Integration Of AI Research Articles

Enhancing Supply Chain Sustainability: The Role of Blockchain and Digital Technologies

The aim of this study was to investigate the role of blockchain and digital technologies in enhancing supply chain sustainability. Data was collected through an extensive review of academic literature, industry reports, and case studies. One remarkable finding was that blockchain technology can reduce supply chain fraud by up to 80% by providing an immutable and transparent ledger. The research examined the integration of AI, IoT, and blockchain, highlighting their combined impact on improving operational efficiency, transparency, and reducing environmental footprints. Keeping in mind the fact that supply chains are becoming increasingly complex and environmentally impactful, the paper concluded that the adoption of these technologies is critical for achieving sustainable supply chain practices. The study underscores the need for addressing implementation challenges, such as high costs and data privacy concerns, to fully realise the potential of digitalization and blockchain in creating resilient, efficient, and eco-friendly supply chains. This research provides valuable insights for both academics and practitioners aiming to enhance supply chain sustainability through technological innovation.

Exploring college students' risk perception and acceptance intention of facial recognition technology in China

In response to China’s ‘smart campus’ trend, facial recognition technology (FRT) in higher education has evolved from identification to emotion recognition, raising various risks and controversies. Understanding college students’ perceptions is key to addressing these concerns and facilitating AI integration. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) model to gauge students’ attitudes towards FRT, introducing three new constructs: ‘personal innovativeness,’ ‘trust in the system,’ and ‘perceived risks.’ Results show that students’ personal innovativeness and trust in the system positively relate to their acceptance intention. Trust in FRT is hindered by perceived privacy, psychological, and performance risks, while perceived social risks are insignificant. Higher education shows less resistance to FRT due to normalized campus surveillance. Compared to Western societies, Chinese students’ Confucian cultural stance of ‘harmony but not uniformity’ reduces concerns about racial or religious discrimination from FRT. These findings are crucial for assessing FRT’s necessity and legality in higher education and devising risk mitigation strategies.

The “Cognitive” Architectural Design Process and Its Problem with Recent Artificial Intelligence Applications

The connection between cognitive science and the architectural design process reveals significant gaps that limit the full potential of creating effective built environments. A key issue is the insufficient integration of cognitive principles into design workflows. Architects frequently rely on traditional methods and aesthetic considerations without fully understanding how spatial configurations influence human cognition and behavior. While recent AI applications in architecture, such as Computer-Aided Drafting (CAD), Building Information Modeling (BIM), and interactive web and VR presentations, show promising advancements, AI still struggles with complex architectural functions. AI lacks the creativity and imagination inherent in human cognition. It operates based on fixed programming, producing specific outcomes and requiring human oversight to apply insights from one dataset to another. The primary challenge in using AI for architectural design is ensuring minimal design flaws, as replicating human cognitive abilities with AI and various machine learning techniques remains difficult. This research paper aims to explore the relationship between cognitive science, artificial intelligence, and the architectural design process through four main objectives: First, to investigate the integration of AI with current architectural software applications. Second, to examine potential connections between AI and major architectural design trends. Third, to define two frameworks for the Cognitive Architectural Design Process to guide the development of AI systems in architectural design by analyzing key cognitive design theories. Finally, to create a proposed "Architectural Design Process-Cognitive Pilot Map" from an architectural perspective to aid AI programmers in developing architecture design software applications.

Photoeducation strategy through emerging technologies

Excessive exposure to ultraviolet radiation, added to the large amount of time that children spend exposed to the sun, causes lesions on their skin, significantly increasing the risk of suffering from skin cancer or melanoma in adulthood; Emerging technologies, such as mobile apps and UV-sensitive bracelets, offer practical and effective solutions to address the lack of knowledge and awareness about the risks of sun exposure, transforming the way we learn and engage with the world. particularly preschoolers.Objective: propose an educational strategy through emerging technology with the integration of AI in health education programs, aimed at children under 5 years of age. Methodology: a qualitative, ethnographic study was carried out, with the participation of 13 caregivers, 1 teacher, considering inclusion and exclusion criteria. The data were processed manually for exhaustive knowledge and meticulous review that can guide a complete and accurate interpretation of the participants' experiences. Results and conclusion: the study highlights the need for comprehensive and preventive education in photoprotection, using innovative technological tools to reach both children and their parents and teachers. Emerging technologies, such as mobile apps and UV-sensitive bracelets, offer practical and effective solutions to the risks of sun exposure. However, for this strategy to be effective, it is essential that it be implemented in an educational environment that supports and reinforces its use.

"This Verdict was Created with the Help of Generative AI...?" On the Use of Large Language Models by Judges

In selected jurisdictions like the USA, China, and Singapore, the integration of AI into judicial proceedings has become a tangible reality. Algorithms play a pivotal role in supporting decision-making processes during the formulation of judicial verdicts. Large Language Models (LLMs), such as ChatGPT, are also being increasingly used by judges, often in circumstances where the legal parameters surrounding their usage remain unclear. This comment delves into the realm of interdisciplinary research inquiries surrounding the application of AI in the judiciary. It begins by exploring how judges currently use ChatGPT in rendering verdicts within cases that have been publicly disclosed so far. Subsequently, it navigates through the array of questions different disciplines might pose concerning this development and discusses strategies for harmonizing these diverse perspectives in collaborative projects, potentially employing frameworks like the “orders of change” taxonomy. The article concludes with a call for increased research endeavors, particularly emphasizing the need for interdisciplinary cooperation to comprehensively address the application of AI in the judiciary.

GenAI for Scientific Discovery in Electrochemical Energy Storage: State-of-the-Art and Perspectives from Nano- and Micro-Scale.

The transition to electric vehicles (EVs) and the increased reliance on renewable energy sources necessitate significant advancements in electrochemical energy storage systems. Fuel cells, lithium-ion batteries, and flow batteries play a key role in enhancing the efficiency and sustainability of energy usage in transportation and storage. Despite their potential, these technologies face limitations such as high costs, material scarcity, and efficiency challenges. This research introduces a novel integration of Generative AI (GenAI) within electrochemical energy storage systems to address these issues. By leveraging advanced GenAI techniques like Generative Adversarial Networks, autoencoders, diffusion and flow-based models, and multimodal large language models, this paper demonstrates significant improvements in material discovery, battery design, performance prediction, and lifecycle management across different types of electrochemical storage systems. The research further emphasizes the importance of nano- and micro-scale interactions, providing detailed insights into optimizing these interactions for improved efficiency and longevity. Additionally, the paper discusses the challenges and future directions for integrating GenAI in energy storage research, highlighting the importance of data quality, model transparency, workflow integration, scalability, and ethical considerations. By addressing these aspects, this research sets a new benchmark for the use of GenAI in battery development, promoting sustainable, efficient, and safer energy solutions.

AI Tools in LIS Research: Navigating Opportunities and Challenges for Scholarly Advancement

ABSTRACT This study examines the integration of AI tools in Library and Information Science (LIS) research to understand their implications on academic practices. Using a careful survey-based methodology, the study explores the advantages of AI tools, related challenges, and potential behavioural impacts, mainly focusing on whether they unintentionally promote satisfaction among researchers. Findings reveal an ideal position for AI tool utilisation, highlighting varied degrees of usage and perceived benefits like increased efficiency and expanded research scope. However, challenges encompass ethical concerns, integration complexities, and diverging opinions regarding the tools’ potential impact on researcher diligence. This study contributes significantly by addressing the less-explored behavioural implications of AI tool integration in research, offering refined insights into their multifaceted impact on academic practices. It underscores the need for a balanced and informed approach to AI integration, considering their transformative potential and ethical considerations in scholarly pursuits.

Scholarly apps: enhancing research mobility and accessibility

Purpose This paper aims to explore how scholarly apps are changing the landscape of academic research by increasing mobility, accessibility and user engagement. It assesses the role of mobile-based applications in supporting research activities, enabling access to scholarly content and facilitating seamless communication between researchers and libraries. Design/methodology/approach A brief content analysis of prominent scholarly apps was performed to evaluate their prime features and usability. Findings Scholarly apps improve research mobility by offering on-the-go access to academic resources, databases, communication tools, personalized content recommendations and integrated automated citation tools. The integration of AI and machine learning into these apps holds potential for further personalizing and streamlining research processes. Research limitations/implications Although certain discussed mobile apps are well known and frequently used by users, their value in the field of mobile technology remains significant. Despite their vast popularity, these apps continue to play a significant part in the mobile app ecosystem due to their proven efficacy, widespread acceptance and continuous relevance in meeting user demands. Originality/value To the best of the authors’ knowledge, this is one of the first studies to comprehensively evaluate the scholarly apps and contributes to the literature on mobile-based library services.

Synergistic Integration of AI and 6-DOF Robotic Arm for Enhanced Bomb Disposal Capabilities

Synergistic Integration of AI and 6-DOF Robotic Arm for Enhanced Bomb Disposal Capabilities

Using ChatGPT in Teaching Computer Programming and Studying its Impact on Students Performance

Given the recent emergence of artificial intelligence as an important topic that can contribute to improve curricula and lecture delivery, an increasing number of scholars are investigating its impact in various educational fields. The ChatGPT, which is an artificial intelligence model developed by OpenAI, represents a significant advancement in the generative artificial intelligence area. Since its announcement, integrating the ChatGPT into computer programming curricula - and into other scientific curricula - has yielded some challenges. The main challenge is clearly evident in the conduct of in-class tests and in-lab assignments, where the students are given specific tasks to be accomplished within a certain time frame. As they might seek help from ChatGPT, these types of assessments could be considered a potential threat to academic integrity and may be viewed as a form of academic dishonesty. This study aims at integrating ChatGPT into computer programming curricula, exploring its potential to enhance undergraduate education. It follows a mixed methods approach to examine the potential integration of ChatGPT in teaching computer programming as a supplementary tool. A quasi-experimental design is followed, in which an experimental group is allowed to use ChatGPT and compared to a control group that was not. A research sample of 26 undergraduate students (13 males, 13 females) from the College of Education at Sultan Qaboos University participated in the study. The methodology encompassed three research instruments: in-class exams, in-lab assignments, and semi-structured interviews. These research instruments were utilized to assess the impact of ChatGPT on students' academic performance, which served as both independent (use of ChatGPT) and dependent (student performance) variables. The quantitative analysis revealed a significant enhancement in students' performance, while the qualitative analysis of semi-structured interviews indicated that participants view ChatGPT as a valuable support for learning. Feedback from participants suggested combining ChatGPT with traditional teaching methods to optimize learning outcomes. This study highlights the feasibility and educational benefits of incorporating AI tools like ChatGPT into teaching methodologies. It suggests that such integration can provide a more engaging and effective learning environment, potentially revolutionizing computer programming education. This paper supports e-learning practice by integrating AI-driven tools like ChatGPT into the educational framework and advances the e-learning area by demonstrating these technologies' potential to improve student academic performance in the learning environments. However, the study also acknowledges the need for further research to explore the long-term effects of AI integration in educational settings and to address any emerging challenges. These findings propose a promising direction for future curricular enhancements and suggest an effective method for the integration of AI technologies to support and enrich traditional educational frameworks.

A comprehensive review of quantum image encryption methods: design characteristics, cryptographic properties, and AI integration

A comprehensive review of quantum image encryption methods: design characteristics, cryptographic properties, and AI integration

Generative artificial intelligence and ELT

Abstract There is undoubtedly, and understandably, a growing interest in incorporating generative artificial intelligence (GenAI) technologies into ELT. While advanced AI models have the potential to support language education, offering new tools and resources to enhance learning, their use also raises important questions regarding ethics and responsibility. As we follow the emergence of GenAI as another ELT tool, it is crucial to strike a balance between leveraging the benefits of these technologies and maintaining the core values of effective pedagogy. Educators must develop clear guidelines and best practices for the responsible integration of AI in the classroom, ensuring that it enhances rather than replaces human interaction and critical thinking. In this Special Issue on GenAI and ELT we explore some of the applications, their potential, and the challenges of incorporating GenAI in ELT.

University Teachers’ Views on the Adoption and Integration of Generative AI Tools for Student Assessment in Higher Education

This study examines the factors that may impact the adoption of generative artificial intelligence (Gen AI) tools for students’ assessment in tertiary education from the perspective of early-adopter instructors in the Middle East. It utilized a self-administered online survey and the Unified Theory of Acceptance and Use of Technology (UTAUT) model to collect data from 358 faculty members from different countries in the Middle East. The Smart PLS software 4 was used to analyze the data. The findings of this study revealed that educators developed new strategies to integrate Gen AI into assessment and used a systematic approach to develop assignments. Moreover, the study demonstrated the importance of developing institutional policies for the integration of Gen AI in education, as a driver factor influencing the use of Gen AI in assessments. Additionally, the research identified significant factors, namely performance expectancy, effort expectancy, social influences, and hedonic motivation, shaping educators’ behavioral intentions and actual use of Gen AI tools to assess students’ performance. The findings reveal both the potential advantages of Gen AI, namely enhanced student engagement and reduced instructor workloads, and challenges, including concerns over academic integrity and the possible negative impact on students’ writing and thinking skills. This study emphasizes the significance of targeted professional development and ethical criteria for the proper integration of Gen AI in educational assessment.

Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

AbstractArtificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.

Integration of AI / NLP for Improving Care Coordination

Integration of AI / NLP for Improving Care Coordination

Risk Management and Software Solutions in Financial Institutions: Move Towards Effective Implementation

Technological advancements and intensified regulatory pressures have led to a new level of the evolution of risk management in financial institutions. The entities have exposure to different categories of risks, such as credit risks, market risks, operational risks, and cybersecurity risks. The growing intensity of these factors makes proper risk management crucial for the stabilization and success of such enterprises. This report analyzes the best practices and software applications adopted by financial institutions for effective risk management. Improved risk identification, assessment, mitigation, and reporting within the organization would be through the integration of advanced analytics, AI, ML, and cloud computing. Based on this, the current paper assesses the part that these technologies play in contributing to compliance, cost savings, and better decision-making skills.

Future of B2B Credit Management: AI, Automation, and Real-Time Data Integration

Future of B2B Credit Management: AI, Automation, and Real-Time Data Integration

Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?

The integration of AI and ML into energy forecasting is crucial for modern energy management. Federated Learning (FL) is particularly noteworthy because it enhances data privacy and facilitates collaboration among distributed energy resources. It enables model training across multiple locations while minimizing reliance on centralized servers and data transfers. However, FL faces significant security challenges, particularly from adversarial attacks that can undermine the models' integrity and reliability. This paper addresses these security concerns by evaluating the effectiveness of Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL) in distributed load forecasting. We conducted a comparative analysis using two publicly available datasets, household data and smart grid data, for short-term load forecasting. Our study finds that DFL is more robust against adversarial attacks and artificial noise compared to CFL. Specifically, adversarial model poisoning attacks impact only the targeted client in DFL, while CFL is more broadly affected. For instance, in the presence of attacks, CFL's average client Mean Absolute Error (MAE) increased from 0.084 to 0.78 kWh for Dataset 1 and from 0.475 to 11.159 MW for Dataset 2, whereas DFL maintained relatively low error rates. Conversely, artificial noise had a less pronounced effect on CFL compared to DFL. Additionally, we introduce Decentralized Random Layer Aggregation (DRLA) to enhance DFL's robustness, offering further insights into improving FL methodologies in the energy sector. Our findings also indicate that only a single layer of the neural network is required for local updates during the aggregation process in DFL.

Exploring the perceptions of Chinese pre-service teachers on the integration of generative AI in English language teaching: Benefits, challenges, and educational implications

Exploring the perceptions of Chinese pre-service teachers on the integration of generative AI in English language teaching: Benefits, challenges, and educational implications

Impact of AI Integration on Journalists’ Mental Health: A Quantitative Study

Impact of AI Integration on Journalists’ Mental Health: A Quantitative Study