Use Of Artificial Intelligence Applications Research Articles

The Use of AI Powered ChatGPT for Nursing Education.

In late 2022, an AI (artificial intelligence) application, ChatGPT (generative pre-trained transformer), was released free for public use. Although present use of AI applications are scant in nursing education, the easy access to ChatGPT will inevitably influence educational experiences for both educators and students. Nursing educators have an opportunity to leverage this new technology by understanding the functionality and limitations of ChatGPT. This article examines the framework and functionality of ChatGPT and considers a potential nursing education assignment using the AI powered ChatGPT. The AI application, ChatGPT, is reviewed within the context of health care and nursing education and a potential nursing assignment leveraging ChatGPT is considered. Nursing educators will increase their knowledge about ChatGPT and consider a possible nursing curriculum assignment using ChatGPT. Although not without limitations, nursing educators can leverage this new AI powered technology for an enhanced student experience. [J Nurs Educ. 2024;63(X):XXX-XXX.].

Perceptions of Faculty and Students About Use of Artificial Intelligence in Medical Education: A Qualitative Study.

Artificial intelligence (AI) implies using a computer to model intelligent behavior with minimal human intervention. With the advances of AI use in healthcare comes the need to reform medical education to produce doctors competentin AI use. Therefore, this qualitative study was conducted to explore faculty and students' perspectives on AI, their use of AI applications, and their perspective on its value and impact on medical education at a Saudi faculty of medicine. This qualitative study was conducted at the Faculty of Medicine, Jazan University in Saudi Arabia. A direct interview was held with 11 faculty members, and six focus group discussions were conducted with students from the second to sixth year (34 students). Data were collected using semi-structured open-ended interview questions based on relevant literature. Most respondents (91.11%) believed AI systems would positively impact medical education, especially in research, knowledge gain, assessment, and simulation. However, ethical concerns were raised about threats to academic integrity, plagiarism, privacy/confidentiality issues, and AI's lacking cultural sensitivity. Faculty and students felt a need for training on AI use (80%) and that the curriculum could adapt to integrate AI (64.44%), though resources were seen as currently needing to be improved. AI's potential to enhance medical education is generally viewed positively in the study, but ethical concerns must be addressed. Integrating AI into medical education programs requires adequate resources, training, and curriculum adaptation. There is still a need for further research in this area to develop comprehensive strategies.

An Analysis of the Ideal Use of Artificial Intelligence Applications in Public Relations

Yapay zekâ alanında yapılan ilk çalışmalar insan zekâsını taklit edebilecek makineler yaratma fikri üzerine temellendirilmiştir. Günümüzde çalışmaların yönü insan zekâsını taklit edebilen makinelerin kişi, kurum, marka veya devletler üzerinde yaratmış olduğu değişim ve bu değişimin yol açtığı dönüşüm üzerine çevrilmektedir. Zira artık yapay zekâ disiplinler arası bir çalışma alanı olarak kabul görmekte ve hemen hemen her sektörde iş ve iletişim süreçlerini şekillendirerek etkisini göstermektedir. Bu çalışmada yapay zekâ uygulamalarının halkla ilişkilerde kullanımı üzerinde durulmuş; kurum ve ajanslarda yapay zekâ uygulamalarından hangi görevlerde ve hangi amaçlar için yararlanıldığı, yapay zekâ destekli uygulamalardan alınan dokümanlar üzerinden ortaya konulmaya çalışılmıştır. Çalışma, yapay zekânın halkla ilişkilerde ideal kullanımına katkıda bulunma çabasıyla ‘insan ile makine’ veya ‘uygulayıcı ile yapay zekâ’ arasında kurulacak olan ‘iş birliğine’ ve bu iş birliğinden doğan ‘meta zekâya’ yönelik bir çerçeve çizmeyi amaçlamaktadır. Çalışmada yapay zekâ uygulamalarının başta sosyal medya izleme ve dinleme, trend takibi, paydaş analizi olmak üzere pek çok halkla ilişkiler sürecinde kullanıldığı görülmekle birlikte bu kullanımın, tek başına yeterli olmadığı ve ancak bir uygulayıcı tarafından desteklendiğinde (yorumlandığında) anlamlı hale gelebileceği sonucuna ulaşılmıştır.

UTILIZING ARTIFICIAL INTELLIGENCE APPLICATIONS IN TRAINING: REALITY & CHALLENGES

The field of artificial intelligence (AI) is a modern science that has benefited various disciplines, contributing to the enhancement of service quality across different sectors, including training. This study aims to explore the use of AI applications in the training field in the Sultanate of Oman. The research investigates the current utilization of these applications in training, the challenges faced by trainers, and the key areas of application in training. The researcher employed a descriptive methodology, suitable for this study, with a study population comprising members of the Omani Trainers Association affiliated with the Human Resources Association. The sample targeted 78 trainers in various fields. A questionnaire, consisting of 26 items distributed across two axes (reality and challenges), was prepared for data collection. Additionally, an open-ended question was included for the third aspect of the study. Statistical methods, including frequency and percentage calculations, mean, standard deviation, Pearson correlation coefficient, and Cronbach's alpha, were used to analyze the data, ensuring the reliability and validity of the study tool. The study results indicate that, from the perspective of the study sample, the current implementation of AI applications in training is perceived as average. This reflects their dissatisfaction with this reality, which contradicts the vision and mission of a true trainer. The study also identifies various challenges in the use of AI applications in training, emphasizing the need for collaborative efforts to find practical solutions and provide better services that help organizations achieve their goals. Furthermore, the study highlights seven areas where trainers utilize AI applications in training, with knowledge and information search and scientific facts research leading among these areas. The study recommends the development of a specialized program for training professional trainers in the application of AI across all fields

Artificial Intelligence: A forward-looking vision for a better future for humanity Analytical theoretical study

The world is witnessing a tremendous Revolution in terms of Technological Development, entry into the world of Artificial Intelligence, and the possession of modern technologies in automation and algorithms that are useful for computers and robots, which is certainly necessary and indispensable, as; Progress and development in various fields require the necessity of relying on artificial intelligence technologies and its multiple uses. Despite the advantages of using artificial intelligence, it faces many challenges and risks that face humans as a result of these uses. They are not limited to the various aspects (economic, social, and media) that affect humans from Where only the labor market and unemployment rates, but there are also other risks according to the nature and specificity of the social and cultural context of our Arab societies that must be taken into account when entering the world of artificial intelligence? All of these reasons made the researcher address the research issue and the current research topic This Research aims to Learn the Science of Artificial Intelligence, and provide a forward-looking vision of what a better future for humanity may look like in the future. This is because it is a modern Science that has gained great Importance in recent years for its many applications in various fields. It also aims to identify its importance, benefits, types and concepts associated with it, and reveal the areas of use of Artificial Intelligence applications, how to use them, and the challenges facing its use. This research was devoted to studying the reality of artificial intelligence. In light of the developments to identify the challenges and address them automatically, to develop a future vision about artificial intelligence in the twenty-first century, the research was entitled: Artificial Intelligence: A forward-looking vision for a better future for humanity Keywords:) A Proposed Vision - Artificial Intelligence - Forward-looking vision - Applications of Artificial Intelligence – Technology (

Role of AI in Blended Learning: A Systematic Literature Review

As blended learning moved toward a new phase during the COVID-19 pandemic, advancements in artificial intelligence (AI) technology provided opportunities to develop more diverse and dynamic blended learning. This systematic review focused on publications related to the use of AI applications in blended learning. The original studies from January 2007 to October 2023 were extracted from the Google Scholar, ERIC, and Web of Science databases. Finally, 30 empirical studies under the inclusion criteria were reviewed based on two conceptual frameworks: four key challenges of blended learning and three roles of AI. We found that AI applications have been used mainly for the online asynchronous individual learning component in blended learning; little work has been conducted on AI applications that help connect online activities with classroom-based offline activities. Many studies have identified the role of AI as a direct mediator to help control flexibility and autonomy of students in blended learning. However, abundant studies have also identified AI as a supplementary assistant using advanced learning analytics technologies that promote effective interactions with students and facilitate the learning process. Finally, the fewest number of studies have explored the role of AI as a new subject such as use as pedagogical agents or robots. Considering the advancements of generative AI technologies, we expect more research on AI in blended learning. The findings of this study suggested that future studies should guide teachers and their smart AI partner to implement blended learning more effectively.

An Evaluation of the Use of Artificial Intelligence Applications in Online Education

Achieving targeted learning outcomes in online education and creating an effective learning environment require an interactive learning environment with a sense of learning community, personalized learning opportunities, a well-designed instructional content with effective use of visuals, sounds and teaching assistants, and instructors who can create and present them all effectively. AI applications can be utilized for a wide range of purposes in addressing the indicators of effective online education and developing strategies to achieve them. This research aims to explore various applications of AI in online education and its impact on teaching and learning processes. In line with this purpose, the study used qualitative case study method to explore the potential of AI applications in improving the quality of online education and the extent to which such applications can be utilized in online education. Following the research, examples of artificial intelligence (AI) applications that can be used in online education for the purposes of providing personalized learning experiences, creating learning content, emphasizing the use of visual and audio content in teaching, providing learning assistance, improving student engagement through the use of teaching assistants and chatbots, strengthening the interaction between the instructor and students, and evaluation were presented and suggestions were made for the contexts in which these applications can be used. The potential and limitations of AI applications in the development of online education were emphasized. On the other hand, it was recommended that the ethical implications of using AI applications in online education should be considered through further research and critical evaluation.

Use of artificial intelligence applications in the health sector: Preliminary diagnosis with deep learning method

Amaç: Bilgi çağı olarak adlandırılan günümüz dünyasında dijitalleşme her sektörde olduğu gibi sağlık sektöründe de kendisine önemli bir yer edinmiştir. Özellikle yapay zekâ teknolojilerinin sağlık sektörü üzerinde muazzam denilebilecek bir değişim etkisi sözkonusudur. Tanı ve teşhis yapabilmenin yanı sıra, sektörde yer alan nitelikli insan kaynağına alternatif olabilmesi beklenen yapay
 zekâ, bu araştırma da alt kümeleri olan derin öğrenme ağları perspektifinden ele alınmıştır. Çalışmada derin öğrenme ağlarından olan ResNet101 ve GoogLeNet bağlamında yapay zekânın hastalık teşhisindeki başarı düzeyinin belirlenmesi amaçlanmaktadır.
 
 Yöntem: Araştırma amacı doğrultusunda açık erişimli Kaggle web sitesinden beyin tümörü türlerinden olan Glioma, Meningioma ve Pituitary içeren toplamda 2124 adet MR görüntü veri seti elde edilmiş, bu verilerle %70 eğitim, %30 test oranı ile araştırmada kullanılan derin öğrenme ağlarının, görüntü ayrıştırma ve tanımlama başarı düzeyleri karşılaştırılmıştır.
 
 Bulgular: Analiz bulguları derin öğrenme ağlarının üç farklı beyin tümörü hastalığını ayrıştırma ve tanımlama noktasında başarılı olduğunu göstermiştir. Derin öğrenme ağlarının başarı düzeyleri incelendiğinde ResNet101 derin öğrenme ağının %91.5, GoogLeNet derin öğrenme ağının ise %87.9 başarı düzeyine sahip olduğu tespit edilmiştir.
 
 Sonuç: Araştırmanın bulguları doğrultusunda ResNet101 ve GoogLeNet derin öğrenme ağlarının beyin tümörü türlerini tanımlama ve ayrıştırma noktasında kullanılabilir olduğu, ResNet101 derin öğrenme ağının araştırma özelinde daha yüksek başarı oranı kaydettiği ve son olarak yapay zekâ teknolojilerinin sağlık sektöründe önemli bir yer edinme potansiyelinin olduğu tespit edilmiştir.

THE USE OF ARTIFICIAL INTELLIGENCE APPLICATIONS FOR EDUCATION AND SCIENTIFIC RESEARCH

Artificial Intelligence (AI), particularly Generative Artificial Intelligence (GAI), is increasingly being used in education and scientific research. This article explores the use of AI applications, such as ChatGPT, Scite, and Litmaps in education and scientific research. This kind of application offers several advantages but should be used with certain caution. Any inference must be verified made by AI tools to ensure their validity. While current AI technologies can quickly analyze vast amounts of data or represent networks of citations, their results may be influenced by the quality and comprehensiveness of databases. Thus, human experts must review and verify the results carefully to ensure their accuracy and originality. Additionally, academic institutions should educate students and researchers about the risks of utilizing GAI models while providing comprehensive guidelines to prevent plagiarism.

The impact of the use of artificial intelligence applications in interior design and the monitoring of the pros and cons through applied experiments

The impact of the use of artificial intelligence applications in interior design and the monitoring of the pros and cons through applied experiments

The transformative effect of artificial intelligence in hospitals : The focus is on the individual

Rapid advances in digital technology and the promising potential of artificial intelligence (AI) are changing our everyday lives and have already impacted on hospital procedures. The use of AI applications, in particular, enables awide range of possible uses and has considerable potential for improving medical and nursing care. In radiological diagnostics, for example, there are already many well-researched applications for AI-based image evaluation. In this article further AI developments are presented, which can help to relieve medical staff in order to create more time for direct patient care. In addition, essential aspects regarding the development and transfer of AI-based applications are highlighted. It is crucial that the integration of AI into medical practice is carried out with the utmost care and prudence. Data protection and ethical aspects need to be considered and respected at all times. Ensuring the reliability and integrity of AI systems is essential to earn the trust of both patients and healthcare professionals. Acomprehensive inspection for possible bias within the underlying data and algorithms is indispensable. In this field of tension between promising possibilities and ethical challenges, the digital transformation in medicine and care can be designed to increase patient safety and to relieve staff.

Challenging AI for Sustainability: what ought it mean?

AbstractThis paper argues that the terms ‘Sustainable artificial intelligence (AI)’ in general and ‘Sustainability of AI’ in particular are overused to the extent that they have lost their meaning. The AI for (social) good movement is a manifestation of this trend in which almost any application used in the context of healthcare or agriculture can be classified as AI for good regardless of whether such applications have been evaluated from a broader perspective. In this paper, we aim to create a common understanding of what the ‘AI for Sustainability’ movement ought to mean. We distinguish between two possible AI for Sustainability applications, namely those that fulfill the necessary conditions and those that fulfill the sufficient conditions. The former are purely predictive systems that serve as information providers. The latter are directly involved in an activity that contributes to a sustainability goal. We argue that taking action is a key element in distinguishing between these two application groups, as inaction is the key bottleneck in effectively tackling climate change. Furthermore, we question how effective the use of AI applications can be for sustainability when the systems themselves are inherently unsustainable. Hence, AI for Sustainability should include both an action that contributes to a sustainable end goal as well as an investigation of the sustainability issues of the AI system itself. Following that, Sustainable AI research can be on a gradient: AI in an application domain, AI towards sustainability, and AI for Sustainability.

Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology

ObjectivesTo define requirements that condition trust in artificial intelligence (AI) as clinical decision support in radiology from the perspective of various stakeholders and to explore ways to fulfil these requirements.MethodsSemi-structured interviews were conducted with twenty-five respondents—nineteen directly involved in the development, implementation, or use of AI applications in radiology and six working with AI in other areas of healthcare. We designed the questions to explore three themes: development and use of AI, professional decision-making, and management and organizational procedures connected to AI. The transcribed interviews were analysed in an iterative coding process from open coding to theoretically informed thematic coding.ResultsWe identified four aspects of trust that relate to reliability, transparency, quality verification, and inter-organizational compatibility. These aspects fall under the categories of substantial and procedural requirements.ConclusionsDevelopment of appropriate levels of trust in AI in healthcare is complex and encompasses multiple dimensions of requirements. Various stakeholders will have to be involved in developing AI solutions for healthcare and radiology to fulfil these requirements.Clinical relevance statementFor AI to achieve advances in radiology, it must be given the opportunity to support, rather than replace, human expertise. Support requires trust. Identification of aspects and conditions for trust allows developing AI implementation strategies that facilitate advancing the field.Key Points• Dimensions of procedural and substantial demands that need to be fulfilled to foster appropriate levels of trust in AI in healthcare are conditioned on aspects related to reliability, transparency, quality verification, and inter-organizational compatibility.•Creating the conditions for trust to emerge requires the involvement of various stakeholders, who will have to compensate the problem’s inherent complexity by finding and promoting well-defined solutions.

May Artificial Intelligence Be a Co-Author on an Academic Paper?

Dear Colleagues,
 Recently, for an article submitted to the European Journal of Therapeutics, it was reported that the paper may have been written with artificial intelligence support at a rate of more than 50% in the preliminary examination made with Turnitin. However, the authors did not mention this in the article’s material method or explanations section. Fortunately, the article’s out-of-date content and fundamental errors in its methodology allowed us no difficulty making the desk rejection decision.
 On the other hand, similar situations that we may encounter later caused us to discuss how we would decide when the artificial intelligence support of the articles was written. The general opinion that we have adopted and currently available in the literature is that if artificial intelligence is used while writing an article, how artificial intelligence is used in the methodology should be written in detail.
 Moreover, we encountered a much more exciting situation during our evaluation. In a few academic studies, we have seen that artificial intelligence is added as a co-author. On July 06, 2023, in the Web of Science, using the advanced search, we found four articles with the author name ChatGPT [1]. We have determined that ChatGPT is the author in one of these articles [2] and the Group Author in three [3-5].
 Lee [6] stated that although artificial intelligence tools are much more advanced than search engines, they cannot be an author regarding research ethics because they cannot take responsibility for what they write. Similarly, Goto and Katanoda [7] stated that it is the author’s responsibility to confirm that the texts written by ChatGPT are correct. However, Pourhoseingholi et al. [8] reported that keeping up with technology is inevitable. Additionally, they said that“this action will be more fruitful and practical in extended dimensions when international institutes like ICMJE or COPE come up with the appropriate adjustments and establish robust criteria to scheme the AI authorship”.
 Most probably, the use of artificial intelligence applications in scientific articles and whether it can be a co-author in these papers will be discussed soon.
 We encourage interested authors to submit their ideas to our journal as a letter to the editor.
 Yours sincerely,

An Assessment on the Problems and Possible Solutions of Artificial Intelligence Tools and Techniques on Execution, Monitoring and Reporting of Impact Investments

Impact analysis is an important work that should be done by business analysts in order to perform a successful requirements management in terms of compliance with regulations, environment and stakeholder needs as a result of possible changes in business demands and innovative technology. This multidisciplinary study examines the problems, solutions, stakeholder expectations, technical tools and approaches related to social impact assessments (SIA), with the assumption that impact investment needs will gradually increase in the light of legal regulations and business expectations in Türkiye. In this study, a literature review on impact analysis and impact investments is made and evaluations are made in the light of the current situation and possible future developments. In particular, based on the assumption that SIA is a priori to impact investments, the possibilities and ways of using artificial intelligence (AI) algorithms and smart Management Information System (MIS) tools are evaluated in the context of the innovative re-organization. In this study, various suggestions are developed by assertion that the tendency in the use of AI applications and smart MIS tools will gradually increase and will become a necessity in terms of meeting stakeholder expectations from impact investments in a realistic and futuristic manner.

Supporting students’ self-regulated learning in online learning using artificial intelligence applications

Self-regulated learning (SRL) is crucial for helping students attain high academic performance and achieve their learning objectives in the online learning context. However, learners often face challenges in properly applying SRL in online learning environments. Recent developments in artificial intelligence (AI) applications have shown promise in supporting learners’ self-regulation in online learning by measuring and augmenting SRL, but research in this area is still in its early stages. The purpose of this study is to explore students’ perceptions of the use of AI applications to support SRL and to identify the pedagogical and psychological aspects that they perceive as necessary for effective utilization of those AI applications. To explore this, a speed dating method using storyboards was employed as an exploratory design method. The study involved the development of 10 AI application storyboards to identify the phases and areas of SRL, and semi-structured interviews were conducted with 16 university students from various majors. The results indicated that learners perceived AI applications as useful for supporting metacognitive, cognitive, and behavioral regulation across different SRL areas, but not for regulating motivation. Next, regarding the use of AI applications to support SRL, learners requested consideration of three pedagogical and psychological aspects: learner identity, learner activeness, and learner position. The findings of this study offer practical implications for the design of AI applications in online learning, with the aim of supporting students’ SRL.

Performance and risks of ChatGPT used in drug information: an exploratory real-world analysis

ObjectivesTo investigate the performance and risk associated with the usage of Chat Generative Pre-trained Transformer (ChatGPT) to answer drug-related questions.MethodsA sample of 50 drug-related questions were consecutively collected and entered...

AI-Augmented HRM: Literature review and a proposed multilevel framework for future research

The research using artificial intelligence (AI) applications in HRM functional areas has gained much traction and a steep surge over the last three years. The extant literature observes that contemporary AI applications have augmented HR functionalities. AI-Augmented HRM HRM(AI) has assumed strategic importance for achieving HRM domain-level outcomes and organisational outcomes for a sustainable competitive advantage. Moreover, there is increasing evidence of literature reviews pertaining to the use of AI applications in different management disciplines (i.e., marketing, supply chain, accounting, hospitality, and education). There is a considerable gap in existing studies regarding a focused, systematic literature review on HRM(AI), specifically for a multilevel framework that can offer research scholars a platform to conduct potential future research. To address this gap, the authors present a systematic literature review (SLR) of 56 articles published in 35 peer-reviewed academic journals from October 1990 to December 2021. The purpose is to analyse the context (i.e., chronological distribution, geographic spread, sector-wise distribution, theories, and methods used) and the theoretical content (key themes) of HRM(AI) research and identify gaps to present a robust multilevel framework for future research. Based upon this SLR, the authors identify noticeable research gaps, mainly stemming from - unequal distribution of previous HRM(AI) research in terms of the smaller number of sector/country-specific studies, absence of sound theoretical base/frameworks, more research on routine HR functions(i.e. recruitment and selection) and significantly less empirical research. We also found minimal research evidence that links HRM(AI) and organisational-level outcomes. To overcome this gap, we propose a multilevel framework that offers a platform for future researchers to draw linkage among diverse variables starting from the contextual level to HRM and organisational level outcomes that eventually enhance operational and financial organisational performance.

No Thanks, Dear AI! Understanding the Effects of Disclosure and Deployment of Artificial Intelligence in Public Sector Recruitment

Abstract Applications based on artificial intelligence (AI) play an increasing role in the public sector and invoke political discussions. Research gaps exist regarding the disclosure effects—reactions to disclosure of the use of AI applications—and the deployment effect—efficiency gains in data savvy tasks. This study analyzes disclosure effects and explores the deployment of an AI application in a preregistered field experiment (n = 2,000) co-designed with a public organization in the context of employer-driven recruitment. The linear regression results show that disclosing the use of the AI application leads to significantly less interest in an offer among job candidates. The explorative analysis of the deployment of the AI application indicates that the person–job fit determined by the leaders can be predicted by the AI application. Based on the literature on algorithm aversion and digital discretion, this study provides a theoretical and empirical disentanglement of the disclosure effect and the deployment effect to inform future evaluations of AI applications in the public sector. It contributes to the understanding of how AI applications can shape public policy and management decisions, and discusses the potential benefits and downsides of disclosing and deploying AI applications in the public sector and in employer-driven recruitment.

Правовая квалификация модели машинного обучения на основе технического анализа этого процесса

Machine learning technologies are at the heart of artificial intelligence applications, ranging from use in the military to household assistants. The result of machine learning as a technical process is the creation of models that are able to produce a forecast of some degree of accuracy, on the basis of which people make decisions, and artificial intelligence systems perform actions. The machine learning model is a new phenomenon with an indefinite legal regime, however, regarding the creation and use of models, legal relations are formed, contracts are concluded, economic benefits are created during machine learning: all this needs legal qualification. Since inductive development methods are used to create models, machine learning has an atypical structure compared to a conventional computer program, which makes it difficult to determine the legal regime of the model and the legal consequences of its creation and operational application. The article presents an element-by-element technical analysis of machine learning as a process and a model as its result. On this basis the legal nature of the model is determined at all stages of its life cycle, protected elements of the structure are indicated; the legal significance of machine learning methods for the legal qualification of models is analysed, the character of training data external to the model is substantiated, and a benefit derived from training data in the form of model parameters is indicated. The conducted research is the basis for further analysis of legal relations that develop regarding the creation and use of artificial intelligence applications.