Artificial Intelligence Intelligence Research Articles

The role of institutional innovation and artificial intelligence in the corporate eco-innovation - a case study of high-tech enterprises in Liaoning province of China

This research investigates the intricate interplay of institutional factors and technological adoption in the context of corporate ecological innovation. Grounded in the New Institutionalism Theory and the Technology-Organization-Environment (TOE) framework, the study explores how formal and informal institutional innovations shape the behavior and capabilities of companies engaged in ecological innovation in Liaoning Province, China. Furthermore, the research examines the role of Artificial Intelligence (AI) in promoting ecological innovation. The study classifies AI into three categories: data analytics and predictive AI, automation and intelligent production AI, and customer service and user experience AI. These dimensions are investigated in terms of their influence on ecological innovation, revealing how each element contributes to enhancing companies' capabilities in addressing ecological challenges. The research proposes hypotheses regarding the positive influence of these AI dimensions on ecological innovation outcomes. The findings from this research contribute to the education of the intricate dynamics between institutional innovations, technological adoption, and corporate ecological innovation. The insights garnered are crucial for policymakers, businesses, and researchers striving to cultivate sustainable practices and innovation in ’the contemporary economic and environmental milieu.

Exploring EFL teachers’ emotional experiences and adaptive expertise in the context of AI advancements: A positive psychology perspective

The rapid advancements in Artificial Intelligence (AI) are reshaping language education, raising concerns about the professional future of English as a Foreign Language (EFL) teachers. While existing literature often highlights the effectiveness of AI tools, it tends to overlook their impact on teachers' relevant expertise development and emotional experiences. This longitudinal study addresses this gap by exploring EFL teachers' emotional landscapes and adaptive expertise development through the lens of positive psychology. Employing thematic analysis, the study draws on data from three rounds of semi-structured interviews with ten EFL teachers, complemented by students' post-class reflections and classroom observations. The findings highlight the pivotal role of positive emotions in EFL teachers' professional development, revealing a dynamic, non-linear interaction between positive and negative emotions as teachers navigate AI-integrated language education. This emotional interplay is significantly influenced by teachers' social connections and professional backgrounds. The study underscores the need for institutional support systems that promote positive emotions and enhance teachers' digital literacy. By deepening our understanding of EFL teachers’ emotional experiences and professional growth within the AI-integrated educational context, this study offers valuable insights for designing targeted interventions and curriculum reforms that foster effective collaboration between human and AI intelligence in global education.

Transformative Impact of AI on Education: Personalizing Learning and Addressing Challenges

Abstract: This article explores the transformative impact of Artificial Intelligence (AI) on education, focusing on its ability to personalize learning experiences and address longstanding educational challenges. It examines key areas where AI is making significant contributions, including adaptive learning systems, intelligent tutoring, accessibility improvements, and data-driven decision-making. The article discusses emerging trends such as augmented and virtual reality integration, emotionally intelligent AI systems, and lifelong learning support. Drawing on various studies and real-world implementations, it highlights the potential of AI to enhance student outcomes, support educators, and create more inclusive and efficient educational environments. While acknowledging the exciting possibilities, the article also addresses important considerations regarding privacy, ethics, and the evolving role of human educators in an AI-enhanced educational landscape.

Optimizing electronic health records to support artificial intelligence

Electronic health records (EHRs) provide the most important data sources for artificial intelligence (AI). Gaining access to quality data suitable for advanced analytics continues to be challenging. This rapid review documents the current state of available data; identifies foundational AI data/information needs; and explores the benefits of adopting new and emerging technologies to design and implement next-generation EHRs. Opportunities to optimize EHRs for AI purposes are identified. This review was informed by expert knowledge and shared experiences supported by the literature, including technical standards. Main findings include poor ecosystem-wide infrastructures due to the lack of adopting the right set of standards, and current data and knowledge governance no longer fit for purpose. While many jurisdictions are continuing the use of legacy systems, some forward-looking national health systems and health-care facilities are adopting transformational strategies by adopting a strong data and digital focus to transition to new-generation systems. New foundational-level national infrastructures with strong leadership and governance are essential to enhance the governance and quality of available data, from collection at source throughout the entire data supply chain. Secure and ubiquitous access to high-quality EHR data at scale will foster the evolution of more intelligent and trustworthy AI. Key characteristics of next-generation EHRs supported by currently available technologies and standards that are able to meet digital era demands are provided in this paper. We conclude that the use of generative AI in clinical settings can only be reliably achieved when EHRs are optimized throughout the entire global digital health ecosystem.

AI: The Future of Social Engineering!

Abstract: Artificial intelligence (AI) is at the forefront of computer science today. Everyone is talking about AI and how it is the way of the future. Companies are using machine learning (ML)algorithms to enhance their business offerings, which is showing promise in the realm of improved efficiency. The potential benefit of a fully developed AI is exceptional, but so are the threats that AI poses. While the developers of the various forms of AI are eager to be the first to create a fully functional, truly intelligent AI, they do not always consider the negative possibilities that AI creates. ChatGPT was recently used to hack itself and exposed a vulnerability in its open-source library. In addition to using AI to create hacks and exploits, AI is also being used to support social engineering efforts by creating more convincing social engineering attacks. Whether the attack is using AI to duplicate a person's voice to convince a loved one to send a gift card to get them out of jail or if it is being used to simply scrape a person’s social media to develop a more precise method of attack, the concern that AI will be used for nefarious purposes is genuinely profound. This paper is a case study looking into how AI is and will be used to improve social engineering. A literature review was conducted to identify how researchers are already seeing how AI is being used and to project future threats. AI is here to stay, and the threats it brings are existential, and it is imperative that these threats are realized, and defensive measures are developed. This case study looks at how AI is and will be used to improve the efficacy of social engineering attacks.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception.

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Developing Rapport between Humans and Machines: Emotionally Intelligent AI Assistants

Abstract: A new area of research that has the potential to completely transform human-computer interaction is the incorporation of emotional intelligence into artificial intelligence (AI) systems. Through the use of sentiment analysis, facial recognition, and voice tone analysis, AI assistants are now able to recognize and comprehend human emotions, allowing them to respond to users with greater emotional nuance and empathy [1] [2]. This opens up new avenues for therapeutic mental health interventions, emotionally supportive dialogues, and improved customer service encounters [3].

Artificial Intelligence in the 21st Century: Opportunities, Risks and Ethical Imperatives

Artificial Intelligence in the 21st Century: Opportunities, Risks and Ethical Imperatives

ВИКОРИСТАННЯ ШІ ДЛЯ ЗАХИСТУ ЦИФРОВОГО АКТИВУ ЩОДО УПРАВЛІННЯ БІЗНЕС ПРОЦЕСАМИ В УМОВАХ ЦИФРОВИХ ТРАНСФОРМАЦІЙ: ЕВОЛЮЦІЯ ТА МАЙБУТНЄ

It is important to note that in the conditions of the digital transformation of the economy, the management of business processes requires new innovative tools, including artificial intelligence (AI), which is actively being used in business and is becoming a competitive advantage. The relevance of the research lies in the fact that in the conditions of the formation of the digital economy, modern methods of managing business processes with the use of AI go beyond the framework of business infrastructure. After all, the use of mobile devices, cloud and hybrid services and the transfer of data about a business company to contractors and customers, and therefore information for solving business problems acquires commercial business value and due to the existing risks of its loss, needs protection. The purpose of the article is to study the use of artificial intelligence for the protection of a digital asset in terms of business process management in the context of digital transformations in the context of the evolution and future of AI. In the research process, methods of analysis, systematization and generalization were used. The risks of the loss of a digital asset in the conditions of digitalization due to cyberattacks, which the business receives in the event of information insecurity, are characterized. It is proposed to introduce a new economic term – a digital innovative asset, which is based on business, commercial value in information and communication technologies for managing business processes in the conditions of digital transformation of the economy and requires a reliable protection system. The stages of the evolution of artificial intelligence in the context of digital asset protection are defined. The methods used by AI to identify and predict threats to a digital asset for the purpose of protection are described. The practical value of the research lies in the effectiveness of the use of intelligent AI and machine learning in the protection of a digital innovative asset, which will become a practice and is especially important in the conditions of the formation of a digital economy. After all, predictive AI optimizes threat detection and creates effective cybersecurity solutions that are closely related to the threat landscape, which tends to constantly change and create danger for the digital asset, which is the basis of business process management, which will reduce risks and provide business opportunities. The vector of further research is the development of a global digital strategy for the protection of a digital asset.

The Application and Challenges of Artificial Intelligence in the Transportation Field

In this paper, we analyze the key role played by artificial intelligence (AI) technology in shaping the future of the automotive industry, aiming to provide a comprehensive guide for automotive enterprises embracing the transformative potential of AI integration. We begin by establishing the impact of AI in the transportation and automotive sectors, leading to an innovative AI solution: “Automotive Intelligent AI Assistant”. This solution harmonizes the functionalities of Advanced Driver Assistance Systems (ADAS) and Driver State Monitoring (DMS). The system aims to address inherent flaws in both systems while enhancing overall vehicle safety and performance. Through case analyses and challenge assessments, we show how enterprises can be empowered to gain a deep understanding of the implementation of this cutting-edge solution. Furthermore, we emphasize market potential, identify key concerns, and highlight the advantages offered by this integration.

Visual image design of the internet of things based on AI intelligence

Visual object detection has emerged as a critical technology for Unmanned Arial Vehicle (UAV) use due to advances in computer vision. New developments in fields like communication technology and the UAV needs to be able to act autonomously by gathering data and then making choices. These tendencies have brought us to cutting-edge levels of health care, transportation, energy, monitoring, and security for visual image detection and manufacturing endeavors. These include coordination in communication via IoT, sustainability of IoT network, and optimization challenges in path planning. Because of their limited battery life, these gadgets are limited in their range of communication. UAVs can be seen as terminal devices connected to a large network where a swarm of other UAVs is coordinating their motions, directing one another, and maintaining watch over locations outside its visual range. One of the essential components of UAV-based applications is the ability to recognize objects of interest in aerial photographs taken by UAVs. While aerial photos might be useful, object detection is challenging. As a result, capturing aerial photographs with UAVs is a unique challenge since the size of things in these images might vary greatly. The study proposal included specific information regarding the Detection of Visual Images by UAVs (DVI-UAV) using the IoT and Artificial Intelligence (AI). Included in the study of AI is the concept of DSYolov3. The DSYolov3 model was presented to deal with these problems in the UAV industry. By fusing the channel-wise feature across multiple scales using a spatial pyramid pooling approach, the proposed study creates a novel module, Multi-scale Fusion of Channel Attention (MFCAM), for scale-variant object identification tasks. The method's effectiveness and efficiency have been thoroughly tested and evaluated experimentally. The suggested method would allow us to outperform most current detectors and guarantee that the models will be useable on UAVs. There will be a 95 % success rate in terms of visual image detection, a 94 % success rate in terms of computation cost, a 97 % success rate in terms of accuracy, and a 95 % success rate in terms of effectiveness.

AI Dialog: Utilization, Challenges, and Ethics in the Age of Artificial Intelligence

In this writing, we will delve into every aspect of the utilization, challenges, as well as ethical considerations in harnessing Artificial Intelligence (AI) Technology, with a focus on ChatGPT, an exceptional language model. AI technology has become an inseparable element in modern life, manifesting across various sectors such as business, healthcare, governance, and several others. The capabilities of AI are capable of driving data-driven decision-making, reducing the potential for human errors, and enhancing process efficiency. However, its impact is also not devoid of potential risks, including data security and the replacement of human roles. To ensure intelligent AI usage, it is imperative to uphold AI ethics, protect user information and privacy, strive to prevent discriminatory practices, and ensure system safety stability. The questions raised in this study revolve around the benefits and challenges of implementing ChatGPT, optimizing its use, and its role in the realm of education. The mission of this study is to unravel the benefits, challenges, as well as ethical considerations in the utilization of ChatGPT in the AI era, while ensuring responsible steps in its application.

Building Socially Intelligent AI Systems: Evidence from the Trust Game Using Artificial Agents with Deep Learning

The trust game, a simple two-player economic exchange, is extensively used as an experimental measure for trust and trustworthiness of individuals. We construct deep neural network–based artificial intelligence (AI) agents to participate a series of experiments based upon the trust game. These artificial agents are trained by playing with one another repeatedly without any prior knowledge, assumption, or data regarding human behaviors. We find that, under certain conditions, AI agents produce actions that are qualitatively similar to decisions of human subjects reported in the trust game literature. Factors that influence the emergence and levels of cooperation by artificial agents in the game are further explored. This study offers evidence that AI agents can develop trusting and cooperative behaviors purely from an interactive trial-and-error learning process. It constitutes a first step to build multiagent-based decision support systems in which interacting artificial agents are capable of leveraging social intelligence to achieve better outcomes collectively. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Y. (D.) Wu extends her gratitude for the financial support provided through the RSCA Seed [Grant 22-RSG-01-004] from the San Jose State University. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4782 .

Investigation of Artificial Intelligence algorithms in education

The application of Artificial Intelligence (AI) in education has garnered increasing attention in recent years. This study investigates the primary methods of AI in education, including The Teaching Machine System, Intelligent Tutoring Systems, Intelligent Educational Systems, and the Intelligent College Student Comprehensive Quality Evaluation Model. The Teaching Machine System employs pre-defined rules and algorithms to deliver personalized instruction to students. Intelligent Tutoring Systems use AI algorithms to provide real-time feedback and guidance to students, tailoring the learning experience to address their learning gaps. Intelligent Educational Systems use AI to assist with instructional design, assessment, and delivery, while the Intelligent College Student Comprehensive Quality Evaluation Model is an AI-based system designed to evaluate students' comprehensive quality. Furthermore, this paper provides relevant descriptions and insights into the applications of AI in language and mathematics learning, highlighting the limitations of current AI models in these areas. Despite the advances in AI, challenges still exist, including limitations in processing unstructured data and the need for more effective human-AI interactions. Nonetheless, the potential of AI to enhance the quality and accessibility of education remains promising, and further research is needed to explore its full potential in the field of education.

The application of Artificial Intelligence (AI) in Mobile Learning (M-learning)

This paper study the application of artificial intelligence (AI) in mobile learning (m-learning). In the this report study we discuss the meaning of Artificial intelligent AI and Mobile Learning Materials, make them clear to people and students. then, explain the significance of the application of Artificial Intelligence in mobile learning materials. The use of “artificial intelligence” in mobile learning Materials a opportunity to breakdown the traditional borders of learning in the classroom to make students-instructors regarding teaching classes fixed to the future m-learning market. This report research introduce five problems in mobile learning.These problems make it necessary to apply artificial Intelligence in mobile learning materials. Furthermore, the development of mobile learning materials as part of ubiquitous learning in Open Learning Universities. Furthermore, how are students utilising Mobile Learning for open learning? Smartphone devices have evolved from a means of communication to tools for M-learning, open learning, work, socialisation, and entertainment as smart mobile technology has advanced and their affordability has increased. Mobile learning Materials are the most important thing for open learning students to make learning as simple as possible. Additionally, the impact of applying artificial intelligence to mobile learning materials in open learning faculty and students was investigated. Finally, the study discusses the possibility of creating Mobile-learning Materials for use in open learning. The data were gathered through the use of an E-survey administered by faculty, as well as interviews with students and a sample of each study group. The findings indicate that college students' perceptions of using mobile Learning Materials are improving, and they are willing to use mobile learning.

Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT

ChatGPT is revolutionizing the field of higher education by leveraging deep learning models to generate human-like content. However, its integration into academic settings raises concerns regarding academic integrity, plagiarism detection, and the potential impact on critical thinking skills. This article presents a study that adopts a thing ethnography approach to understand ChatGPT’s perspective on the challenges and opportunities it represents for higher education. The research explores the potential benefits and limitations of ChatGPT, as well as mitigation strategies for addressing the identified challenges. Findings emphasize the urgent need for clear policies, guidelines, and frameworks to responsibly integrate ChatGPT in higher education. It also highlights the need for empirical research to understand user experiences and perceptions. The findings provide insights that can guide future research efforts in understanding the implications of ChatGPT and similar Artificial Intelligence (AI) systems in higher education. The study concludes by highlighting the importance of thing ethnography as an innovative approach for engaging with intelligent AI systems and calls for further research to explore best practices and strategies in utilizing Generative AI for educational purposes.

Pengenalan Teknologi Artificial Intelligence (AI) dalam Meningkatkan Pengalaman Belajar Seni di Era Digitalisasi Pendidikan

Art education has undergone significant transformation in the era of digitalization. With the advancement of Artificial Intelligence (AI) technology, there is tremendous potential to enhance the art learning experience. This research aims to elucidate how AI technology can be effectively utilized to improve art education in the context of digitalization. The study aims to introduce AI technology and explore its potential applications in enhancing the art learning experience. In the context of art learning experience, AI technology can provide broader access to digital art collections, online galleries, and virtual museums. Students can explore various artworks and cultures through digital platforms, thereby enriching their understanding and appreciation of art. The research findings reveal that AI technology can offer personalized guidance to students in developing their artistic skills. Intelligent AI systems can analyze students' artworks, provide tailored suggestions, and assist in enhancing their technical and creative abilities. In conclusion, the integration of AI technology in art education holds immense potential to elevate students' learning experiences. By expanding access, providing personalized guidance, and creating interactive experiences, AI technology enriches art learning in the era of digitalization. This is expected to provide greater insights and understanding of how AI technology can be harnessed to enhance the art learning experience in the context of digitalization. By optimizing the potential of AI, art education can become more inclusive, personalized, and engaging.

How intelligent automation, service robots, and AI will reshape service products and their delivery

Intelligent Automation in form of robots, smart self-service technologies, wearable technologies, software and systems such as machine learning, generative artificial intelligence (AI) such as ChatGPT, and the metaverse are increasingly adopted in a wide range of customer-facing service settings. The shift toward robot- and AI-powered services will lead to improved customer experiences, service quality, and productivity all at the same time. However, these also carry ethical, fairness, and privacy risks for customers and society. In this opinion piece, we discuss the implications of the service revolution for service firms, their marketing, and their customers, and provide avenues for future research opportunities.

Exploring the Diverse Applications of Deep Learning Across Multiple Domains

This study examines the interdisciplinary connections that result from deep learning applications in artificial intelligence (AI) across several fields. A division of machine learning recognized as "deep learning" has proven to be remarkably effective at drawing out intricate patterns and representations from enormous datasets. Deep learning algorithms have made important improvements in fields as well as computer vision, natural language processing, robotics, finance, healthcare, advertising, and more by utilizing deep neural networks. This study’s goal is to evaluate the impact of deep learning in AI across disciplines and to draw attention to its revolutionary potential. The study examines the specific uses of deep learning in several fields and talk about the potential and problems that come with its deployment. The study provides a look at how precise object recognition, scene comprehension, and image production made deep learning transform the computer vision. Deep learning approaches have improved machine translation, sentiment analysis, and dialogue systems in natural language processing. This research study offers insights into the possibility of interdisciplinary cooperation by looking at the uses, difficulties, and future prospects of deep learning in AI. It demonstrates the transformative impact of deep learning in fostering creativity and advancement across a variety of sectors, opening the door for the creation of more intelligent and advanced AI systems.

Exploring Data-Driven Components of Socially Intelligent AI through Cooperative Game Paradigms

The development of new approaches for creating more “life-like” artificial intelligence (AI) capable of natural social interaction is of interest to a number of scientific fields, from virtual reality to human–robot interaction to natural language speech systems. Yet how such “Social AI” agents might be manifested remains an open question. Previous research has shown that both behavioral factors related to the artificial agent itself as well as contextual factors beyond the agent (i.e., interaction context) play a critical role in how people perceive interactions with interactive technology. As such, there is a need for customizable agents and customizable environments that allow us to explore both sides in a simultaneous manner. To that end, we describe here the development of a cooperative game environment and Social AI using a data-driven approach, which allows us to simultaneously manipulate different components of the social interaction (both behavioral and contextual). We conducted multiple human–human and human–AI interaction experiments to better understand the components necessary for creation of a Social AI virtual avatar capable of autonomously speaking and interacting with humans in multiple languages during cooperative gameplay (in this case, a social survival video game) in context-relevant ways.