近年來，隨著人口高齡化及糖尿病、高血壓、肥胖與心血管疾病等慢性非傳染性疾病增加，慢性腎臟病（Chronic Kidney Disease, CKD）之盛行率與疾病負擔亦持續攀升。由於其早期症狀不明顯，民眾常於腎功能受損後才察覺異常，因此，如何透過預防性衛生教育協助民眾辨識危險因子、理解檢驗數值，並建立正確的飲食、生活型態與用藥觀念，已成為重要課題。然而，傳統門診衛教、衛教手冊及網路搜尋仍有時間有限、資訊分散、缺乏個人化及資訊可信度不易判斷等問題；生成式人工智慧雖可提供健康資訊，但使用者亦可能因不知如何提問而使諮詢內容發散。基於此，本研究運用大型語言模型之提示語工程與檢索增強生成技術，發展具引導式提問、雙向多輪對話、專業知識庫檢索、文字與語音互動、虛擬人物及個人化學習摘要功能之「AI慢性腎臟病諮詢代理人」。
本研究旨在比較使用「AI慢性腎臟病諮詢代理人」與「Google AI網路搜尋引擎」之受試者，在慢性腎臟病健康識能、數位健康素養與科技接受度上的差異，並分析不同人工智慧整體態度受試者之表現。研究採真實驗研究法，共招募41位受試者，隨機分派為實驗組21人與控制組20人。兩組於實驗前完成人工智慧整體態度量表與慢性腎臟病健康識能前測，接受45分鐘衛教諮詢後，再完成後測、數位健康素養量表與科技接受度量表；控制組另體驗「AI慢性腎臟病諮詢代理人」並填寫科技接受度比較量表。另將實驗組依慢性腎臟病健康識能後測平均數分為高、低分組，運用國立政治大學DLLL實驗室所開發之「ENA認知網絡分析系統」，以GPT-5進行關鍵詞標註與概念歸類，並以Plotly.js繪製三維認知網路圖；另訪談兩組共32位受試者。
研究結果顯示，兩組慢性腎臟病健康識能後測皆顯著高於前測，但組間差異未達顯著。控制組之低、中人工智慧整體態度受試者，以及實驗組之中、高人工智慧整體態度受試者，其慢性腎臟病健康識能均顯著提升；惟不同人工智慧整體態度受試者之組間學習成效皆未達顯著。兩組在數位健康素養之尋找、評估與應用面向，以及科技接受度之整體科技接受度、認知有用性與認知易用性上亦未達顯著差異。惟控制組體驗「AI慢性腎臟病諮詢代理人」後，其科技接受度比較量表各面向得分皆顯著高於中位數3，顯示其評價與偏好高於「Google AI網路搜尋引擎」。
認知網路分析顯示，高、低分組皆以「慢性腎臟病基礎知識」為核心，且最強連結皆出現在「慢性腎臟病健康識能」與「慢性腎臟病基礎知識」之間。低分組之概念連結較多元但較分散，高分組則呈現以基礎知識向健康識能、預防、用藥及飲食攝取延伸之集中結構，顯示其知識整合程度較高。訪談結果指出，「AI慢性腎臟病諮詢代理人」之引導式提問、雙向多輪對話、知識庫與學習摘要，有助於掌握學習方向、聚焦主題、評估資訊可信度及整理學習重點；「Google AI網路搜尋引擎」雖具操作熟悉、回應快速與資訊廣泛等優點，但較仰賴使用者自行規劃提問、整理資訊與交叉查證，且內容較易發散，多數控制組受訪者較偏好「AI慢性腎臟病諮詢代理人」。
綜合而言，兩種諮詢模式皆能提升慢性腎臟病健康識能，但在慢性腎臟病健康識能、數位健康素養與科技接受度之組間差異未達顯著。「AI慢性腎臟病諮詢代理人」可提供較具結構性、主題聚焦性與互動性的衛教諮詢歷程，並獲得較高的有用性、易用性與整體使用價值評價，顯示其具應用於慢性腎臟病預防性衛生教育之潛力。

In recent years, the prevalence and disease burden of chronic kidney disease (CKD) have continued to increase alongside population aging and the rising incidence of chronic noncommunicable diseases, including diabetes, hypertension, obesity, and cardiovascular disease. Because CKD often presents with few noticeable symptoms in its early stages, individuals may not recognize abnormalities until kidney function has already deteriorated. Therefore, preventive health education that helps the public identify risk factors, understand laboratory results, and develop appropriate knowledge regarding diet, lifestyle, and medication use has become increasingly important. However, conventional outpatient health education, educational materials, and online searches remain limited by insufficient consultation time, fragmented information, a lack of personalization, and difficulties in assessing information credibility. Although generative artificial intelligence can provide health information, users may not know how to formulate appropriate questions, resulting in unfocused consultations. To address these issues, this study applied large language model–based prompt engineering and retrieval-augmented generation to develop an “AI Chronic Kidney Disease Consultation Agent” incorporating guided questioning, bidirectional multi-turn dialogue, professional knowledge-base retrieval, text- and voice-based interaction, a virtual character, and personalized learning summaries.
This study aimed to compare CKD health literacy, eHealth literacy, and technology acceptance between participants using the “AI Chronic Kidney Disease Consultation Agent” and those using the “Google AI Search Engine,” and to examine the performance of participants with different levels of general attitudes toward artificial intelligence. A true experimental design was adopted. A total of 41 participants were recruited and randomly assigned to an experimental group using the AI Chronic Kidney Disease Consultation Agent (n = 21) or a control group using the Google AI Search Engine (n = 20). Before the experiment, both groups completed the General Attitudes towards Artificial Intelligence Scale and a CKD health literacy pretest. After a 45-minute health education consultation, they completed a CKD health literacy posttest, an eHealth literacy scale, and a technology acceptance scale. The control group subsequently experienced the AI Chronic Kidney Disease Consultation Agent and completed a technology acceptance comparison scale. In addition, the experimental group was divided into high- and low-scoring groups based on the mean CKD health literacy posttest score. Their human–AI dialogue records were analyzed using the “ENA Cognitive Network Analysis System” developed by the DLLL Laboratory at National Chengchi University. GPT-5 was used for keyword annotation and concept classification, while Plotly.js was used to generate three-dimensional cognitive network visualizations. A total of 32 participants from both groups were also interviewed.
The results showed that CKD health literacy posttest scores were significantly higher than pretest scores in both groups, although no significant between-group difference was found. CKD health literacy improved significantly among control-group participants with low and moderate general attitudes toward artificial intelligence and experimental-group participants with moderate and high attitudes. However, no significant between-group differences in learning outcomes were found among participants with different levels of general attitudes toward artificial intelligence. No significant differences were identified between the two groups in the finding, evaluating, and applying dimensions of eHealth literacy or in overall technology acceptance, perceived usefulness, and perceived ease of use. However, after experiencing the AI Chronic Kidney Disease Consultation Agent, control-group participants scored significantly above the scale midpoint of 3 across all dimensions of the technology acceptance comparison scale, indicating more favorable evaluations and preferences than for the Google AI Search Engine.
The cognitive network analysis showed that “CKD Basic Knowledge” served as the central concept in both the high- and low-scoring groups, and the strongest connection in both groups occurred between “CKD-specific health literacy” and “CKD Basic Knowledge.” The low-scoring group demonstrated more diverse but dispersed conceptual connections, whereas the high-scoring group exhibited a more concentrated structure extending from basic knowledge to health literacy, prevention, medication, and dietary intake, indicating a higher degree of knowledge integration. Interview findings revealed that the guided questioning, bidirectional multi-turn dialogue, knowledge base, and learning summaries provided by the AI Chronic Kidney Disease Consultation Agent helped participants identify learning directions, remain focused, assess information credibility, and organize key learning points. Although the Google AI Search Engine offered familiar operation, rapid responses, and broad information coverage, it relied more heavily on users to plan questions, organize information, and conduct cross-verification, and its consultation content was more likely to become unfocused. Most control-group interviewees preferred the AI Chronic Kidney Disease Consultation Agent.
Overall, both consultation approaches improved CKD health literacy, although no significant between-group differences were found in CKD health literacy, eHealth literacy, or technology acceptance. The AI Chronic Kidney Disease Consultation Agent provided a more structured, topic-focused, and interactive health education consultation process and received more favorable evaluations of usefulness, ease of use, and overall value, demonstrating its potential for application in preventive CKD health education.

中文文獻
台灣腎臟醫學會。護腎報報：2022 世界腎臟日-台灣腎臟月。上網日期：2023年1月30日。檢自：http://www.ckd-tsn.org.tw/news-view.php?ID=32
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