 4.8 for Gemini, and 56.51 ± 6.8 for DeepSeek, reflecting moderate reliability overall. Among the generated texts, 27 (90%) offered information rated as moderate in quality. Moreover, the inclusion of verifiable references showed a statistically significant positive relationship with both reliability and quality measures (P < .05). Conclusion Artificial intelligence chatbots cannot replace complete nursing care plans. For AI-driven tools, it is advised to improve the clarity of the generated content, include reliable references, and have the material reviewed by professionals. Chatbot ChatGPT Gemini DeepSeek Nursing care Nursing care plan Figures Figure 1 1. Introduction The implementation of technological innovations in healthcare reduces the workload of nurses and physicians while also transforming the organization of care processes. Building on this foundation, artificial intelligence (AI) applications are increasingly being used for clinical documentation, decision support, and patient education, thanks to their natural language processing (NLP) capabilities ( 1 ). AI systems based on large language models (LLMs) perform functions such as strengthening clinical decision support processes in healthcare, preparing patient education materials, and contributing to the development of nursing care plans; in this context, the preparation of nursing care plans, which are critical in the nursing process, emerges as one of the prominent application areas of these systems ( 1 ). Artificial intelligence (AI) refers to systems that enable computers to use thinking and learning capabilities modeled on the human mind; these include decision-making, learning from experiences, analyzing language and visual data, and generating solutions to problems ( 2 ) Artificial intelligence, with its capacity to create significant changes in nursing practice, has been a driving force behind notable developments in the field. AI encompasses various applications in nursing practice, including both hardware-based solutions and digital platforms ( 3 ). These technological combinations not only enhance the efficiency of nursing care but also provide new opportunities in diagnostic and care practices ( 1 , 3 ). AI systems based on large language models (LLMs) perform functions such as enhancing clinical decision support processes in healthcare, preparing patient education materials, and contributing to the development of nursing care plans; in this context, the preparation of care plans, which are critical in the nursing process, emerges as one of the prominent application areas of these systems. However, to ensure the reliability of the generated content, it must be reviewed and supervised by experienced healthcare professionals ( 1 ). The use of AI-supported models in the preparation of nursing care plans can optimize the planning process and enhance standardization in practice through the integration of scientific resources, clinical guidelines, and patient information ( 4 ). Their capacity to analyze patient-derived data, identify nursing diagnoses, suggest necessary interventions, and predict care outcomes allows nurses to reduce their clinical workload and dedicate more time to direct patient interaction. Thus, the integration of AI into nursing care plan development is considered an important step that both facilitates workload management and strengthens evidence-based nursing practice. However, rapid production alone is not sufficient; the generated content must also be rigorously evaluated in terms of readability, reliability, and quality ( 5 ). Readability refers to how easily a text can be comprehended and is crucial for healthcare providers as well as patients. The complexity of texts in the healthcare field is influenced by factors such as the use of medical terminology, sentence structure, and language simplicity. Studies indicate that the majority of patient education materials are written above the suggested sixth-grade reading level, which can make them challenging for patients to comprehend ( 6 ). Similarly, high readability in nursing care plans prepared under heavy workloads can lead to misunderstandings and errors in practice; therefore, these plans must undergo review by experienced supervisors. Otherwise, they may even result in differences in interpretation among colleagues ( 7 ). In nursing care plans generated by artificial intelligence, complex and technical content has also been observed to potentially limit the effective use of the text. Reliability is a critical measure that determines the effective use of artificial intelligence applications in healthcare. Some AI models can produce information that is false or unverified, a phenomenon known as “hallucination”; such erroneous outputs can negatively impact healthcare professionals’ clinical decisions and jeopardize patient safety ( 8 ). Therefore, in the preparation of nursing care plans, it is essential that the content is based on scientific evidence and rigorously reviewed for accuracy and consistency to ensure reliability. Moreover, a reliable care plan fosters trust in both clinical practice and multidisciplinary communication, contributing to the improvement of care quality ( 9 ). Quality in healthcare texts is not limited to readability and accuracy but also encompasses clinical applicability, measurability, and alignment with patients’ individual needs ( 10 ). When evaluating the quality of nursing care plans, it is important to assess whether the plans align with the SMART criteria (Specific, Measurable, Achievable, Realistic, Time-bound) for the defined care goals and whether they are prepared and implemented in an integrated and practical manner within the nursing process. These criteria directly affect the effectiveness of care, goal attainment, and patient safety. Moreover, quality assessment goes beyond content accuracy to include the applicability, measurability, and consistency of care plans within the clinical context. For this purpose, standardized tools such as DISCERN can be used to systematically evaluate the quality of both written health materials and nursing care plans, providing reliable data for clinical practice and educational processes ( 11 ). In recent years, various artificial intelligence models have been developed and implemented in healthcare applications. Among them, advanced large language models like OpenAI’s ChatGPT and Google DeepMind’s Gemini, and the China-based DeepSeek have gained particular attention ( 12 ). ChatGPT is one of the most studied models due to its widespread use in both academic and clinical settings ( 13 ). The Gemini model stands out for its ability to process text, visual, and tabular data simultaneously ( 14 ). The DeepSeek model is notable for its high processing speed, cost effectiveness, and large number of parameters ( 15 ). The performance of these models in generating nursing care plans represents a timely and important area of research for both educational and clinical practice contexts. While some studies in the literature have examined ChatGPT’s performance in developing nursing care plans, evaluations of Gemini and DeepSeek in this context remain insufficient. Moreover, existing research generally focuses on only one aspect—readability, reliability, or quality—without addressing these criteria comprehensively. The perceptions of nurses and other healthcare professionals regarding AI-generated nursing care plans have been investigated to a limited extent, leaving questions about the clinical validity and reliability of these plans largely unexplored ( 16 ). This study has been designed to address gaps in the literature, aiming to comparatively evaluate nursing care plans produced by ChatGPT, Gemini, and DeepSeek in terms of readability, reliability, and quality criteria ( 7 ). The data obtained from the research will highlight the advantages and limitations of various AI systems, enabling an assessment of their applicability and potential in healthcare. Furthermore, the study aims to provide scientific insights that contribute both to the improvement of clinical nursing care practices and to the effective integration of healthcare technologies into nursing applications. 2. Materials and methods 2.1.Research Design and Sample This research seeks to examine the quality of AI-generated texts from chatbots such as ChatGPT, Gemini, and DeepSeek. The study’s sampling framework is based on the Central Limit Theorem, which states that with a sufficiently large number of independent random variables, the mean tends to follow a Gaussian distribution, thereby allowing reliable estimation of population parameters ( 17 ) . The statistical power and generalizability of the study’s results are supported by the Central Limit Theorem. As noted by the theorem and emphasized by Hair et al. (2010), a sample size of 30 units allows for more reliable estimation of population parameters ( 18 ). In line with this theoretical framework, our analysis encompasses a total of 90 nursing care plans—30 texts generated by each of the ChatGPT, Gemini, and DeepSeek chatbots. This approach was adopted to maximize the representativeness of the findings. 2.2.Selection of Topics The texts produced by AI tools (ChatGPT, Gemini, and DeepSeek) were derived from topics chosen from the 2021–2023 NANDA International taxonomy, covering a total of 267 unique nursing diagnoses ( 19 ). With all 267 nursing diagnoses listed, a randomization table was produced using http://www.random.org (refer to Supplementary Digital Content Randomization Table.docx). This method allowed 30 diagnoses to be chosen at random, ensuring equal selection probability for each and preserving the fairness of the sampling procedure ( 20 , 21 ). Each selected diagnosis was then input into three AI chatbots to create care plans, producing a total of 90 texts. 2.3.Data Collection A structured data collection protocol was implemented in this study to comparatively evaluate the performance of current AI models in producing texts consistent with academic conventions. The data collection occurred on July 8, 2025, using the web interfaces of the latest versions of ChatGPT ( 22 ), Gemini ( 23 ), and DeepSeek ( 24 ). All platforms were accessed via Google Chrome (version 109.0.5414.119) on a Windows operating system to ensure reproducibility. Including such technical details is vital for validating and verifying the scientific reliability of the findings ( 25 ). For each nursing diagnosis selected at random, the AI chatbots (ChatGPT, Gemini, and DeepSeek) received the prompt: ‘Prepare a text on -NANDA DIAGNOSIS- and the corresponding nursing care plan, including as many references and in-text citations as you can. The aim of this prompt was twofold: to generate meaningful content and to evaluate the models’ capacity to accurately include academic references ( 7 ). Highlighting “nursing care plan” aimed to ensure that the outputs were clinically applicable and logically organized ( 26 , 27 ). Investigating AI models’ competence in producing texts with academic citations is a growing area of research interest ( 28 ). The texts produced by ChatGPT, Gemini, and DeepSeek were systematically saved by the investigators to enable detailed assessment and further analysis during later stages of the study. 2.4.Data Collection Tools 2.4.1. Descriptive Information Assessment The descriptive data collection form comprised 12 items covering characteristics such as word and paragraph count, text similarity score (measured with iThenticate and Turnitin), number of references, year of publication, type of document, and access options ( 29,30). 2.4.2.Readability assessment Two online platforms were used to examine the readability of nursing care plan texts generated by AI chatbots (ChatGPT, Gemini, and DeepSeek). ReadabilityFormulas.com (Calculator 1) and Online-Utility.org (Calculator 2) employ various readability formulas to systematically evaluate the texts ( 31 – 33 ). Seven readability formulas were applied in the analysis: To measure readability, the study applied established formulas such as Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFOG), Simple Measure of Gobbledygook (SMOG), Automated Readability Index (ARI), Coleman-Liau Index (CLI), and Linsear Write (LW). Each text’s readability was computed using these formulas, with results reported as medians accompanied by minimum and maximum values. A readability threshold of 80 or higher was considered acceptable for FRES, while a score of 6 or higher was used for the other six formulas ( 34 , 35 ) Two expert nursing researchers (M.G. and S.C.Y.) carried out independent assessments of the AI-generated texts, focusing on readability, standard, and dependability. The ultimate scores were derived by combining the evaluations from both researchers and computing the average. The calculation methods of the seven applied readability formulas and all related information are shown in Fig. 1 (Fig. 1 . Calculation Methods and Related Information of the Seven Applied Readabilityt). Reliability and Quality Assessment Developed through a partnership between the British Library and the NHS, DISCERN provides a standardized method for assessing the reliability of health-related information available on the internet ( 11 ). Comprising 16 questions across three sections, DISCERN assesses reliability (first eight questions), treatment information quality (next seven questions), and overall quality (final question). Items are rated on a 1-to-5 scale, with the cumulative score ranging from 16 to 80. Based on the scoring criteria, total ratings below 40 are classified as poor, ratings between 40 and 79 as moderate, and ratings of 80 or above as high quality ( 36 ). 2.4. Statistical analysis SPSS version 25 (IBM Corp., Armonk, NY) was employed for all sta s cal analyses. Descriptive statistics comprised counts, percentages, average values, standard deviations, and ranges, while relationships between numerical variables and DISCERN scores were examined using Spearman’s correlation. Turni n and iThen cate soware were u lized to assess text similarity. Con nuous variables were compared using the Mann-Whitney U and Wilcoxon tests. The reliability of readability calculators was examined via intraclass correla on coefficients (ICC), and inter-observer agreement was assessed using Cohen’s κ. A P-value < .05 was considered sta s cally significant. 2.5. Ehical Considerations Researchers utilized AI platforms provided as a ‘free research preview,’ which did not require formal ethical approval or institutional authorization. Because the data are transparent and verifiable, the study clearly details methods for AI usage, including the process of choosing, randomizing, and organizing diagnoses. 3. Results Two researchers independently evaluated the outputs generated by ChatGPT, Gemini, and DeepSeek, which were based on randomly chosen nursing diagnoses. The evaluations demonstrated strong consistency between the researchers, with a Cohen’s Kappa of 0.827, indicating reliable assessments. The mean number of words was 495.3 ± 134.4 for ChatGPT, 531 ± 41.1 for Gemini, and 497.7 ± 70.1 for DeepSeek, with DeepSeek texts generally being shorter, as shown by the 374–552 word range. In the source analysis, it was found that most of the publica ons cited in the texts were fic onal and not available in academic databases. Tradi onal plagiarism detec on tools (iThen cate, Turni n) failed to iden fy this, showing a 0% similarity rate. However, as a cri cal finding, Turni n’s AI detec on feature correctly iden fied all 90 texts as AI-generated with 100% accuracy (Table 1 . Descriptive Information Form). This result highlights the limita ons of tradi onal plagiarism tools in detec ng original yet fic onal content produced by ar ficial intelligence. Table 1 Descriptive Information Form ChatGBT Gemini Deepseek Variables Min-Max Mean ± SD Min-Max Mean ± SD Min-Max Mean ± SD Publication Years of References 1984–2023 2018 ± 5,05 1979–2023 2016 ± 7,00 1980–2023 2017 ± 6,05 Number of References 4–7 5,6 ± 1,08 3–8 4.33 ± 2,06 3–9 4,36 ± 1,91 References that Are Accessible 0–4 0.63 ± 1,01 0–5 0.53 ± 1,01 0–3 0.63 ± 1,01 References from the Web 1–4 1.14 ± 1.88 1–4 2.14 ± 1.58 1–7 2.14 ± 1.88 References from the Web That Are Accessible 1–3 0.25 ± 0.58 1–3 0.27 ± 0.58 1–4 0.25 ± 1.58 References Cited from Journals 1–6 2.25 ± 1.25 1–6 2.35 ± 1.25 1–3 2.25 ± 0.25 Number of Available Journal Article References 1–3 0.23 ± 0.58 1–3 0.23 ± 0.60 1–3 0.23 ± 0.58 References from Books 1–2 1.46 ± 0.36 1–2 1.56 ± 0.32 1–2 1.66 ± 0.29 Book References That Are Accessible 1–2 0.25 ± 0.59 1–2 0.25 ± 0.59 1–2 0.25 ± 0.59 Word Count 402–741 495.3 ± 134.4 487–587 531 ± 41.1 374–552 497,7 ± 70.1 Number of Paragraphs 4–9 5.65 ± 1.00 4–8 5.65 ± 1.00 4–6 5.65 ± 1.00 iThenticate Similarity Rate 0 0 0 0 0 0 Turnitin Similarity Rate 0 0 0 0 0 0 Turnitin AI Similarity Rate 100 100 ± 0.00 100 100 ± 0.00 100 100 ± 0.00 3.1. Readability Evaluation of AI Chatbots (ChatGPT, Gemini, and DeepSeek) Based on the Mean Scores of Calculators 1 and 2 Comparison of texts generated by ChatGPT, Gemini, and DeepSeek with the sixth-grade reading standard indicated statistically significant differences across all readability metrics (P < .001). These findings suggest that the texts exceed the sixth-grade reading level. Additionally, significant differences were found when comparing each calculator’s output with the combined average (P < .001) (Tables 2 – 3 ). (Table 2 . Readability scores of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and analysis of the text content at a 6th-grade reading level [median (minimum–maximum)], were performed using Calculator 1. ( https://readabilityformulas.com/free-readability-formula-tests.php ), Table 3 . Readability scores of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and analysis of the text content at a 6th-grade reading level [median (minimum–maximum)], were performed using Calculator 2. ( https://readabilityformulas.com/free-readability-formula-tests.php ) Table 2 Readability scores of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and analysis of the text content at a 6th-grade reading level [median (minimum–maximum)], were performed using Calculator 1. ( https://readabilityformulas.com/free-readability-formula-tests.php ) Calculator 1 statistics ChatGPT Google Gemini Deepseek Chat GPT C6thGRL (P)*1 Gemini C6thGRL (P)*1 Deepseek C6thGRL (P)*1 Between Chatgpt and Gemini2 Between Chatgpt and deepseek2 Between gemini and deepseek2 FRES 31.35 (9–55) 40.50 (22–55) 36 (10–70) < .001 < .001 < .001 .005 .830 .026 GFOG 17.75(13.40–25.65) 15.78 (12–25.09) 20.47 (15.60–54.60) < .001 < .001 < .001 .007 .001 < .001 FKGL 14.29 (11.74–20.72) 11.23 (7.79–15.99) 15.99 (13.13–49.57) < .001 < .001 < .001 .001 .001 < .001 CLI 15.17 (12.37–21.42) 13.71 (11.08–16.28) 15.87 (12.31–18.63) < .001 < .001 < .001 .008 .213 < .001 SMOG 12.85(9.57–18.09) 11.67 (8.67–16.42) 14.58 (11.57–32.37) < .001 < .001 < .001 .005 .002 < .001 ARI 16.52 (12.58–23.45) 14.47 (10.14–26.16) 18.71 (14.15–60.62) < .001 < .001 < .001 .015 .001 < .001 LW 16.27 (12.44–22.38) 14.32 (7.42–31.10) 23.41 (14.50–85.25) < .001 < .001 < .001 .017 < .001 < .001 Grade level 15.00 (12.00–20.00) 13.00 (10.00–20.00) 18.00 (13.00–45.00) < .001 < .001 < .001 .022 .005 < .001 Reading level n (%) n (%) n (%) Difficult to read 1 (4.6) 5 (22.7) 0 (0) < .001 < .001 < .001 Very difficult to read 3 (12.6) 6 (27.3) 1 (4.5) Extremally difficult to read 13 (58.1) 7 (31.8) 19 (86.4) Professional 5 (23.7) 3 (13.6) 2 (9.1) Somewhat difficult 0 (0) 1 (4.5) 0 (0) Readers age n (%) n (%) n (%) 8–9 years old (Fourth and Fifth graders) 0 (0) 1 (4.5) 0 (0) .019 .255 < .001 10–11 years old (Fifth and Sixth graders) 0 (0) 0 (0) 0 (0) 11–13 years old (Sixth and Seventh graders) 0 (0) 0 (0) 0 (0) 12–14 years old (Seventh and Eighth graders) 0 (0) 0 (0) 0 (0) 13–15 years old (Eighth and Ninth graders) 0 (0) 0 (0) 0 (0) 14–15 years old (Ninth to Tenth graders) 0 (0) 0 (0) 0 (0) 15–17 years old (Tenth to Eleventh graders) n (%) 0 (0) 1 (4.5) 0 (0) 17–18 years old (Twelfth graders) 2 (4.6) 4 (19.2) 0 (0) 18–19 years old (college level entry) 2 (13.7) 6 (27.4) 2 (4.4) 21–22 years old (college level) 4 (21.7) 4 (13.6) 3 (9.2) 23 + years old 14 (59.2) 8 (31.8) 16 (72.8) College graduate 0 (0) 0 (0) 3 (12.7) P values in bold are statistically significant. ARI = Automated Readability Index, CLI = Coleman-Liau Index, FKGL = Flesch-Kincaid Grade Level, FRES = Flesch reading ease score, GFPG = Gunning FOG, LW = Linsear Write, SMOG = Simple Measure of Gobbledygook. *C6thGRL (P): Comparison of the responses according to 6th grade reading level (P). 1 Wilcoxon test. 2Chi-Square test for categorical variables and Mann–Whitney U test for continuous variables. Table 3 Readability scores of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and analysis of the text content at a 6th-grade reading level [median (minimum–maximum)], were performed using Calculator 2. ( https://readabilityformulas.com/free-readability-formula-tests.php ) Calculator 2 statistics ChatGPT Google Gemini Deepseek Chat GPT C6thGRL (P)*1 Gemini C6thGRL (P)*1 Deepseek C6thGRL (P)*1 Between Chatgpt and Gemini2 Between Chatgpt and deepseek2 Between gemini and deepseek2 FRES 28.73 (9.59–61.29) 40.83 (24.59–53.29) 33.83 (9.59–72.29) < .001 < .001 < .001 .003 .649 .033 GFOG 16.53 (13.12–26.16) 13.43 (10.12–21.11) 19.04 (14.12–51.16) < .001 < .001 < .001 .004 .003 < .001 FKGL 14.98 (11.25–21.04) 11.98 (7.25–16.04) 16.98 (13.25–48.04) < .001 < .001 < .001 .001 .004 < .001 CLI 14.83 (11.43–21.30) 13.98 (11.25–16.04) 15.98 (12.25–18.04) < .001 < .001 < .001 .003 .140 < .001 SMOG 16.31 (12.70–20.46) 13.38 (11.25–16.24) 16.98 (14.25–32.04) < .001 < .001 < .001 .005 .003 < .001 ARI 16.14 (11.71–21.74) 13.28 (10.25–20.24) 17.88 (13.25–59.04) < .001 < .001 < .001 .006 .001 < .001 P values in bold are statistically significant. ARI = Automated Readability Index, CLI = Coleman-Liau Index, FKGL = Flesch-Kincaid Grade Level, FRES = Flesch reading ease score, GFPG = Gunning FOG, LW = Linsear Write, SMOG = Simple Measure of Gobbledygook. *C6thGRL (P): Comparison of the responses according to 6th grade reading level (P). 1 Wilcoxon test. 2Chi-Square test for categorical variables and Mann–Whitney U test for continuous variables. 3.2. Analysis of AI Chatbot Outputs (ChatGPT, Gemini, and DeepSeek) Based on the Mean Results from Calculators 1 and 2 Evaluation of the readability of nursing care plan texts produced by AI chatbots (ChatGPT, Gemini, and DeepSeek) for certain nursing diagnoses revealed that median readability scores for nursing care plan texts generated by ChatGPT included GFOG 17.14 (13.26–25.9), FRES 30.04 (9.29–58.14), SMOG 14.58 (11.13–19.27), FKGL 14.63 (11.49–20.88), ARI 16.33 (12.14–22.59), and CLI 15.00 (10.09–21.36), corresponding to an annual education level. For Gemini, medians were GFOG 14.60 (11.06–23.1), FRES 40.66 (23.29–54.14), SMOG 12.52 (9.96–16.33), FKGL 11.60 (7.52–16.01), ARI 13.87 (10.19 23.2), and CLI 13.84 (11.16–16.16). DeepSeek median values were GFOG 19.75 (14.86 52.88), FRES 34.91 (9.79–71.14), SMOG 15.78 (12.91–32.2), FKGL 16.48 (13.19–48.80), ARI 18.29 (13.7–59.83), and CLI 15.92 (12.28–18.33) (Table 4 . The analysis of readability metrics of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and the statistical evaluation of text complexity according to a 6th-grade reading standard [median (minimum–maximum)], was performed using mean values obtained from Calculator 1 and Calculator 2 for comparison purposes.). Table 4 The analysis of readability metrics of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and the statistical evaluation of text complexity according to a 6th-grade reading standard [median (minimum–maximum)], was performed using mean values obtained from Calculator 1 and Calculator 2 for comparison purposes. Calculator 1 statistics ChatGPT * Chat GPT C6thGRL (P) 1,2 Gemini * Gemini C6thGRL (P) 1,2 Deepseek* Deepseek C6thGRL (P) 1,2 Between Chatgpt and Gemini Between Chatgpt and deepseek Between gemini and deepseek FRES 30.04 (9,29-58.14) < .001 40.66 (23,29-54.14) < .001 34.91 (9,79-71.14) < .001 .003 .745 .021 GFOG 17.14 (13.26–25.9) < .001 14.60 (11.06–23.1) < .001 19.75 (14.86–52.88) < .001 .004 .003 < .001 FKGL 14.63 (11,49-20.88) < .001 11.60 (7,52-16.01) < .001 16.48 (13,19-48.80) < .001 .001 .003 < .001 CLI 15.00(11,09-21.36) < .001 13.84(11,16-16.16) < .001 15.92(12,28-18.33) < .001 .003 .001 < .001 SMOG 14.58 (11,13-19.27) < .001 12.52 (9,96-16.33) < .001 15.78 (12,91-32.2) < .001 .015 .105 < .001 ARI 16.33 (12.14–22.59) < .001 13.87 (10.19–23.2) < .001 18.29 (13.7–59,83) < .001 .006 .002 < .001 Calculator 1: https://readabilityformulas.com/free-readability-formula-tests.php Calculator 2: https://www.online-utility.org/english/readability_test_and_improve.jsp P values in bold are statistically significant. ARI = Automated Readability Index, CLI = Coleman-Liau Index, FKGL = Flesch-Kincaid Grade Level, FRES = Flesch reading ease score, GFPG = Gunning FOG, LW = Linsear Write, SMOG = Simple Measure of Gobbledygook. *[(Calculator 1) + (Calculator 2)]/2. 1C6thGRL (P): Comparison of the responses according to 6th grade reading level (P). 2Wilcoxon test. 3Chi-Square test for categorical variables and Mann–Whitney U test for continuous variables. 3.3. Reliability and quality assessment Using DISCERN, the quality and content of texts generated by AI chatbots (ChatGPT, Gemini, and DeepSeek) were analyzed. Results revealed moderate reliability, quality of nursing care information, and overall text quality (Table 5), with all 90 texts (100%) displaying moderate reliability and 27 texts (90%) containing nursing care information of moderate quality. Overall text quality assessment revealed that for ChatGPT, 10% of texts were high quality, 70% moderate, and 20% low. For Gemini and DeepSeek, 6.6% were high quality, 70% moderate, and 23.3% low (Table 5. Mean Scores of the DISCERN Total Scale and Subscales and Evaluation of Texts Generated by ChatGPT, Gemini, and Deepseek Using DISCERN). A consistent pattern emerged across all AI platforms (ChatGPT, Gemini, and DeepSeek): texts with a higher number of accessible and verifiable references demonstrated greater reliability according to the DISCERN scale. Statistical analysis revealed a significant positive correlation between the number of accessible references and the publication reliability subscale of DISCERN for all models (r = .423 for ChatGPT, r = .438 for Gemini, r = .438 for DeepSeek; all p < .05).” The number of accessible references was significantly linked to total DISCERN scores for all three AI models (r = .190 for ChatGPT, r = .490 for Gemini, r = .370 for DeepSeek; all p < .05). This association persisted for accessible journal article references, with a statistically significant correlation with publication reliability (r = .342; p < .05) (Table 6 . Correlation Analysis Between Descriptive Criteria and DISCERN). Table 6 Correlation Analysis Between Descriptive Criteria and DISCERN Tablo 5. Mean Scores of the DISCERN Total Scale and Subscales and Evaluation of Texts Generated by ChatGPT, Gemini, and Deepseek Using DISCERN DISCERN Bölümler ChatGBT Min-Max ChatGBT Ort ± SD ChatGBT Poor (Score 79%) ChatGBT Fair (Score 40%-79%) ChatGBT Good (Score > 79%) Gemini Min-Max Gemini Ort ± SD Gemini Poor (Score 79%) Gemini Fair (Score 40%-79%) Gemini Good (Score > 79%) Deepseek Min-Max Deepseek Gemini Ort ± SD Deepseek Poor (Score 79%) Deepseek Fair (Score 40%-79%) Deepseek Good (Score > 79%) S1 to S8 Reliability of Publication* 21–35 26.62 ± 2.85 0 (0) 30 (100) 0 (0) 20–35 25.62 ± 3.85 0 (0) 20 (100) 0 (0) 20–35 25.62 ± 3.85 0 (0) 20 (100) 0 (0) S9 ila S15 Quality of information on nursing care** 13–36 27.22 ± 4,65 3 ( 10 ) 27 (90) 0 (0) 12–36 26.22 ± 5,65 3( 10 ) 27 (90) 0 (0) 11–37 26.22 ± 5,65 3( 10 ) 27 (90) 0 (0) S16 Overall quality*** 3–5 3.56 ± 0.6 6 ( 20 ) 21 (70) 3 ( 10 ) 2–5 2.56 ± 0.5 7 (23.3) 21(70) 2 ( 6 , 6 ) 3–5 2.56 ± 0.5 7 (23.3) 21(70) 2 ( 6 , 6 ) Total**** 40–69 57,41 ± 5,9 40–70 58,41 ± 4,8 40–69 56,51 ± 6,8 * Minimum-maximum scores for reliability of publication range from 8 to 40 points.** Minimum-maximum scores for quality of information on nursing care range from 7 to 35 points.*** Minimum-maximum scores for overall quality range from 1 to 5 points.**** Minimum-maximum scores of DISCERN tool range from 16 to 80 points 3.7. Intraclass Correlation Coefficients (ICCE) GFOG, FRES, CLI, FKGL, ARI, and SMOG scores were calculated using two different calculators ( https://www.online-utility.org/english/readability_test_and_improve.jsp , https:// readabilityformulas.com/free-readability-formula-tests.php). 3.8. Intraclass Correlation Coefficient (ICCE) for ChatGPT The ICCE values for ChatGPT were 0.974 for the Flesch Reading Ease Score (FRES), 0.976 for the Flesch-Kincaid Grade Level (FKGL), 0.945 for the Gunning Fog Index (GFOG), 0.970 for the Coleman-Liau Index (CLI), 0.962 for the Automated Readability Index (ARI), and 0.960 for the SMOG index. 3.9. Intraclass Correlation Coefficient (ICCE) for Gemini Gemini exhibited intraclass correlation coefficients of 0.955 for Flesch Reading Ease Score (FRES), 0.889 for Flesch-Kincaid Grade Level (FKGL), 0.945 for Gunning Fog Index (GFOG), 0.925 for Coleman-Liau Index (CLI), 0.925 for Automated Readability Index (ARI), and 0.874 for SMOG. 3.10. Intraclass Correlation Coefficient (ICCE) for DeepSeek The interclass correlation coefficients (ICCE) for DeepSeek were as follows: 0.932 for Flesch Reading Ease Score (FRES), 0.971 for Flesch-Kincaid Grade Level (FKGL), 0.980 for Gunning Fog Index (GFOG), 0.979 for Coleman-Liau Index (CLI), 0.971 for Automated Readability Index (ARI), and 0.985 for SMOG. 3.11. Intraclass Correlation Coefficient (ICCE) for DISCERN The ICCE values for DISCERN were 0.825 for ChatGPT, 0.835 for Gemini, and 0.780 for DeepSeek. 4. Discussion Nursing care plan texts produced by AI chatbots (ChatGPT, Gemini, and DeepSeek) were analyzed for readability, quality, and reliability in this study. The findings highlight that, although AI-generated texts are increasingly utilized in healthcare, they continue to exhibit considerable limitations. The U.S. NIH recommends that health information be written for a sixth-grade reading level, yet the nursing care plans produced by AI surpassed this standard. Readability issues in texts generated by AI chatbots have emerged as a common finding across many health-related domains. Although there is no study specifically focusing on patient care plans, the existing literature supports this general trend. According to Ozduran et al. (2025), answers provided by ChatGPT, Gemini, and Perplexity on pain-related questions were difficult to comprehend and lacked sufficient quality ( 37 ). In a similar vein, Nutbeam (2023) emphasized that while AI platforms can provide accessible health information, their responses often exceed the comprehension abilities of individuals with limited health literacy, underscoring the need for caution in their deployment ( 38 ). Other studies have reported comparable findings: Gul et al. (2023) demonstrated that responses to questions about subdural hematoma had readability levels above recommended standards, while Erdat et al. (2025) found that AI-generated answers to common cancer-related questions were often difficult to understand, especially for individuals with limited health literacy ( 39 , 40 ). The findings of our study align with the existing literature, showing that texts generated by AI chatbots (ChatGPT, Gemini, and DeepSeek) require reading skills above the recommended level. This has significant implications for nursing practice and patient engagement. On the one hand, the presence of complex and challenging expressions in texts may impede nurses and nursing students’ ability to comprehend and execute care plans effectively; on the other hand, clear and understandable content can serve as a valuable learning tool for students. More importantly, excessively complex language may limit patients’ active participation in their own healthcare processes, negatively affecting treatment adherence. Accordingly, the simplicity, clarity, and literacy-level appropriateness of AI-generated supportive health texts are key determinants in improving care quality and supporting recovery success. Beyond informational accuracy, the quality of a text is evaluated by the clarity of its structure, its clinical relevance, and its consistency with measurable objectives. Our study revealed that nursing care plans generated by AI chatbots (ChatGPT, Gemini, and DeepSeek) do not fully meet these quality criteria, often remaining limited to general statements and with objectives that are not always aligned with clinical standards. This finding is consistent with other studies in the literature. For instance, Gilart et al. (2025) found that AI-generated nursing care plans often lacked specificity and measurability, particularly when evaluated against standardized frameworks such as NANDA, NOC, and NIC. Similarly, Olszewski et al. (2025) reported that although chatbot responses to hypertension-related questions were generally accurate, their readability levels frequently required university-level literacy, limiting accessibility for patients with lower health literacy. This underscores an important point: high information accuracy alone does not guarantee high quality; true quality is achieved when information is not only correct but also practical, understandable, and actionable ( 4 , 41 ). Since the reliability of AI-produced information relies on its verifiability, it represents one of the most essential quality standards. The study identified that only 25% of references generated by ChatGPT, Gemini, and DeepSeek were verifiable, highlighting a significant concern. This result reflects the issue of ‘hallucinated citations,’ whereby AI systems create fictitious references. Consistent with prior literature, our results reflect similar limitations. Recent evidence has shown that AI-generated medical content often lacks depth and factual precision. Kung et al. (2023) reported that ChatGPT achieved a passing score on the USMLE but occasionally produced inaccurate or fabricated information. Similarly, Gilson et al. (2023) evaluated ChatGPT’s performance across multiple medical licensing exams and found that while its responses were linguistically fluent, they often lacked clinical reasoning and failed to meet expert-level standards. Sallam (2023) also noted that although these models have great promise for healthcare education and research, their overall reliability remains moderate. These findings support the DISCERN quality scores observed in our study, where AI-generated nursing care plans showed only moderate reliability despite including seemingly valid sources ( 42 – 44 ). Artificial intelligence (AI) technologies are seen as a promising support tool for basic research; however, the reliability and sufficiency of these systems, particularly chatbots, in producing academic content are subjects of serious debate. Studies in the literature indicate that the current capabilities of AI in this field carry significant limitations. For instance, in a study by Sahin et al. (2024), the responses of five different artificial intelligence chatbots (ChatGPT, Bard, Bing, Ernie, and Copilot) to questions about erectile dysfunction were analyzed. The study revealed that overall readability levels were high, with Bard’s responses being the easiest to understand, while ChatGPT’s responses required a higher level of education to comprehend ( 45 ). Similarly, it has been reported that article abstracts prepared by AI remain more superficial and vague compared to original abstracts written by humans ( 46 ). These findings, combined with Xue et al.'s observation that ChatGPT struggles to provide in-depth and comprehensive information in dialogues on medical topics, clearly demonstrate that AI has not yet reached the desired maturity in academic content production ( 47 ). In parallel with the general trend in the literature, our study has confirmed that the texts generated by AI chatbots (ChatGPT, Gemini, and DeepSeek) offer only a moderate level of reliability. This situation clearly demonstrates that the information provided by these technologies should not be viewed as a standalone source by nurses and other healthcare professionals in patient care and treatment processes. It is absolutely essential that the information undergoes careful professional scrutiny before being integrated into clinical practice. In the context of academic integrity, our study revealed an interesting dilemma. Traditional plagiarism detection software (iThenticate, Turnitin) identified the AI-generated texts as "original" by finding a 0% similarity rate. However, the AI detection module within the very same Turnitin software correctly labeled all of the examined texts (100%) as "AI-generated." This finding indicates that traditional plagiarism tools are insufficient for detecting content created by artificial intelligence, but that next-generation AI detectors can largely resolve this issue. Previous studies have demonstrated the potential of artificial intelligence to revolutionize many areas of healthcare, ranging from clinical decision support systems to patient education ( 2 ). This technology has the capacity to both optimize the workflows of healthcare professionals and empower patients. However, while most existing research examines general web resources or theoretical applications, our study delves directly into the core of the issue. This research specifically aimed to analyze patient care plan texts generated instantaneously by AI chatbots. Examining these texts using practical criteria such as readability, quality, and reliability constitutes a critical step toward understanding this technology's effectiveness and applicability in real-world patient care scenarios, thus filling a gap in the current literature with this unique approach. AI-based tools offer significant opportunities for healthcare in areas such as accelerating access to information, text generation, and supporting clinical decision processes. Despite this potential, however, the current maturity level of the technology is far from making it a reliable, standalone authority for medical decisions. Specifically, AI continues to face limitations in domains that demand emotional nuance and human-centered judgment, such as developing personalized, empathetic, and patient-centered care plans ( 48 ).Furthermore, it is mandatory that users be extremely aware and cautious about critical issues like ethics, privacy, and data security. Therefore, future research and development must critically focus on: instantly linking AI systems to up-to-date medical databases, supporting every piece of information they provide with verifiable references, and most importantly, creating systems that can generate intelligible, multilingual content for everyone, including individuals with low health literacy. While the broad potential of artificial intelligence in healthcare is undeniable, concerns such as privacy breaches, data security vulnerabilities, and the inability to ensure the reliability of medical information present significant risks ( 49 ).Therefore, our study confirms that expert judgment and professional supervision are indispensable, especially in sensitive and critical processes like patient care planning. Consequently, this research provides an important foundation for the development of future systems that aim to establish a more reliable, up-to-date, and transparent collaboration between human expertise and artificial intelligence. 4.1. Strength of the Study This investigation sets itself apart from earlier studies by assessing the levels of readability, quality, and reliability in patient care plan documents. Unlike common research methodologies, we analyzed the relationships between various popular AI chatbots instead of relying on a single one. To ensure standardization in the readability assessment, we utilized two different, publicly available calculators instead of a single tool, and the agreement between these calculators was also examined. 4.2. Limitations of the Study This research is subject to several methodological constraints. The scope was limited to ChatGPT, Gemini, and DeepSeek, selected for their accessibility and prevalence, which restricts the breadth of comparison across other AI systems. Furthermore, the analysis relied exclusively on outputs generated on July 8, 2025, raising the possibility that results might differ over time. Finally, the sample consisted of 90 texts, a practical limitation despite AI’s ability to produce unlimited outputs, potentially reducing the generalizability of the conclusions. 5. Conclusion This analysis focused on nursing care plan texts created by the AI platforms ChatGPT, Gemini, and DeepSeek, assessing their readability, reliability, and quality. The results demonstrated that readability levels consistently surpassed the recommended sixth-grade standard, whereas both information quality and overall text quality were considered moderate. The study shows that AI chatbots can create original texts and that incorporating diverse sources enhances information reliability. However, the moderate quality and reliability of the outputs, along with the requirement for a relatively advanced literacy level, could complicate the understanding and practical application of care plans across different groups within the healthcare field. Given the importance of accuracy and clarity in healthcare, AI-generated content should be critically evaluated before clinical application. With careful oversight, AI chatbots can serve as both educational aids for nursing students and productivity-enhancing tools for professionals. The primary purpose of such technologies is to reinforce—not replace—the human-centered competencies central to nursing, thereby providing professionals with more time and opportunities. To deliver more effective information, AI chatbots must broaden their databases, rely on trustworthy academic references, and undergo expert-guided content improvement. Nonetheless, even with these enhancements, they cannot replicate the in-person interaction that is fundamental to nursing care. Declarations Ethics Approval and Consent to Participate Researchers utilized AI platforms provided as a “free research preview,” which did not require formal ethical approval or institutional authorization. Because the data are transparent and verifiable, the study clearly details the methods of AI use, including the selection, randomization, and organization of nursing diagnoses. Therefore, formal ethical approval was not required. Consent for Publication Not applicable. Competing Interests The authors declare that they have no competing interests. Funding Not applicable. Author Contribution Gokcen Gokalp M. and Cinar Yucel S. designed the study. Data were collected and analyzed by Gokcen Gokalp M. The manuscript was drafted by Gokcen Gokalp M. and reviewed by Cinar Yucel S. Both authors read and approved the final version of the manuscript. Acknowledgements Not applicable. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Topol EJ. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again . New York: Basic Books. Available from: https://www.basicbooks.com/titles/eric-topol/deep-medicine/9781541644632/ Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vascular Neurol. 2017;2(4):230–43. https://doi.org/10.1136/svn-2017-000101 . 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14:37:19","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":216159,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8080438/v1/56b4a84fda71755c5f872385.html"},{"id":96190933,"identity":"30799a47-29c0-4f11-8160-598d47953023","added_by":"auto","created_at":"2025-11-18 14:37:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182763,"visible":true,"origin":"","legend":"\u003cp\u003eCalculation Methods and Related Information of the Seven Applied Readability\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8080438/v1/78c10af18f0a934032427003.png"},{"id":96257113,"identity":"7f71b706-f7c4-48f8-a979-84571ef13adc","added_by":"auto","created_at":"2025-11-19 07:51:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1869732,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8080438/v1/0f63333f-9136-4bc2-a433-cc0f5c1e34d2.pdf"},{"id":96190935,"identity":"9076c3c6-b2ee-4156-8aa1-7106521a3632","added_by":"auto","created_at":"2025-11-18 14:37:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45689,"visible":true,"origin":"","legend":"","description":"","filename":"RandomizationBMC.docx","url":"https://assets-eu.researchsquare.com/files/rs-8080438/v1/be924381975fe61fb3dd09a1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComparative Analysis of Nursing Care Plans Produced by Artificial Intelligence Models (ChatGPT, Gemini, Deepseek) in Terms of Readability, Reliability and Quality\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe implementation of technological innovations in healthcare reduces the workload of nurses and physicians while also transforming the organization of care processes. Building on this foundation, artificial intelligence (AI) applications are increasingly being used for clinical documentation, decision support, and patient education, thanks to their natural language processing (NLP) capabilities (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). AI systems based on large language models (LLMs) perform functions such as strengthening clinical decision support processes in healthcare, preparing patient education materials, and contributing to the development of nursing care plans; in this context, the preparation of nursing care plans, which are critical in the nursing process, emerges as one of the prominent application areas of these systems (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Artificial intelligence (AI) refers to systems that enable computers to use thinking and learning capabilities modeled on the human mind; these include decision-making, learning from experiences, analyzing language and visual data, and generating solutions to problems (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Artificial intelligence, with its capacity to create significant changes in nursing practice, has been a driving force behind notable developments in the field. AI encompasses various applications in nursing practice, including both hardware-based solutions and digital platforms (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These technological combinations not only enhance the efficiency of nursing care but also provide new opportunities in diagnostic and care practices (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). AI systems based on large language models (LLMs) perform functions such as enhancing clinical decision support processes in healthcare, preparing patient education materials, and contributing to the development of nursing care plans; in this context, the preparation of care plans, which are critical in the nursing process, emerges as one of the prominent application areas of these systems. However, to ensure the reliability of the generated content, it must be reviewed and supervised by experienced healthcare professionals (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The use of AI-supported models in the preparation of nursing care plans can optimize the planning process and enhance standardization in practice through the integration of scientific resources, clinical guidelines, and patient information (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Their capacity to analyze patient-derived data, identify nursing diagnoses, suggest necessary interventions, and predict care outcomes allows nurses to reduce their clinical workload and dedicate more time to direct patient interaction. Thus, the integration of AI into nursing care plan development is considered an important step that both facilitates workload management and strengthens evidence-based nursing practice. However, rapid production alone is not sufficient; the generated content must also be rigorously evaluated in terms of readability, reliability, and quality (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eReadability refers to how easily a text can be comprehended and is crucial for healthcare providers as well as patients. The complexity of texts in the healthcare field is influenced by factors such as the use of medical terminology, sentence structure, and language simplicity. Studies indicate that the majority of patient education materials are written above the suggested sixth-grade reading level, which can make them challenging for patients to comprehend (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Similarly, high readability in nursing care plans prepared under heavy workloads can lead to misunderstandings and errors in practice; therefore, these plans must undergo review by experienced supervisors. Otherwise, they may even result in differences in interpretation among colleagues (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In nursing care plans generated by artificial intelligence, complex and technical content has also been observed to potentially limit the effective use of the text.\u003c/p\u003e\u003cp\u003eReliability is a critical measure that determines the effective use of artificial intelligence applications in healthcare. Some AI models can produce information that is false or unverified, a phenomenon known as \u0026ldquo;hallucination\u0026rdquo;; such erroneous outputs can negatively impact healthcare professionals\u0026rsquo; clinical decisions and jeopardize patient safety (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Therefore, in the preparation of nursing care plans, it is essential that the content is based on scientific evidence and rigorously reviewed for accuracy and consistency to ensure reliability. Moreover, a reliable care plan fosters trust in both clinical practice and multidisciplinary communication, contributing to the improvement of care quality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eQuality in healthcare texts is not limited to readability and accuracy but also encompasses clinical applicability, measurability, and alignment with patients\u0026rsquo; individual needs (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). When evaluating the quality of nursing care plans, it is important to assess whether the plans align with the SMART criteria (Specific, Measurable, Achievable, Realistic, Time-bound) for the defined care goals and whether they are prepared and implemented in an integrated and practical manner within the nursing process. These criteria directly affect the effectiveness of care, goal attainment, and patient safety. Moreover, quality assessment goes beyond content accuracy to include the applicability, measurability, and consistency of care plans within the clinical context. For this purpose, standardized tools such as DISCERN can be used to systematically evaluate the quality of both written health materials and nursing care plans, providing reliable data for clinical practice and educational processes (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, various artificial intelligence models have been developed and implemented in healthcare applications. Among them, advanced large language models like OpenAI\u0026rsquo;s ChatGPT and Google DeepMind\u0026rsquo;s Gemini, and the China-based DeepSeek have gained particular attention (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). ChatGPT is one of the most studied models due to its widespread use in both academic and clinical settings (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The Gemini model stands out for its ability to process text, visual, and tabular data simultaneously (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The DeepSeek model is notable for its high processing speed, cost effectiveness, and large number of parameters (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The performance of these models in generating nursing care plans represents a timely and important area of research for both educational and clinical practice contexts. While some studies in the literature have examined ChatGPT\u0026rsquo;s performance in developing nursing care plans, evaluations of Gemini and DeepSeek in this context remain insufficient. Moreover, existing research generally focuses on only one aspect\u0026mdash;readability, reliability, or quality\u0026mdash;without addressing these criteria comprehensively. The perceptions of nurses and other healthcare professionals regarding AI-generated nursing care plans have been investigated to a limited extent, leaving questions about the clinical validity and reliability of these plans largely unexplored (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study has been designed to address gaps in the literature, aiming to comparatively evaluate nursing care plans produced by ChatGPT, Gemini, and DeepSeek in terms of readability, reliability, and quality criteria (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The data obtained from the research will highlight the advantages and limitations of various AI systems, enabling an assessment of their applicability and potential in healthcare. Furthermore, the study aims to provide scientific insights that contribute both to the improvement of clinical nursing care practices and to the effective integration of healthcare technologies into nursing applications.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1.Research Design and Sample\u003c/h2\u003e\u003cp\u003eThis research seeks to examine the quality of AI-generated texts from chatbots such as ChatGPT, Gemini, and DeepSeek. The study\u0026rsquo;s sampling framework is based on the Central Limit Theorem, which states that with a sufficiently large number of independent random variables, the mean tends to follow a Gaussian distribution, thereby allowing reliable estimation of population parameters (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eThe statistical power and generalizability of the study\u0026rsquo;s results are supported by the Central Limit Theorem. As noted by the theorem and emphasized by Hair et al. (2010), a sample size of 30 units allows for more reliable estimation of population parameters (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In line with this theoretical framework, our analysis encompasses a total of 90 nursing care plans\u0026mdash;30 texts generated by each of the ChatGPT, Gemini, and DeepSeek chatbots. This approach was adopted to maximize the representativeness of the findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2.Selection of Topics\u003c/h2\u003e\u003cp\u003eThe texts produced by AI tools (ChatGPT, Gemini, and DeepSeek) were derived from topics chosen from the 2021\u0026ndash;2023 NANDA International taxonomy, covering a total of 267 unique nursing diagnoses (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith all 267 nursing diagnoses listed, a randomization table was produced using \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.random.org\u003c/span\u003e\u003cspan address=\"http://www.random.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (refer to Supplementary Digital Content Randomization Table.docx). This method allowed 30 diagnoses to be chosen at random, ensuring equal selection probability for each and preserving the fairness of the sampling procedure (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Each selected diagnosis was then input into three AI chatbots to create care plans, producing a total of 90 texts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3.Data Collection\u003c/h2\u003e\u003cp\u003eA structured data collection protocol was implemented in this study to comparatively evaluate the performance of current AI models in producing texts consistent with academic conventions. The data collection occurred on July 8, 2025, using the web interfaces of the latest versions of ChatGPT (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), Gemini (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), and DeepSeek (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). All platforms were accessed via Google Chrome (version 109.0.5414.119) on a Windows operating system to ensure reproducibility. Including such technical details is vital for validating and verifying the scientific reliability of the findings (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). For each nursing diagnosis selected at random, the AI chatbots (ChatGPT, Gemini, and DeepSeek) received the prompt: \u0026lsquo;Prepare a text on -NANDA DIAGNOSIS- and the corresponding nursing care plan, including as many references and in-text citations as you can. The aim of this prompt was twofold: to generate meaningful content and to evaluate the models\u0026rsquo; capacity to accurately include academic references (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Highlighting \u0026ldquo;nursing care plan\u0026rdquo; aimed to ensure that the outputs were clinically applicable and logically organized (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Investigating AI models\u0026rsquo; competence in producing texts with academic citations is a growing area of research interest (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe texts produced by ChatGPT, Gemini, and DeepSeek were systematically saved by the investigators to enable detailed assessment and further analysis during later stages of the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4.Data Collection Tools\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. Descriptive Information Assessment\u003c/h2\u003e\u003cp\u003eThe descriptive data collection form comprised 12 items covering characteristics such as word and paragraph count, text similarity score (measured with iThenticate and Turnitin), number of references, year of publication, type of document, and access options ( 29,30).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2.Readability assessment\u003c/h2\u003e\u003cp\u003eTwo online platforms were used to examine the readability of nursing care plan texts generated by AI chatbots (ChatGPT, Gemini, and DeepSeek). ReadabilityFormulas.com (Calculator 1) and Online-Utility.org (Calculator 2) employ various readability formulas to systematically evaluate the texts (\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeven readability formulas were applied in the analysis: To measure readability, the study applied established formulas such as Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFOG), Simple Measure of Gobbledygook (SMOG), Automated Readability Index (ARI), Coleman-Liau Index (CLI), and Linsear Write (LW). Each text\u0026rsquo;s readability was computed using these formulas, with results reported as medians accompanied by minimum and maximum values. A readability threshold of 80 or higher was considered acceptable for FRES, while a score of 6 or higher was used for the other six formulas (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTwo expert nursing researchers (M.G. and S.C.Y.) carried out independent assessments of the AI-generated texts, focusing on readability, standard, and dependability. The ultimate scores were derived by combining the evaluations from both researchers and computing the average. The calculation methods of the seven applied readability formulas and all related information are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Calculation Methods and Related Information of the Seven Applied Readabilityt).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eReliability and Quality Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDeveloped through a partnership between the British Library and the NHS, DISCERN provides a standardized method for assessing the reliability of health-related information available on the internet (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Comprising 16 questions across three sections, DISCERN assesses reliability (first eight questions), treatment information quality (next seven questions), and overall quality (final question). Items are rated on a 1-to-5 scale, with the cumulative score ranging from 16 to 80. Based on the scoring criteria, total ratings below 40 are classified as poor, ratings between 40 and 79 as moderate, and ratings of 80 or above as high quality (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e\u003cp\u003eSPSS version 25 (IBM Corp., Armonk, NY) was employed for all sta s cal analyses. Descriptive statistics comprised counts, percentages, average values, standard deviations, and ranges, while relationships between numerical variables and DISCERN scores were examined using Spearman\u0026rsquo;s correlation. Turni n and iThen cate soware were u lized to assess text similarity. Con nuous variables were compared using the Mann-Whitney U and Wilcoxon tests. The reliability of readability calculators was examined via intraclass correla on coefficients (ICC), and inter-observer agreement was assessed using Cohen\u0026rsquo;s κ. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;.05 was considered sta s cally significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Ehical Considerations\u003c/h2\u003e\u003cp\u003eResearchers utilized AI platforms provided as a \u0026lsquo;free research preview,\u0026rsquo; which did not require formal ethical approval or institutional authorization. Because the data are transparent and verifiable, the study clearly details methods for AI usage, including the process of choosing, randomizing, and organizing diagnoses.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTwo researchers independently evaluated the outputs generated by ChatGPT, Gemini, and DeepSeek, which were based on randomly chosen nursing diagnoses. The evaluations demonstrated strong consistency between the researchers, with a Cohen\u0026rsquo;s Kappa of 0.827, indicating reliable assessments. The mean number of words was 495.3\u0026thinsp;\u0026plusmn;\u0026thinsp;134.4 for ChatGPT, 531\u0026thinsp;\u0026plusmn;\u0026thinsp;41.1 for Gemini, and 497.7\u0026thinsp;\u0026plusmn;\u0026thinsp;70.1 for DeepSeek, with DeepSeek texts generally being shorter, as shown by the 374\u0026ndash;552 word range.\u003c/p\u003e\u003cp\u003eIn the source analysis, it was found that most of the publica ons cited in the texts were fic onal and not available in academic databases. Tradi onal plagiarism detec on tools (iThen cate, Turni n) failed to iden fy this, showing a 0% similarity rate. However, as a cri cal finding, Turni n\u0026rsquo;s AI detec on feature correctly iden fied all 90 texts as AI-generated with 100% accuracy (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Descriptive Information Form). This result highlights the limita ons of tradi onal plagiarism tools in detec ng original yet fic onal content produced by ar ficial intelligence.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Information Form\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eChatGBT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eGemini\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eDeepseek\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMin-Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin-Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMin-Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublication Years of References\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1984\u0026ndash;2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026thinsp;\u0026plusmn;\u0026thinsp;5,05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1979\u0026ndash;2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2016\u0026thinsp;\u0026plusmn;\u0026thinsp;7,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1980\u0026ndash;2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2017\u0026thinsp;\u0026plusmn;\u0026thinsp;6,05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of References\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026ndash;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,6\u0026thinsp;\u0026plusmn;\u0026thinsp;1,08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u0026ndash;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2,06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4,36\u0026thinsp;\u0026plusmn;\u0026thinsp;1,91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReferences that Are Accessible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1,01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1,01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1,01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReferences from the Web\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026ndash;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReferences from the Web That Are Accessible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReferences Cited from Journals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Available Journal Article References\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReferences from Books\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBook References That Are Accessible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWord Count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e402\u0026ndash;741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e495.3\u0026thinsp;\u0026plusmn;\u0026thinsp;134.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e487\u0026ndash;587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e531\u0026thinsp;\u0026plusmn;\u0026thinsp;41.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e374\u0026ndash;552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e497,7\u0026thinsp;\u0026plusmn;\u0026thinsp;70.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Paragraphs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u0026ndash;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eiThenticate Similarity Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurnitin Similarity Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurnitin AI Similarity Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.1. Readability Evaluation of AI Chatbots (ChatGPT, Gemini, and DeepSeek) Based on the Mean Scores of Calculators 1 and 2\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComparison of texts generated by ChatGPT, Gemini, and DeepSeek with the sixth-grade reading standard indicated statistically significant differences across all readability metrics (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). These findings suggest that the texts exceed the sixth-grade reading level. Additionally, significant differences were found when comparing each calculator\u0026rsquo;s output with the combined average (P\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Readability scores of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and analysis of the text content at a 6th-grade reading level [median (minimum\u0026ndash;maximum)], were performed using Calculator 1. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://readabilityformulas.com/free-readability-formula-tests.php\u003c/span\u003e\u003cspan address=\"https://readabilityformulas.com/free-readability-formula-tests.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Readability scores of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and analysis of the text content at a 6th-grade reading level [median (minimum\u0026ndash;maximum)], were performed using Calculator 2. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://readabilityformulas.com/free-readability-formula-tests.php\u003c/span\u003e\u003cspan address=\"https://readabilityformulas.com/free-readability-formula-tests.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e Readability scores of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and analysis of the text content at a 6th-grade reading level [median (minimum\u0026ndash;maximum)], were performed using Calculator 1. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://readabilityformulas.com/free-readability-formula-tests.php\u003c/span\u003e\u003cspan address=\"https://readabilityformulas.com/free-readability-formula-tests.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalculator 1 statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChatGPT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoogle Gemini\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDeepseek\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChat GPT C6thGRL (P)*1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGemini C6thGRL (P)*1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDeepseek C6thGRL (P)*1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBetween Chatgpt and Gemini2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBetween Chatgpt and deepseek2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eBetween gemini and deepseek2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFRES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.35 (9\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.50 (22\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (10\u0026ndash;70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.75(13.40\u0026ndash;25.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.78 (12\u0026ndash;25.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.47 (15.60\u0026ndash;54.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFKGL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.29 (11.74\u0026ndash;20.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.23 (7.79\u0026ndash;15.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.99 (13.13\u0026ndash;49.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.17 (12.37\u0026ndash;21.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.71 (11.08\u0026ndash;16.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.87 (12.31\u0026ndash;18.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.85(9.57\u0026ndash;18.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.67 (8.67\u0026ndash;16.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.58 (11.57\u0026ndash;32.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.52 (12.58\u0026ndash;23.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.47 (10.14\u0026ndash;26.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.71 (14.15\u0026ndash;60.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.27 (12.44\u0026ndash;22.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.32 (7.42\u0026ndash;31.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.41 (14.50\u0026ndash;85.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 (12.00\u0026ndash;20.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.00 (10.00\u0026ndash;20.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.00 (13.00\u0026ndash;45.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReading level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifficult to read\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery difficult to read\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtremally difficult to read\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (86.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSomewhat difficult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReaders age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u0026ndash;9 years old\u003c/p\u003e\u003cp\u003e(Fourth and Fifth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026ndash;11 years old\u003c/p\u003e\u003cp\u003e(Fifth and Sixth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u0026ndash;13 years old\u003c/p\u003e\u003cp\u003e(Sixth and Seventh graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;14 years old\u003c/p\u003e\u003cp\u003e(Seventh and Eighth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u0026ndash;15 years old\u003c/p\u003e\u003cp\u003e(Eighth and Ninth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u0026ndash;15 years old\u003c/p\u003e\u003cp\u003e(Ninth to Tenth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;17 years old\u003c/p\u003e\u003cp\u003e(Tenth to Eleventh graders) n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u0026ndash;18 years old\u003c/p\u003e\u003cp\u003e(Twelfth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;19 years old\u003c/p\u003e\u003cp\u003e(college level entry)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u0026ndash;22 years old\u003c/p\u003e\u003cp\u003e(college level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u0026thinsp;+\u0026thinsp;years old\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (59.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (72.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eP values in bold are statistically significant. ARI\u0026thinsp;=\u0026thinsp;Automated Readability Index, CLI\u0026thinsp;=\u0026thinsp;Coleman-Liau Index, FKGL\u0026thinsp;=\u0026thinsp;Flesch-Kincaid Grade Level, FRES\u0026thinsp;=\u0026thinsp;Flesch reading ease score, GFPG\u0026thinsp;=\u0026thinsp;Gunning FOG, LW\u0026thinsp;=\u0026thinsp;Linsear Write, SMOG\u0026thinsp;=\u0026thinsp;Simple Measure of Gobbledygook. *C6thGRL (P): Comparison of the responses according to 6th grade reading level (P). 1 Wilcoxon test. 2Chi-Square test for categorical variables and Mann\u0026ndash;Whitney U test for continuous variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReadability scores of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and analysis of the text content at a 6th-grade reading level [median (minimum\u0026ndash;maximum)], were performed using Calculator 2. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://readabilityformulas.com/free-readability-formula-tests.php\u003c/span\u003e\u003cspan address=\"https://readabilityformulas.com/free-readability-formula-tests.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalculator 2 statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChatGPT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoogle Gemini\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDeepseek\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChat GPT C6thGRL (P)*1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGemini C6thGRL (P)*1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDeepseek C6thGRL (P)*1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBetween Chatgpt and Gemini2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBetween Chatgpt and deepseek2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eBetween gemini and deepseek2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFRES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.73 (9.59\u0026ndash;61.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.83 (24.59\u0026ndash;53.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.83\u003c/p\u003e\u003cp\u003e(9.59\u0026ndash;72.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.53 (13.12\u0026ndash;26.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.43 (10.12\u0026ndash;21.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.04\u003c/p\u003e\u003cp\u003e(14.12\u0026ndash;51.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFKGL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.98 (11.25\u0026ndash;21.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.98 (7.25\u0026ndash;16.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.98\u003c/p\u003e\u003cp\u003e(13.25\u0026ndash;48.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.83\u003c/p\u003e\u003cp\u003e(11.43\u0026ndash;21.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.98 (11.25\u0026ndash;16.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.98\u003c/p\u003e\u003cp\u003e(12.25\u0026ndash;18.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.31\u003c/p\u003e\u003cp\u003e(12.70\u0026ndash;20.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.38 (11.25\u0026ndash;16.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.98\u003c/p\u003e\u003cp\u003e(14.25\u0026ndash;32.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.14\u003c/p\u003e\u003cp\u003e(11.71\u0026ndash;21.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.28 (10.25\u0026ndash;20.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.88\u003c/p\u003e\u003cp\u003e(13.25\u0026ndash;59.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eP values in bold are statistically significant. ARI\u0026thinsp;=\u0026thinsp;Automated Readability Index, CLI\u0026thinsp;=\u0026thinsp;Coleman-Liau Index, FKGL\u0026thinsp;=\u0026thinsp;Flesch-Kincaid Grade Level, FRES\u0026thinsp;=\u0026thinsp;Flesch reading ease score, GFPG\u0026thinsp;=\u0026thinsp;Gunning FOG, LW\u0026thinsp;=\u0026thinsp;Linsear Write, SMOG\u0026thinsp;=\u0026thinsp;Simple Measure of Gobbledygook. *C6thGRL (P): Comparison of the responses according to 6th grade reading level (P). 1 Wilcoxon test. 2Chi-Square test for categorical variables and Mann\u0026ndash;Whitney U test for continuous variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2. Analysis of AI Chatbot Outputs (ChatGPT, Gemini, and DeepSeek) Based on the Mean Results from Calculators 1 and 2\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Evaluation of the readability of nursing care plan texts produced by AI chatbots (ChatGPT, Gemini, and DeepSeek) for certain nursing diagnoses revealed that median readability scores for nursing care plan texts generated by ChatGPT included GFOG 17.14 (13.26\u0026ndash;25.9), FRES 30.04 (9.29\u0026ndash;58.14), SMOG 14.58 (11.13\u0026ndash;19.27), FKGL 14.63 (11.49\u0026ndash;20.88), ARI 16.33 (12.14\u0026ndash;22.59), and CLI 15.00 (10.09\u0026ndash;21.36), corresponding to an annual education level. For Gemini, medians were GFOG 14.60 (11.06\u0026ndash;23.1), FRES 40.66 (23.29\u0026ndash;54.14), SMOG 12.52 (9.96\u0026ndash;16.33), FKGL 11.60 (7.52\u0026ndash;16.01), ARI 13.87 (10.19 23.2), and CLI 13.84 (11.16\u0026ndash;16.16). DeepSeek median values were GFOG 19.75 (14.86 52.88), FRES 34.91 (9.79\u0026ndash;71.14), SMOG 15.78 (12.91\u0026ndash;32.2), FKGL 16.48 (13.19\u0026ndash;48.80), ARI 18.29 (13.7\u0026ndash;59.83), and CLI 15.92 (12.28\u0026ndash;18.33) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The analysis of readability metrics of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and the statistical evaluation of text complexity according to a 6th-grade reading standard [median (minimum\u0026ndash;maximum)], was performed using mean values obtained from Calculator 1 and Calculator 2 for comparison purposes.).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe analysis of readability metrics of nursing care plan texts generated by ChatGPT, Gemini, and Deepseek, and the statistical evaluation of text complexity according to a 6th-grade reading standard [median (minimum\u0026ndash;maximum)], was performed using mean values obtained from Calculator 1 and Calculator 2 for comparison purposes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalculator 1 statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChatGPT \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChat GPT\u003c/p\u003e\u003cp\u003eC6thGRL (P)\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGemini\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGemini C6thGRL (P)\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDeepseek*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDeepseek C6thGRL (P)\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBetween Chatgpt and Gemini\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBetween Chatgpt and deepseek\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eBetween gemini and deepseek\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFRES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.04 (9,29-58.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.66 (23,29-54.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.91 (9,79-71.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.14 (13.26\u0026ndash;25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.60 (11.06\u0026ndash;23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.75 (14.86\u0026ndash;52.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFKGL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.63 (11,49-20.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.60 (7,52-16.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.48 (13,19-48.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00(11,09-21.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.84(11,16-16.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.92(12,28-18.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.58 (11,13-19.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.52 (9,96-16.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.78 (12,91-32.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.33 (12.14\u0026ndash;22.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.87 (10.19\u0026ndash;23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.29 (13.7\u0026ndash;59,83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eCalculator 1: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://readabilityformulas.com/free-readability-formula-tests.php\u003c/span\u003e\u003cspan address=\"https://readabilityformulas.com/free-readability-formula-tests.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eCalculator 2: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.online-utility.org/english/readability_test_and_improve.jsp\u003c/span\u003e\u003cspan address=\"https://www.online-utility.org/english/readability_test_and_improve.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eP values in bold are statistically significant.\u003c/p\u003e\u003cp\u003eARI\u0026thinsp;=\u0026thinsp;Automated Readability Index, CLI\u0026thinsp;=\u0026thinsp;Coleman-Liau Index, FKGL\u0026thinsp;=\u0026thinsp;Flesch-Kincaid Grade Level, FRES\u0026thinsp;=\u0026thinsp;Flesch reading ease score, GFPG\u0026thinsp;=\u0026thinsp;Gunning FOG, LW\u0026thinsp;=\u0026thinsp;Linsear Write, SMOG\u0026thinsp;=\u0026thinsp;Simple Measure of Gobbledygook.\u003c/p\u003e\u003cp\u003e*[(Calculator 1) + (Calculator 2)]/2.\u003c/p\u003e\u003cp\u003e1C6thGRL (P): Comparison of the responses according to 6th grade reading level (P).\u003c/p\u003e\u003cp\u003e2Wilcoxon test.\u003c/p\u003e\u003cp\u003e3Chi-Square test for categorical variables and Mann\u0026ndash;Whitney U test for continuous variables.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Reliability and quality assessment\u003c/h2\u003e\u003cp\u003eUsing DISCERN, the quality and content of texts generated by AI chatbots (ChatGPT, Gemini, and DeepSeek) were analyzed. Results revealed moderate reliability, quality of nursing care information, and overall text quality (Table\u0026nbsp;5), with all 90 texts (100%) displaying moderate reliability and 27 texts (90%) containing nursing care information of moderate quality.\u003c/p\u003e\u003cp\u003eOverall text quality assessment revealed that for ChatGPT, 10% of texts were high quality, 70% moderate, and 20% low. For Gemini and DeepSeek, 6.6% were high quality, 70% moderate, and 23.3% low (Table\u0026nbsp;5. Mean Scores of the DISCERN Total Scale and Subscales and Evaluation of Texts Generated by ChatGPT, Gemini, and Deepseek Using DISCERN). A consistent pattern emerged across all AI platforms (ChatGPT, Gemini, and DeepSeek): texts with a higher number of accessible and verifiable references demonstrated greater reliability according to the DISCERN scale. Statistical analysis revealed a significant positive correlation between the number of accessible references and the publication reliability subscale of DISCERN for all models (r\u0026thinsp;=\u0026thinsp;.423 for ChatGPT, r\u0026thinsp;=\u0026thinsp;.438 for Gemini, r\u0026thinsp;=\u0026thinsp;.438 for DeepSeek; all p\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u0026rdquo; The number of accessible references was significantly linked to total DISCERN scores for all three AI models (r\u0026thinsp;=\u0026thinsp;.190 for ChatGPT, r\u0026thinsp;=\u0026thinsp;.490 for Gemini, r\u0026thinsp;=\u0026thinsp;.370 for DeepSeek; all p\u0026thinsp;\u0026lt;\u0026thinsp;.05). This association persisted for accessible journal article references, with a statistically significant correlation with publication reliability (r\u0026thinsp;=\u0026thinsp;.342; p\u0026thinsp;\u0026lt;\u0026thinsp;.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Correlation Analysis Between Descriptive Criteria and DISCERN).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation Analysis Between Descriptive Criteria and DISCERN\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"16\" nameend=\"c16\" namest=\"c1\"\u003e\u003cp\u003eTablo 5. Mean Scores of the DISCERN Total Scale and Subscales and Evaluation of Texts Generated by ChatGPT, Gemini, and Deepseek Using DISCERN\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDISCERN B\u0026ouml;l\u0026uuml;mler\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChatGBT Min-Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChatGBT Ort\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChatGBT Poor (Score 79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChatGBT Fair (Score 40%-79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChatGBT Good (Score\u0026thinsp;\u0026gt;\u0026thinsp;79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGemini Min-Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGemini Ort\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGemini Poor (Score 79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eGemini Fair (Score 40%-79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eGemini Good (Score\u0026thinsp;\u0026gt;\u0026thinsp;79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eDeepseek Min-Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eDeepseek Gemini Ort\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003eDeepseek Poor (Score 79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003eDeepseek Fair (Score 40%-79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003eDeepseek Good (Score\u0026thinsp;\u0026gt;\u0026thinsp;79%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS1 to S8 Reliability of Publication*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e20\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e25.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e20 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS9 ila S15 Quality of information on nursing care**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u0026ndash;36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4,65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12\u0026ndash;36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5,65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e27 (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e11\u0026ndash;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e26.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5,65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e3(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e27 (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS16 Overall quality***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21(70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e7 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e21(70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e2 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal****\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57,41\u0026thinsp;\u0026plusmn;\u0026thinsp;5,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e58,41\u0026thinsp;\u0026plusmn;\u0026thinsp;4,8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e40\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e56,51\u0026thinsp;\u0026plusmn;\u0026thinsp;6,8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"16\"\u003e* Minimum-maximum scores for reliability of publication range from 8 to 40 points.** Minimum-maximum scores for quality of information on nursing care range from 7 to 35 points.*** Minimum-maximum scores for overall quality range from 1 to 5 points.**** Minimum-maximum scores of DISCERN tool range from 16 to 80 points\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Intraclass Correlation Coefficients (ICCE)\u003c/h2\u003e\u003cp\u003eGFOG, FRES, CLI, FKGL, ARI, and SMOG scores were calculated using two different calculators (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.online-utility.org/english/readability_test_and_improve.jsp\u003c/span\u003e\u003cspan address=\"https://www.online-utility.org/english/readability_test_and_improve.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://\u003c/span\u003e\u003cspan address=\"https://\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ereadabilityformulas.com/free-readability-formula-tests.php).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Intraclass Correlation Coefficient (ICCE) for ChatGPT\u003c/h2\u003e\u003cp\u003eThe ICCE values for ChatGPT were 0.974 for the Flesch Reading Ease Score (FRES), 0.976 for the Flesch-Kincaid Grade Level (FKGL), 0.945 for the Gunning Fog Index (GFOG), 0.970 for the Coleman-Liau Index (CLI), 0.962 for the Automated Readability Index (ARI), and 0.960 for the SMOG index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.9. Intraclass Correlation Coefficient (ICCE) for Gemini\u003c/h2\u003e\u003cp\u003eGemini exhibited intraclass correlation coefficients of 0.955 for Flesch Reading Ease Score (FRES), 0.889 for Flesch-Kincaid Grade Level (FKGL), 0.945 for Gunning Fog Index (GFOG), 0.925 for Coleman-Liau Index (CLI), 0.925 for Automated Readability Index (ARI), and 0.874 for SMOG.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.10. Intraclass Correlation Coefficient (ICCE) for DeepSeek\u003c/h2\u003e\u003cp\u003eThe interclass correlation coefficients (ICCE) for DeepSeek were as follows: 0.932 for Flesch Reading Ease Score (FRES), 0.971 for Flesch-Kincaid Grade Level (FKGL), 0.980 for Gunning Fog Index (GFOG), 0.979 for Coleman-Liau Index (CLI), 0.971 for Automated Readability Index (ARI), and 0.985 for SMOG.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.11. Intraclass Correlation Coefficient (ICCE) for DISCERN\u003c/h2\u003e\u003cp\u003eThe ICCE values for DISCERN were 0.825 for ChatGPT, 0.835 for Gemini, and 0.780 for DeepSeek.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eNursing care plan texts produced by AI chatbots (ChatGPT, Gemini, and DeepSeek) were analyzed for readability, quality, and reliability in this study. The findings highlight that, although AI-generated texts are increasingly utilized in healthcare, they continue to exhibit considerable limitations.\u003c/p\u003e\u003cp\u003eThe U.S. NIH recommends that health information be written for a sixth-grade reading level, yet the nursing care plans produced by AI surpassed this standard. Readability issues in texts generated by AI chatbots have emerged as a common finding across many health-related domains. Although there is no study specifically focusing on patient care plans, the existing literature supports this general trend. According to Ozduran et al. (2025), answers provided by ChatGPT, Gemini, and Perplexity on pain-related questions were difficult to comprehend and lacked sufficient quality (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In a similar vein, Nutbeam (2023) emphasized that while AI platforms can provide accessible health information, their responses often exceed the comprehension abilities of individuals with limited health literacy, underscoring the need for caution in their deployment (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Other studies have reported comparable findings: Gul et al. (2023) demonstrated that responses to questions about subdural hematoma had readability levels above recommended standards, while Erdat et al. (2025) found that AI-generated answers to common cancer-related questions were often difficult to understand, especially for individuals with limited health literacy (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe findings of our study align with the existing literature, showing that texts generated by AI chatbots (ChatGPT, Gemini, and DeepSeek) require reading skills above the recommended level. This has significant implications for nursing practice and patient engagement. On the one hand, the presence of complex and challenging expressions in texts may impede nurses and nursing students\u0026rsquo; ability to comprehend and execute care plans effectively; on the other hand, clear and understandable content can serve as a valuable learning tool for students. More importantly, excessively complex language may limit patients\u0026rsquo; active participation in their own healthcare processes, negatively affecting treatment adherence. Accordingly, the simplicity, clarity, and literacy-level appropriateness of AI-generated supportive health texts are key determinants in improving care quality and supporting recovery success.\u003c/p\u003e\u003cp\u003eBeyond informational accuracy, the quality of a text is evaluated by the clarity of its structure, its clinical relevance, and its consistency with measurable objectives. Our study revealed that nursing care plans generated by AI chatbots (ChatGPT, Gemini, and DeepSeek) do not fully meet these quality criteria, often remaining limited to general statements and with objectives that are not always aligned with clinical standards. This finding is consistent with other studies in the literature. For instance, Gilart et al. (2025) found that AI-generated nursing care plans often lacked specificity and measurability, particularly when evaluated against standardized frameworks such as NANDA, NOC, and NIC. Similarly, Olszewski et al. (2025) reported that although chatbot responses to hypertension-related questions were generally accurate, their readability levels frequently required university-level literacy, limiting accessibility for patients with lower health literacy. This underscores an important point: high information accuracy alone does not guarantee high quality; true quality is achieved when information is not only correct but also practical, understandable, and actionable (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSince the reliability of AI-produced information relies on its verifiability, it represents one of the most essential quality standards. The study identified that only 25% of references generated by ChatGPT, Gemini, and DeepSeek were verifiable, highlighting a significant concern. This result reflects the issue of \u0026lsquo;hallucinated citations,\u0026rsquo; whereby AI systems create fictitious references. Consistent with prior literature, our results reflect similar limitations. Recent evidence has shown that AI-generated medical content often lacks depth and factual precision. Kung et al. (2023) reported that ChatGPT achieved a passing score on the USMLE but occasionally produced inaccurate or fabricated information. Similarly, Gilson et al. (2023) evaluated ChatGPT\u0026rsquo;s performance across multiple medical licensing exams and found that while its responses were linguistically fluent, they often lacked clinical reasoning and failed to meet expert-level standards. Sallam (2023) also noted that although these models have great promise for healthcare education and research, their overall reliability remains moderate. These findings support the DISCERN quality scores observed in our study, where AI-generated nursing care plans showed only moderate reliability despite including seemingly valid sources (\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eArtificial intelligence (AI) technologies are seen as a promising support tool for basic research; however, the reliability and sufficiency of these systems, particularly chatbots, in producing academic content are subjects of serious debate. Studies in the literature indicate that the current capabilities of AI in this field carry significant limitations. For instance, in a study by Sahin et al. (2024), the responses of five different artificial intelligence chatbots (ChatGPT, Bard, Bing, Ernie, and Copilot) to questions about erectile dysfunction were analyzed. The study revealed that overall readability levels were high, with Bard\u0026rsquo;s responses being the easiest to understand, while ChatGPT\u0026rsquo;s responses required a higher level of education to comprehend (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Similarly, it has been reported that article abstracts prepared by AI remain more superficial and vague compared to original abstracts written by humans (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). These findings, combined with Xue et al.'s observation that ChatGPT struggles to provide in-depth and comprehensive information in dialogues on medical topics, clearly demonstrate that AI has not yet reached the desired maturity in academic content production (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn parallel with the general trend in the literature, our study has confirmed that the texts generated by AI chatbots (ChatGPT, Gemini, and DeepSeek) offer only a moderate level of reliability. This situation clearly demonstrates that the information provided by these technologies should not be viewed as a standalone source by nurses and other healthcare professionals in patient care and treatment processes. It is absolutely essential that the information undergoes careful professional scrutiny before being integrated into clinical practice. In the context of academic integrity, our study revealed an interesting dilemma. Traditional plagiarism detection software (iThenticate, Turnitin) identified the AI-generated texts as \"original\" by finding a 0% similarity rate. However, the AI detection module within the very same Turnitin software correctly labeled all of the examined texts (100%) as \"AI-generated.\" This finding indicates that traditional plagiarism tools are insufficient for detecting content created by artificial intelligence, but that next-generation AI detectors can largely resolve this issue.\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated the potential of artificial intelligence to revolutionize many areas of healthcare, ranging from clinical decision support systems to patient education (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This technology has the capacity to both optimize the workflows of healthcare professionals and empower patients. However, while most existing research examines general web resources or theoretical applications, our study delves directly into the core of the issue. This research specifically aimed to analyze patient care plan texts generated instantaneously by AI chatbots. Examining these texts using practical criteria such as readability, quality, and reliability constitutes a critical step toward understanding this technology's effectiveness and applicability in real-world patient care scenarios, thus filling a gap in the current literature with this unique approach.\u003c/p\u003e\u003cp\u003eAI-based tools offer significant opportunities for healthcare in areas such as accelerating access to information, text generation, and supporting clinical decision processes. Despite this potential, however, the current maturity level of the technology is far from making it a reliable, standalone authority for medical decisions. Specifically, AI continues to face limitations in domains that demand emotional nuance and human-centered judgment, such as developing personalized, empathetic, and patient-centered care plans (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).Furthermore, it is mandatory that users be extremely aware and cautious about critical issues like ethics, privacy, and data security. Therefore, future research and development must critically focus on: instantly linking AI systems to up-to-date medical databases, supporting every piece of information they provide with verifiable references, and most importantly, creating systems that can generate intelligible, multilingual content for everyone, including individuals with low health literacy.\u003c/p\u003e\u003cp\u003eWhile the broad potential of artificial intelligence in healthcare is undeniable, concerns such as privacy breaches, data security vulnerabilities, and the inability to ensure the reliability of medical information present significant risks (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).Therefore, our study confirms that expert judgment and professional supervision are indispensable, especially in sensitive and critical processes like patient care planning. Consequently, this research provides an important foundation for the development of future systems that aim to establish a more reliable, up-to-date, and transparent collaboration between human expertise and artificial intelligence.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Strength of the Study\u003c/h2\u003e\u003cp\u003eThis investigation sets itself apart from earlier studies by assessing the levels of readability, quality, and reliability in patient care plan documents. Unlike common research methodologies, we analyzed the relationships between various popular AI chatbots instead of relying on a single one. To ensure standardization in the readability assessment, we utilized two different, publicly available calculators instead of a single tool, and the agreement between these calculators was also examined.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Limitations of the Study\u003c/h2\u003e\u003cp\u003eThis research is subject to several methodological constraints. The scope was limited to ChatGPT, Gemini, and DeepSeek, selected for their accessibility and prevalence, which restricts the breadth of comparison across other AI systems. Furthermore, the analysis relied exclusively on outputs generated on July 8, 2025, raising the possibility that results might differ over time. Finally, the sample consisted of 90 texts, a practical limitation despite AI\u0026rsquo;s ability to produce unlimited outputs, potentially reducing the generalizability of the conclusions.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis analysis focused on nursing care plan texts created by the AI platforms ChatGPT, Gemini, and DeepSeek, assessing their readability, reliability, and quality. The results demonstrated that readability levels consistently surpassed the recommended sixth-grade standard, whereas both information quality and overall text quality were considered moderate. The study shows that AI chatbots can create original texts and that incorporating diverse sources enhances information reliability. However, the moderate quality and reliability of the outputs, along with the requirement for a relatively advanced literacy level, could complicate the understanding and practical application of care plans across different groups within the healthcare field. Given the importance of accuracy and clarity in healthcare, AI-generated content should be critically evaluated before clinical application. With careful oversight, AI chatbots can serve as both educational aids for nursing students and productivity-enhancing tools for professionals. The primary purpose of such technologies is to reinforce\u0026mdash;not replace\u0026mdash;the human-centered competencies central to nursing, thereby providing professionals with more time and opportunities.\u003c/p\u003e\u003cp\u003eTo deliver more effective information, AI chatbots must broaden their databases, rely on trustworthy academic references, and undergo expert-guided content improvement. Nonetheless, even with these enhancements, they cannot replicate the in-person interaction that is fundamental to nursing care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003cp\u003eResearchers utilized AI platforms provided as a \u0026ldquo;free research preview,\u0026rdquo; which did not require formal ethical approval or institutional authorization. Because the data are transparent and verifiable, the study clearly details the methods of AI use, including the selection, randomization, and organization of nursing diagnoses. Therefore, formal ethical approval was not required.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGokcen Gokalp M. and Cinar Yucel S. designed the study. Data were collected and analyzed by Gokcen Gokalp M. The manuscript was drafted by Gokcen Gokalp M. and reviewed by Cinar Yucel S. Both authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTopol EJ. (2019). \u003cem\u003eDeep Medicine: How Artificial Intelligence Can Make Healthcare Human Again\u003c/em\u003e. New York: Basic Books. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arxiv.org/abs/2409.07415\u003c/span\u003e\u003cspan address=\"https://arxiv.org/abs/2409.07415\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chatbot, ChatGPT, Gemini, DeepSeek, Nursing care, Nursing care plan","lastPublishedDoi":"10.21203/rs.3.rs-8080438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8080438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWhile AI chatbots make healthcare information more accessible, there is still limited research on the readability, trustworthiness, and overall quality of the nursing care plans they generate.\u003c/p\u003e\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eThe research aims to investigate how AI-driven chatbots like ChatGPT, Gemini, and DeepSeek generate nursing care plan texts in terms of readability, reliability, and overall quality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 30 nursing diagnoses were randomly selected from the NANDA 2021\u0026ndash;2023 taxonomy. For each diagnosis, care plans were generated by three different AI chatbots, yielding 90 texts in total. The generated plans were evaluated through a \u003cb\u003edescriptive criteria form\u003c/b\u003e, the DISCERN tool for health \u003cb\u003einformation\u003c/b\u003e quality, and multiple readability measures (FRES, SMOG, Gunning Fog Index, and Flesch-Kincaid Grade Level).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe analysis revealed that the nursing care plans generated by ChatGPT, Gemini, and DeepSeek had readability scores significantly above the standard sixth-grade level (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). DISCERN analysis yielded average scores of 57.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 for ChatGPT, 58.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 for Gemini, and 56.51\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 for DeepSeek, reflecting moderate reliability overall. Among the generated texts, 27 (90%) offered information rated as moderate in quality. Moreover, the inclusion of verifiable references showed a statistically significant positive relationship with both reliability and quality measures (P\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eArtificial intelligence chatbots cannot replace complete nursing care plans. For AI-driven tools, it is advised to improve the clarity of the generated content, include reliable references, and have the material reviewed by professionals.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis of Nursing Care Plans Produced by Artificial Intelligence Models (ChatGPT, Gemini, Deepseek) in Terms of Readability, Reliability and Quality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 14:37:14","doi":"10.21203/rs.3.rs-8080438/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36276c1b-0b53-4359-9dee-94fc25c6f81f","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T07:43:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 14:37:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8080438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8080438","identity":"rs-8080438","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}