WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
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Article The Relationship Between Nurses Anxiety and Attitudes Towards Artificial Intelligence and Examination of Influencing Factors(BMC, 2026) Nirgiz, Cansu; Sari, Merve Kiymac; Cayli, NazanAim to explore the relationships between nurses'anxiety and attitudes toward artificial intelligence (AI) and the factors associated with them. Background Although AI technologies are increasingly integrated into healthcare, research exploring nurses ' psychological readiness and emotional responses to AI remains limited. Existing studies have primarily focused on nursing students or general healthcare professionals, leaving a gap in understanding how practicing nurses perceive and emotionally adapt to AI within real clinical environments-particularly in T & uuml;rkiye, where digital transformation in healthcare is accelerating. Addressing this gap is essential, as nurses play a pivotal role in ensuring the safe and ethical implementation of AI-driven tools in patient care. Methods This descriptive and correlational study included 562 nurses from 14 branches of a private hospital chain across seven Turkish cities between November 2024 and January 2025.This sample was selected because it represents nurses actively engaged in clinical decision-making within healthcare systems that are rapidly adopting AI technologies. According to a power analysis performed in G*Power (rho = 0.25, alpha = 0.05, power = 0.95), the required sample size was 202 participants. Data were collected through an online questionnaire comprising a Descriptive Information Form, the AI Anxiety Scale, and the General Attitudes toward AI Scale. Descriptive statistics, independent samples t-tests, ANOVA (F), Tukey post hoc test, Pearson correlation, and multiple regression analyses were conducted. Ethical approval was obtained from the Fenerbah & ccedil;e University Ethics Committee, and informed consent was obtained digitally. Results Nurses reported moderate anxiety levels and generally positive attitudes toward AI. Male nurses showed an association with lower anxiety levels and higher positive attitude scores than female nurses. Single individuals and those with higher levels of education showed higher positive attitudes toward AI. Those with 0-3 years of experience in the profession were associated with lower anxiety and higher positive attitude scores. Nurses who used AI in practice, were knowledgeable about its use, or perceived it as reliable showed a relationship with lower anxiety and more positive attitudes. Regression analysis showed that each one-unit increase in the learning and AI configuration subscales of the AI Anxiety Scale was associated with a 0.740-and 0.716-point lower score in the total attitude score, respectively. Conclusion The findings suggest that lower levels of anxiety related to learning and AI configuration are associated with more positive attitudes toward AI. Addressing these specific anxiety domains may be related to the successful integration of AI technologies into clinical practice and could be linked to the digital transformation in healthcare.Article A Novel Acoustic Source Localization Technique for Edge AI Applications: A Lightweight Framework and Implementation for IoT and Smart Sensing Devices(Istanbul University, 2025) Yarkan, S.This paper presents a novel and computationally efficient three-point signal estimation method for acoustic direction finding, designed specifically for low-cost embedded platforms. The proposed approach offers a lightweight alternative to traditional cross-correlation techniques by minimizing computational complexity while preserving high angular resolution. The method was implemented and tested on an STM32F429 microcontroller using a pair of MAX4466 electret microphones arranged on a fixed baseline. The system architecture leverages bare-metal signal processing routines optimized with Acorn RISC Machine Cortex. Microcontroller Software Interface Standard (ARM CMSIS-DSP) libraries, enabling real-time performance on resource-constrained hardware. Extensive experiments were conducted to evaluate the angular estimation accuracy under varying signal-tonoise ratios and source orientations. Results show that the system maintains sub-degree mean square error for source angles up to 30°, with noticeable performance degradation observed at 40° due to the directional response characteristics of the microphone elements. A comprehensive explanation is provided linking this degradation to reduced microphone sensitivity at wider angles of incidence. The proposed solution is ideal for applications requiring embedded acoustic localization, including smart interfaces, vehicular monitoring, and surveillance systems. In addition, the paper discusses the implications of deploying such systems in artificial intelligence (AI)-enabled and security-critical environments, highlighting emerging threats such as adversarial acoustic interference and spoofing attacks. These challenges underscore the importance of resilient and efficient DF methods that can operate reliably within the constraints of embedded systems. The presented work lays the foundation for future research in secure, scalable, and AI-compatible acoustic sensing platforms. © 2025 Elsevier B.V., All rights reserved.Article Evaluating and Comparing Student Responses in Examinations from the Perspectives of Human and Artificial Intelligence (GPT-4 and Gemini)(BMC, 2025) Domanic, Kubra Yildiz; Baycan, Sukran; Yildiz Domanic, KubraBackgroundGenerative Artificial Intelligence (AI) models, such as ChatGPT (GPT-4) and Gemini, offer potential benefits in educational settings, including dental education. These tools have shown promise in enhancing learning and assessment processes, particularly in dental prosthetic technology (DPT) and oral health (OH) programs.ObjectiveThis study aimed to evaluate the accuracy, reliability, and consistency of GPT-4 and Gemini AI models in answering examination questions in dental education. The study focused on multiple-choice questions (MCQs), true/false (T/F) questions, and short-answer questions (SAQs).MethodsAn exploratory study design was used with 30 questions (10 MCQs, 10 T/F, and 10 SAQs) covering key topics in DPT and OH education. ChatGPT and Gemini were tested with the same set of questions on two separate occasions to assess consistency. Responses were evaluated by two independent researchers using a predefined answer key. Data were analyzed using descriptive statistics, the Kappa coefficient for agreement, and the Chi-square test for categorical variables.ResultsChatGPT demonstrated high accuracy in MCQs (90%) and T/F questions (85%) but showed reduced performance in SAQs (60%). Gemini's accuracy ranged between 60% and 70%, with the highest accuracy in SAQs (70%). ChatGPT showed significant consistency across testing dates (Kappa = 0.754; p = 0.001), whereas Gemini's responses were less consistent (Kappa = 0.634; p = 0.001).ConclusionWhile both AI models offer valuable support in dental education, ChatGPT exhibited greater accuracy and consistency in structured assessments. The findings suggest that AI tools can enhance teaching and assessment methods if integrated thoughtfully, supporting personalized learning while maintaining academic integrity.Article Implementation of an AI-Enhanced Motor and Cognitive Intervention: A Case Study in Developmental Delay(Routledge Journals, Taylor & Francis Ltd, 2025) Bektas, Selen Aydoner; Bumin, Gonca; Aydoner Bektas, SelenThis study aimed to explore the implementation of an AI-enhanced motor and cognitive intervention for a 7-year-old child with developmental delay. A case study design was employed using an A-B framework (pre-test, intervention, post-test) over 12 weeks. The intervention incorporated AI-based tools such as Lumosity, Just Dance, and Cogmed for tailored motor and cognitive activities. The Bruininks-Oseretsky Test of Motor Proficiency-2 Brief Form (BOT-2 BF) and the Dynamic Occupational Therapy Cognitive Assessment for Children (DOTCA-Ch) were used to evaluate outcomes. Post-intervention, significant improvements were observed in BOT-2 BF and DOTCA-Ch scores, indicating enhanced motor coordination, and cognitive abilities. AI-enhanced interventions demonstrated the potential to address developmental delays by providing adaptive, engaging, and effective therapeutic activities. The findings highlight the feasibility of integrating AI tools into therapy, with implications for broader adoption in addressing developmental challenges. Further research is recommended to explore generalizability and long-term effects.Article Citation - WoS: 6Citation - Scopus: 6Artificial Intelligence in Nursing Practice: A Qualitative Study of Nurses' Perspectives on Opportunities, Challenges, and Ethical Implications(BMC, 2025) Bodur, Gonul; Cakir, Hanife; Turan, Suzan; Seren, Arzu Kader Harmanci; Goktas, PolatBackgroundThe study aims to explore nurses' views on the effects of artificial intelligence (AI) in nursing, focusing on their understanding, practical applications, ethical considerations, and perceived opportunities and threats.MethodsThis qualitative study used semi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:-$$\end{document}structured interviews to gain comprehensive insights from clinical nurses, adhering to the Standards for Reporting Qualitative Research for methodological rigor. After obtaining ethical approval, researchers conducted semi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:-$$\end{document}structured interviews with 25 clinical nurses. The interviews explored nurses' perceptions of AI, including its basic concepts, applications in nursing practice, ethical and social implications, and potential benefits and drawbacks.ResultsThe analysis identified four overarching themes: (1) Nurses' Conceptualizations of Artificial Intelligence, (2) Opportunities of AI in Nursing Practice, (3) Threats of AI in Nursing Practice, and (4) Ethical and Psychological Concerns in AI-Based Nursing Practice. The findings revealed that nurses had a foundational understanding of AI and its definitions. They acknowledged both the positive and negative impacts of AI technologies on their practice. Nurses expressed that AI could reduce workload, enhance patient care, and improve efficiency. However, they also articulated significant threats, including concerns over professional redundancy, emotional disconnection in caregiving, de-skilling, and the risk of dehumanizing the healthcare environment. Additionally, ethical and psychological concerns emerged, such as ambiguity in accountability, threats to data security and patient safety, unsuitability in psychiatric care contexts, staff surveillance anxiety, and risks of misuse or systemic bias.ConclusionThe study concluded that while nurses possess a basic understanding of AI, the effective and ethical integration of AI technologies in nursing requires targeted training, institutional preparedness, and robust interdisciplinary collaboration. To ensure AI complements rather than compromises nursing values, it is imperative to equip nurses with skills in digital literacy, ethical reasoning, and critical engagement with AI tools. The findings highlight the necessity of structured education programs and policy development that address both the technological and humanistic dimensions of AI use in healthcare. Future research should actively incorporate patient and public voices to ensure that AI-driven transformations in care remain aligned with the principles of patient-centeredness and human dignity.Article Citation - WoS: 1Citation - Scopus: 1Integration of Artificial Intelligence Tools into Interior Architecture Education: A Study on Textual and Visual Representations(Univ Cueca, 2025) Deval, Ozge; Kosencig, Kamile Ozturk; Acirli, Zeynep; Öztürk Kösenciğ, KamileDespite numerous Artificial Intelligence (AI) applications in the field, there is currently a lack of empirical evidence supporting their integration into design education, as well as limitations due to the novelty of these applications. Therefore, this study proposes a workflow integrating AI-assisted ideation and visualization into interior architecture education. An empirical study was conducted with six interior architecture students.The design process has been researched in addition to capturing the functional limitations and difficulties encountered by students who experienced the suggested educational framework.The findings were analyzed using descriptive analysis, a qualitative research method.Findings revealed that AI tools can effectively support the early design phase with the recommended workflow. However, participants often struggled to apply it critically, relying heavily on AI suggestions.This study provides a novel perspective by elucidating the potential benefits, challenges and impacts of AI applications in interior architecture education.Conference Object Efficiency of Mobile-Based AI-Personalized Exercise Program Versus Supervised AI-Personalized Exercise Program in Juvenile Idiopathic Arthritis: A Pilot Study(Elsevier, 2025) Yekdaneh, A.; Arman, N.; Ayaz, N. AktayArticle Citation - WoS: 2Citation - Scopus: 5Evaluating the Potential Role of Ai Chatbots in Designing Personalized Exercise Programs for Weight Management(Taylor & Francis inc, 2025) Sarac, Hakan; Ulusoy, Ismet Tarik; Alpay, Janset; Odemis, Hasan; Sogut, MustafaThis study aimed to evaluate the effectiveness and potential use of artificial intelligence (AI) chatbots in developing personalized exercise programs for weight management. Exercise programs were developed by ChatGPT-4, ChatGPT-4o, Gemini-1.5 Pro models, and a group of human expert trainers for a hypothetical obese individual case. All exercise programs were assessed based on the American College of Sports Medicine (ACSM) and National Academy of Sports Medicine (NASM) guidelines. The chatbot-generated programs were consistent with ACSM and NASM standards, indicating their potential use in low-resource settings. Nevertheless, considerable differences were found between human trainers and chatbots in key parameters, including initial load and target heart rate zone recommendations. While AI chatbots have the potential to enhance accessibility, human expertise remains essential to ensure program safety and effectiveness. The results of this study provide insights into the potential role of AI chatbots in personalized exercise programs for weight management.