Use Of AI Technologies Research Articles

Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States

This article aims to investigate the factors that affect behavioural intention (BI) and user behaviour (UB) among Arabian users of generative artificial intelligence (GenAI) applications in the context of media content creation. The study’s theoretical framework is grounded in the unified theory of acceptance and use of technology (UTAUT2). A sample of 496 users was analysed using the partial least squares structural equation modelling technique (PLS-SEM). The results revealed that BI is significantly influenced by performance expectancy, effort expectancy, social influence, hedonic motivation, habit, and user trust, with hedonic motivation having the greatest impact. In terms of UB, facilitation conditions, habit, user trust, and BI were all found to have a positive and significant impact. This study contributes to the existing theory on the utilisation of GenAI applications by organising findings pertaining to the use of AI technology for media content creation.

Reaping the rewards with minimal toil: Evaluating the polemics of artificial intelligence in academia and the future of academic writing

The use of Artificial Intelligence in academic writing swings on the edge of revolution and obliteration, where the promise of unparalleled efficiency clashes with the specter of intellectual dilution, which challenges the essence of academic rigor and originality. This paper interrogates the ways in which the two extremes of innovation and traditional writing methods in academia can be integrated. As such, the paper argues that it is essential to incorporate new technologies and modern AI tools with critical and historical aspects of academic culture rather than replacing them. In essence, AI should only augment what researchers are able to do, not replace them. As a result, it is important to set specific guidelines and standards in order to deal with issues, of authorship, data collection, controlled storage, and validation of artificial intelligence products. Lastly, the paper recommends the necessity of training and education about AI tools for academics and scientists in institutions of higher learning. The academic community should seek to implement policies and strategies that allow for the use of AI technologies in the execution of scholarly activities without compromising the quality of academic writing.

AI and Research: Methods and Best Practices for Conducting Research Using AI

This video presentation provides an overview of methods and best practices for using AI in academic research, emphasizing its significance from a library perspective. It introduces key concepts, strategies, and the importance of AI literacy, incorporating a brief review of frameworks for the evaluation and use of AI technologies. This video also offers a concise summary of popular AI research tools and best practices based on select recommendations from reputable institutions and industry scholarship. The inclusion of visual and auditory elements detailing key matters pertaining to the use of AI in higher education contributes to a multimodal and diversified approach designed for a broader audience. Video formats may complement other traditional forms of publication by providing visual explanations that are intended to enhance accessibility and engagement with complex concepts in scholarly discourse.

Artificial Intelligence as an Auxiliary Tool in Pediatric Otitis Media Diagnosis

ObjectivesIn order to promote the use of AI technology as the auxiliary tool in pediatric otitis media diagnosis, we use the convolutional neural networks and deep learning for image classification and disease diagnosis. We also designed a Pediatric Otitis Media Classifier to analyze and classify the images for physicians. MethodsA pediatric otitis media classifier was designed for junior physicians (doctors who have been engaged in clinical practice for a short time) as an auxiliary diagnostic tool. To design this classifier for children with otitis media, we used a large number of images of acute otitis media (AOM), secretory otitis media (OME), and normal otoscope images to obtain the optimal convolutional neural network model. ResultsThe average recognition accuracies of the ZFNet and the TSL16 for classification were 97.87% and 97.62%, far exceeding the accuracy of human diagnosis. The results of using the Pediatric Otitis Media Classifier show that we can use the classifier to correctly identify the image types of child middle ear infections. ConclusionsWe developed the Pediatric Otitis Media Classifier for the successful automated classification of AOM and OME in children using otoscopic images. In contrast to the traditional diagnosis of pediatric otitis media, which relies heavily on the experience of doctors, the diagnostic accuracy of even experienced physicians is only approximately 80%. With AI technology, we can improve the accuracy rate to over 98%, which can effectively assist doctors in auxiliary diagnosis. It also reduces delayed treatment, antibiotic misuse, and unnecessary surgery caused by misdiagnosis.

Influences of Digital Literacy and Moral Sensitivity on Artificial Intelligence Ethics Awareness Among Nursing Students.

As artificial intelligence technology has developed, research on the application of AI in the medical field has increased, and there is a high likelihood that the use of AI technology will expand in nursing education and practice in the future. However, ethical issues arise when utilizing AI, necessitating a high level of ethical awareness before its application. This study aimed to identify factors in artificial intelligence ethics awareness among nursing students. Participants were 140 nursing students attending universities in G City and J Province in South Korea. Data were collected using a self-administered questionnaire from 26 August to 6 September 2024. Factors in artificial intelligence ethics awareness were analyzed by multiple regression analysis. Nursing students' artificial intelligence ethics awareness is significantly correlated with digital literacy (r = 0.30, p < 0.001) and moral sensitivity (r = 27, p < 0.001). The influencing factor in artificial intelligence ethics awareness among nursing students was moral sensitivity (β = 0.23, p = 0.042). The explanation power of these variables was 14.0% (F = 46.78, p < 0.001). There is a need to provide education and training programs that can improve moral sensitivity to foster artificial intelligence ethics awareness.

Intelligent energy management system of hydrogen based microgrid empowered by AI optimization technique

Microgrids powered by hydrogen often face challenges in effectively managing energy over an extended duration due to the intermittent nature of renewable energy sources (RES) and fluctuating loads. This research emphasizes the use of AI technologies, including machine learning to improve the efficiency of intelligent energy management system (IEMS), hydrogen storage, fuel cell consumption, and power system quality improvement. A novel approach integrating Isolation Forest Ensemble (IFE) and Modified Water Wave Optimization (MWWO) has been proposed to enhance energy management and to improve the power quality. This method combines the population size with precision and accuracy. The effectiveness of this study has been assessed against established methods (AVO, PSO & IPSO, MFO, SAG & XGBoost, MILP, and QTLBO) concerning numerous factors including component sizing, economics, reliability, and efficiency of microgrid. The precision level of the proposed technique is approximately 83 %. The primary benefit of this research is the improvement of power quality and cost-efficiency in hydrogen based microgrids, attained through optimized and intelligent energy management system. This article furnishes evidence indicating the superiority of the anticipated approach. This study utilizes MATLAB/Simulink and PyCharm framework for its investigation and projection.

Discrimination Associated with Artificial Intelligence Technologies

As is known, there has been a significant increase in the use of AI technologies in various fields related to the work of both public and private sectors. Despite its importance in economic, health, security, and educational fields, the use of AI technologies has led to several ethical and legal risks. For example, there are risks of bias or discrimination when building AI systems, challenges related to privacy and data protection, including issues that arise from errors in health protocol procedures and their impact on patient health. This paper aims to highlight a number of practical applications of bias or discrimination associated with the use of AI technologies, concluding with the importance of having clear ethical governance to address the emerging risks of using AI applications.

Ai-driven Predictive Analytics, Healthcare Outcomes, Cost Reduction, Machine Learning, Patient Monitoring

Integrated with predictive analytics and machine learning, AI has exceeded the traditional approaches of health care contexts by focusing on patient outcomes and costs. This paper aims to discuss the adoption of integrated AI in healthcare systems, categorizing these by how AI predictive models help improve patient health by proactively estimating the course of their illness and its potential impact, prioritizing patient readmissions, and developing effective individualized treatment strategies. The research also identifies more important savings realised through avoiding redundant tests, better utilisation of resources, and shorter hospitalisations. In the current study, the authors present concrete findings for AI-driven Predictive Analytics based on realistic scenarios and quantitative data of health care systems. It also points to the fact that healthcare organisations adopting the use of AI technology have gained an objective that reduced their operations costs by 25% and improved patient outcomes whereby the readmission rates were reduced by between 15% and 20%. Furthermore, there is an evaluation of the ethical considerations of applying AI in healthcare, especially on the subject of patient’s information security. To the best of our knowledge, this study is the first to systematically review AI applications in healthcare and provide detailed suggestions for better understanding the general impact of AI in healthcare to enhance patient outcomes and manage costs. With advancement in artificial intelligence technology, there is a growing importance of how the technology can transform the health care industry.

Tech vs. Tradition: ChatGPT and Mindfulness in Enhancing Older Adults' Emotional Health.

To improve older adults' mental health, this study compared the effects of AI chatbots, such as ChatGPT 3.0, with traditional mindfulness therapies on loneliness and depression in older adults. Despite interest in AI as a complementary tool, empirical evidence on its impact remains limited. For an eight-week intervention, older adult participants from two nursing homes in Hangzhou were assigned to groups focused on mindfulness (group sessions) and chatting (one-on-one ChatGPT). Following the intervention, participants engaged in a researcher-led focus group discussion. After 8 weeks, tension had decreased significantly (p < 0.05) in the Mindfulness group of older adults, and there was no significant difference between the effects of ChatGPT and mindfulness on the emotional intervention of older adults. Findings indicated three themes, including (1) personal experience, reflecting older adults' use of AI technology and mindfulness; (2) attitudes and perspectives on the experiment's desirability and insufficiencies; and (3) needs and expectations for future AI and mindfulness developments, including usability and functional preferences. Similar to mindfulness practice, ChatGPT interactions helped older adults feel less depressed and might eventually reduce costs by replacing mindfulness. In the future, AI can be integrated with conventional techniques to improve interaction by giving AI a more human-like appearance.

A facial and vocal emotion recognition system based on deep learning

The traditional teaching mode, mainly characterized by "cramming" instruction, often overlooks the absorption of knowledge by students during the teaching process. Even in recent years, the emerging "flipped classroom" model heavily relies on teachers' experience and expertise, making it challenging to quantify students' classroom learning states. Among young teachers lacking teaching experience, the replication of excellent teaching models faces difficulties. With the rapid development of artificial intelligence technology, deep learning and other AI techniques are increasingly applied to people's lives. Additionally, emotion computing technology is also becoming more sophisticated. In the field of education, the use of AI technology holds the potential to help teachers systematically and comprehensively assess teaching quality, promoting the sharing of high-quality educational resources nationally and globally. Existing emotion detection technologies often focus on single techniques such as image recognition or natural language processing, resulting in low accuracy in discerning human emotions and making it difficult to accurately identify emotional changes in complex scenarios such as classrooms. Furthermore, there is a lack of comprehensive emotional statistical analysis methods for audio and visual data. In light of these, we have designed and implemented a multi-modal emotion recognition and analysis system. Through testing in classroom settings, this system has the potential to assist classroom teaching and enhance teaching quality.

Kajian Teologi Pastoral terhadap Artificial Intelligence dalam Praktek-praktek Religius

Lately, artificial intelligence has been rapidly advancing and is increasingly being utilized, both by experts in various fields and by the public in common. This article provides a study of the theology of pastoral on the specific use and influence of AI in ChatGPT. By reviewing the utilization of chatbot in various religious practices such as sermon creation, spiritual guidance, and access to religious teachings, this research explores the extent to which chatbot can assist in religious practices. This research includes evaluation of its usage and consideration of its risks associated with the use of AI technology particularly in religious practices. Exploring AI chatbots' potential could help technology as catalysts for transformation of religious practices nowaday. Furthermore, we can direct technology as a tool to enrich faith and the spiritual dimensions of humanity in this digital era.

Integrasi Etika AI dalam Pendidikan Agama Kristen: Tantangan dan Peluang

AI ethics is a branch of ethics that focuses on moral principles in the development and use of AI technologies. This research aims to integrate AI ethics in Christian religious education and explore the opportunities and challenges in the process, as well as strategies to overcome these challenges. The method used is a descriptive qualitative approach with literature studies from various sources such as books, national and international journals, and websites. The results show the importance of integrating AI ethics in Christian religious education to shape students' characters, prepare their future, and improve the quality of learning. Challenges faced include limited resources, digital divide, lack of understanding of AI ethics, and the need for adequate supervision and regulation. Strategies to address these challenges include improving technological infrastructure, training for teachers, and developing policies that support the integration of AI ethics in Christian religious education.

A Glance at Computer-Assisted Language Learning (CALL): A Bibliometric Analysis

Computer-assisted language Learning (CALL) is a learning method that uses computers as auxiliary media to facilitate language learning. This study aims to provide a comprehensive overview of the development of research in Computer-Assisted Language Learning (CALL) in the last five years based on the Scopus database. This quantitative study uses bibliometric analysis with the search keyword Computer-Assisted Language Learning (CALL). The results of this study highlight a significant increase in the number of articles about Computer-Assisted Language Learning (CALL) indexed by Scopus every year, starting from 142 articles in 2018 and 2023, which increased to 251. The type of publication is dominated by journal articles, followed by conference papers and reviews, and the rest in the form of books or book chapters. The largest source of publications comes from Lecture Notes in Computer Science (including the subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) with 29 publications. Our comprehensive analysis reveals a significant downward trend in citations from 2018 to 2023. Furthermore, based on the visualization using VOSviewer shows the use of AI technology in language teaching and the impact of digital media on language learning. The results of this study show that it is essential to continue to review and evaluate the factors influencing the dissemination and acceptance of CALL research. Moreover, it is crucial to reinforce the continuity between technological innovation and practical practice in various areas of life, thereby highlighting the real-world impact of CALL research.

Artificial Intelligence And Cancer Care in Africa

AI's potential to revolutionize oncology through enhanced diagnostics, treatment planning, and patient monitoring is well-documented globally. However, in Africa, its adoption has been slower, albeit steadily progressing. This commentary explores the integration of artificial Intelligence in cancer care across Africa, assessing its current state, challenges and future directions. It highlights significant AI innovations in cancer diagnostics, such as DataPathology, PapsAI, MinoHealth, and Hurone AI, which utilize AI for tissue analysis, cervical cell imaging, disease forecasting, and remote patient monitoring. Despite these advancements, several challenges impede AI's full integration into African healthcare systems. Key issues include data privacy and security, algorithm bias, and insufficient regulatory frameworks. The review emphasizes the necessity of robust data protection policies, representative datasets to mitigate biases, and clear guidelines for AI deployment tailored to the African context. Emerging AI technologies in Africa, such as AI-enhanced telemedicine, mobile health applications, predictive analytics, and virtual tumor boards, show promise in overcoming geographic and resource limitations. These innovations can facilitate remote consultations, continuous patient monitoring, and multidisciplinary collaborations, thereby improving cancer care accessibility and outcomes. Conclusively, recommendations for enhancing AI integration in African cancer care, including investing in data infrastructure, capacity building for healthcare professionals, and fostering international collaborations are discussed. Addressing ethical and regulatory challenges is crucial to ensure responsible and effective use of AI technologies. By leveraging AI, Africa can significantly improve cancer care delivery, reduce mortality rates, and enhance patient quality of life.

Applications and issues of artificial intelligence in the financial sector

This paper investigates the application of artificial intelligence in the financial sector, analyzing the existing technical, ethical, and legal issues, and proposing corresponding solutions. The research background highlights the widespread use of AI technology in the financial industry and the efficiency and cost benefits it brings. The research focuses on challenges related to data quality, feature engineering, model complexity, real-time capability, computational resources, and data privacy protection. The research method includes literature review and case analysis, revealing the applications of AI technology in stock prediction, risk management, trading strategy optimization, and customer service. The results indicate that effective data cleaning, automated feature engineering, model simplification and regularization techniques, the use of interpretability tools like LIME and SHAP, and the introduction of fairness evaluation standards can significantly enhance AI model performance and transparency. The conclusion points out that these measures can not only solve the current technical and ethical issues of AI in the financial sector but also promote the widespread application and standardization of AI technology in other fields.

Kindergarten parents’ perceptions of the use of AI technologies and AI literacy education: Positive views but practical concerns

Kindergarten parents’ perceptions of the use of AI technologies and AI literacy education: Positive views but practical concerns

Text-to-building: experiments with AI-generated 3D geometry for building design and structure generation

The paper seeks to investigate novel potentials for building design and structure generation that arise at the intersection of computational design and AI-generated 3D geometries. Although the use of AI technologies is exponentially increasing inside the architectural discipline, the design of spatial building configurations using AI-generated 3D geometries is still limited in its applications and represents an ongoing field of investigation in advanced architectural research. In this regard, several questions still need to be answered: how can we design new building typologies from AI-generated 3D geometries? And how can we use these typologies to shape both the real and the virtual world?The paper proposes a new approach to architectural design where artificial intelligence is used as the starting point for design exploration, while computational design procedures are employed to convert AI-generated 3D geometries into building elements – such as columns, beams, horizontal and vertical surfaces. The paper starts with a general overview of the current use of artificial intelligence inside the architectural discipline, and then it moves towards the explanation of specific AI generative models for 3D geometry reconstruction and representation. Subsequently, the proposed working pipeline is analysed in more detail – from the creation of 3D geometries using generative AI models to the conversion of such geometries into building elements that can be further designed and optimised using computational design tools and methods. The results shown in the paper are achieved using Shap-E as the main AI model, though the proposed pipeline can be implemented with multiple AI models. The paper ends by showing some of the generated results, finally adding some considerations to the relationship between human and artificial creativity inside the architectural discipline.The work presented in the paper suggests that the use of computational design tools and methods combined with the tectonics of the latent space opens new opportunities for topological and typological explorations. In a time where traditional architectural typologies are moving towards stagnation due to their inability to satisfy new human needs and ways of living, exploring AI-based working pipelines related to architectural design allows the definition of new design solutions for the generation of new architectural spaces. In doing so, the serendipitous aspect of AI biases is used as an auxiliary force to inform design decisions, promoting the discovery of a new inbuilt dynamism between human and artificial creativity. In a time where AI is everywhere, understanding the measure of such dynamism represents a key aspect for the future of the architectural discipline.

Problems of bias and discrimination of human capital in artificial intelligence systems

The article is devoted to the study of the genesis of understanding the causes of bias and discrimination of human capital from traditional forms to the era of digitalisation and artificial intelligence (hereinafter referred to as AI). In this article, the authors continue their case studies in the field of studying the ethics of AI, advantages and risks of its widespread distribution and use. The main purpose of writing this work is to study the question of how the use of AI and algorithmic solutions aggravates the problem of bias and discrimination of human capital which is inevitably associated with the emergence of AI. The authors of the article present a brief overview of the retrospective, discover new modern forms of discrimination generated by the action of AI and subject them to open discussion, offering their vision of solving issues on neutralisation the risks caused by the use of AI technologies in relation to certain groups of workers. The authors consider in the article the manifestations of bias and discrimination in society in general and in the field of human resource management in particular, identify possible threats of discrimination as a result of the spread of AI and the consequences of these threats.

DETEK-AI: A Web-based Deepfake Detection System

AI-driven deepfake bots pose a serious risk to society, security, and trust, which necessitates the development of reliable techniques to detect and mitigate their threat, as their proliferation leads to disinformation, deceit, and harm. Deepfake detection systems can improve cybersecurity, safeguard digital identities, mitigate misinformation, protect reputations of individuals and organizations, assure ethical use of AI technology, empower people and organizations, support regulatory efforts, and contribute to a safer digital ecosystem. Detek AI is a frontrunner in innovative technology aimed at combating deepfake content, safeguarding individuals and organizations from misinformation, and upholding the integrity of online platforms. Keyword— Deepfake Technology, Deepfake Detection, Convolutional Neural Networks, Artificial Intelligence bots, Machine Learning Artificial Intelligence, Face Morphing Detection, Deep Learning, Benford’s Law.

Integrating AI with emotional and social learning in primary education: Developing a holistic adaptive learning ecosystem

This paper highlights the significance, potential benefits, challenges, and proposed solutions associated with integrating AI-driven tools and platforms into SEL initiatives. The importance of integrating AI with SEL in primary education lies in its ability to foster the holistic development of students. By equipping students with the tools to navigate academic challenges, interpersonal relationships, and emotional regulation, schools can create dynamic learning environments that prioritize the whole child. The potential benefits of integrating AI with SEL are manifold. AI-powered adaptive learning platforms can personalize instruction, provide targeted support, and promote the development of emotional intelligence and social skills among students. Additionally, AI-driven tools and platforms can facilitate collaborative learning experiences, promote active engagement, and provide real-time feedback to students and educators. However, the integration of AI with SEL also presents various challenges that must be addressed. Ethical considerations, such as data privacy, algorithmic bias, and the digital divide, require careful attention to ensure equitable access and outcomes for all students. Additionally, educators may lack the necessary knowledge and skills to effectively utilize AI tools and platforms for SEL purposes, highlighting the need for training and professional development programs. To address these challenges, collaborative efforts among educators, policymakers, technologists, and researchers are essential. By working together, stakeholders can develop evidence-based practices and solutions that align with the goals and values of primary education. Training and professional development programs for educators, robust policies and safeguards for the ethical use of AI technologies, and equitable access to technology for all students are critical components of successful integration. Keywords: AI, Learning, Emotions, Social.