AI Usage Research Articles

Usage of AI in Risk Management in Banking Industry

Usage of AI in Risk Management in Banking Industry

Framework For Performance Management, ICT-Based Digitalization in A Higher Education Institution

By improving teaching and learning, research, and governance, digital technology has altered the educational landscape in academic institutions. Adequate infrastructure, improved internet access, modern digital tools, a secure platform, and professionals with digital competency are all critically needed. Higher education institutions in India are clearly visible from the growing usage of ICT, cloud computing, AI, robotics, and virtual reality in daily operations, which improves competencies and aids in skill alignment with industry-based knowledge. Teaching in a regular classroom is becoming obsolete. The way that education is delivered has changed dramatically since the advent of technology. It provides the freedom for students to access learning at any time or location they want. The lines that were once drawn by understudies and educators being available in a similar spot simultaneously have likewise become more foggy because of education turning out to be more computerized. In the past ten years, internet and remote learning have grown in popularity in India. With the majority of the country now having access to digital technology, online learning courses are becoming more and more popular. Besides, understudy evaluation through digitalization technology is currently a chance on the grounds that to the improvement of learning management systems (LMS). Outside of the classroom, students can socialize with their peers. The pupils' soft skills have also been guided by the peer-centered learning component. LMS would enhance student evaluation and the teaching process.

Exploring excitement counterbalanced by concerns towards AI technology using a descriptive-prescriptive data processing method

Given the current pace of technological advancement and its pervasive impact on society, understanding public sentiment is essential. The usage of AI in social media, facial recognition, and driverless cars has been scrutinized using the data collected by a complex survey. To extract insights from data, a descriptive-prescriptive hybrid data processing method is proposed. It includes graphical visualization, cross-tabulation to identify patterns and correlations, clustering using K-means, principal component analysis (PCA) enabling 3D cluster representation, analysis of variance (ANOVA) of clusters, and forecasting potential leveraged by Random Forest to predict clusters. Three well-separated clusters with a silhouette score of 0.828 provide the profile of the respondents. The affiliation of a respondent to a particular cluster is assessed by an F1 score of 0.99 for the test set and 0.98 for the out-of-sample set. With over 5000 respondents answering over 120 questions, the dataset reveals interesting opinions and concerns regarding AI technologies that have to be handled to facilitate AI acceptance and adoption. Its findings have the potential to shape meaningful dialog and policy, ensuring that the evolution of technology aligns with the values and needs of the people.

ARTIFICIAL INTELLIGENCE IN EDUCATION: CASES OF USING CHATGPT 3.5

Formulation of the problem. This research explores the possibilities and peculiarities of employing artificial intelligence in general secondary education. A retrospective analysis of human utilization of artificial intelligence is conducted. Emphasis is placed on the relevance and significance of aligning the education system with contemporary demands and implementing new technological solutions. The focus is on the correctness of queries to systems and the necessity of analyzing work outcomes. Materials and methods. Data collection and processing were conducted during professional development courses for education professionals in the Chernihiv region (Ukraine) through pedagogical observation of course participants and their work with AI. Throughout the study, 34 training sessions were conducted, involving 748 teachers teaching various subjects. Among the showcased cases of AI usage, the most demanded queries among teachers are presented. Results. Examples of text analysis using the generative system ChatGPT 3.5 are provided. The obtained results are analyzed. The findings emphasized the necessity of clearly formulating queries and providing comprehensive input data and complete texts for analysis. In such cases, the artificial intelligence system can provide more accurate responses. This study demonstrated that the system handles this task quite successfully when providing the text of a program code and requesting commentary. However, solving mathematical problems poses specific difficulties, and obtaining the correct answer is only sometimes achievable. That reiterates the critical approach toward the system's obtained work outcomes. Conclusions. ChatGPT 3.5 can correctly analyze well-known facts and provide accurate information about them, but if the facts are local, the system cannot cope with the task. Simple math problems and tasks are performed correctly, but more complex ones already cause difficulties. It is essential not only to write prompts correctly but also to present input data. Such tasks as paraphrasing, translation, and changing the text's tone demonstrate modern AI systems' capabilities. ChatGPT 3.5 can become a good teacher's assistant or assistant for preparing various documentation. Requests for help in writing lesson plans, preparation of educational activities, formation of project topics, and technological maps of projects are carried out by the system at a high level.

What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study

The emergence of ChatGPT has sparked new expectations for AI-empowered educational transformation. However, it remains unknown which factors affect its effectiveness in empowering learners to solve programming problems. Therefore, this study employed a quasi-experimental research design and used Python graphing in programming education as an example to investigate the factors influencing the effectiveness of learners in applying ChatGPT to problem-solving. Findings: AI literacy significantly influences learners’ effectiveness in using ChatGPT to solve problems, with AI awareness and AI usage being key factors. The knowledge base of programming language significantly affects learners’ effectiveness in applying ChatGPT to solve programming problems. Learners’ cognitive level of ChatGPT significantly influences their effectiveness in applying ChatGPT to problem-solving, while usage intention does not have a significant impact. However, learners’ intention to use ChatGPT significantly improves after application. Based on these findings, this study proposes that in the process of empowering education with Artificial Intelligence Generated Content (AIGC) products, the focus on learners should shift from cultivating their AI usage to AI literacy, laying the foundation for empowering learning with AIGC products. It is suggested to shift from mastering specific knowledge to graph-based rules as a method for empowering learning with AIGC products. Additionally, the focus should shift from enhancing learners’ intention to use the technology to strengthen their technological awareness, thereby creating practical pathways for empowering learning with AIGC products.

Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students

While the discussion on generative artificial intelligence, such as ChatGPT, is making waves in academia and the popular press, there is a need for more insight into the use of ChatGPT among students and the potential harmful or beneficial consequences associated with its usage. Using samples from two studies, the current research examined the causes and consequences of ChatGPT usage among university students. Study 1 developed and validated an eight-item scale to measure ChatGPT usage by conducting a survey among university students (N = 165). Study 2 used a three-wave time-lagged design to collect data from university students (N = 494) to further validate the scale and test the study’s hypotheses. Study 2 also examined the effects of academic workload, academic time pressure, sensitivity to rewards, and sensitivity to quality on ChatGPT usage. Study 2 further examined the effects of ChatGPT usage on students’ levels of procrastination, memory loss, and academic performance. Study 1 provided evidence for the validity and reliability of the ChatGPT usage scale. Furthermore, study 2 revealed that when students faced higher academic workload and time pressure, they were more likely to use ChatGPT. In contrast, students who were sensitive to rewards were less likely to use ChatGPT. Not surprisingly, use of ChatGPT was likely to develop tendencies for procrastination and memory loss and dampen the students’ academic performance. Finally, academic workload, time pressure, and sensitivity to rewards had indirect effects on students’ outcomes through ChatGPT usage.

The quality traits of artificial intelligence operations in predicting mental healthcare professionals’ perceptions: A case study in the psychotherapy division

<p>As advancements in healthcare technologies continue to emerge, the integration of AI-Technology has brought about significant transformations in various healthcare sectors. While substantial advancements have been made in applying AI to enhance physical health, its implementation in the field of mental health is still in its early stages. This descriptive study aims to address this gap by exploring the perspectives of mental health professionals (MHPs) on the acceptance and utilization of AI technology. Unified Theory of Acceptance and Use of Technology (UTAUT) was utilized to assess MHPs’ attitudes and beliefs towards AI implementation in psychotherapeutic practices. The sample was compromised of 349 MHPs. The findings reveal the task characteristic (TC) domain as the most influential domain, followed by Performance expectancy (PE), Behavioural intentions (BI), Personal innovativeness in IT (PT), Social influence (SI), Effort expectancy (EE), Perceived substitution crisis (PSC), Technology characteristic (TECH), and Initial trust (IT). The study also identifies statistically significant differences in AI usage based on gender variable, with females demonstrating a higher level of AI usage in comparison to males. Furthermore, the study highlights diverse applications of AI in the field of mental health, including AI-assisted assessments (AAA), chatbots for psychotherapy support (CPS), and data analytics for personalized treatment recommendations (DAPTR). By incorporating mental healthcare professionals’ (MHPs) perspectives, this research significantly contributes to a comprehensive understanding of the acceptance and utilization of AI technology in psychotherapy. The findings offer valuable insights into MHPs’ perceptions, concerns, and perceived advantages associated with integrating AI technology within clinical settings in the field of mental health.</p>

Embracing AI in English Composition: Insights and Innovations in Hybrid Pedagogical Practices

In the rapidly evolving landscape of English composition education, the integration of AI writing tools like ChatGPT and Claude 2.0 has marked a significant shift in pedagogical practices. A mixed-method study conducted in Fall 2023 across three sections, including one English Composition I and two English Composition II courses, provides insightful revelations. The study, comprising 28 student respondents, delved into the impact of AI tools through surveys, analysis of writing artifacts, and a best practices guide developed by an honors student. Initially, the study observed a notable anxiety and mistrust among students regarding the use of AI in writing. However, this apprehension gradually subsided as students increasingly integrated these tools into their writing processes, indicating a shift from skepticism to practical application. The analysis of writing artifacts, particularly early drafts, revealed distinct patterns of AI tool usage, differentiating between students utilizing the tools effectively and those attempting to shortcut the writing process. The final papers, while not overtly indicating AI usage, demonstrated nuanced integration of AI in iterative and recursive tasks like refining arguments and developing ideas at the paragraph level. This suggests a trend towards a hybrid model of writing instruction, where traditional methods are complemented by strategic use of emergent technologies. The study underscores the importance of revised instructional strategies that blend conventional writing techniques with guidance on effective and ethical AI tool usage. It highlights the potential of AI tools in supporting the writing process while also cautioning against over-reliance. The findings of this study offer valuable insights for educators and institutions aiming to develop a balanced and effective hybrid writing instruction model, catering to the needs of contemporary English composition classrooms while maintaining academic integrity.

Exploring the impact of ChatGPT on education: A web mining and machine learning approach

ChatGPT, an artificial intelligence model, has garnered significant interest within education. This study examined public sentiment regarding ChatGPT's influence on education by utilizing web mining and natural language processing (NLP) techniques. By adopting an empirical approach and leveraging machine learning models to process 2003 web articles, the study extracts valuable insights. The results indicate that ChatGPT has emerged as a crucial educational tool, offering advantages for both students and educators. Notably, the study emphasized ChatGPT's role in enhancing students' writing abilities and fostering dynamic, interactive learning environments. ChatGPT's capacity to address a broad spectrum of questions demonstrates its versatility and adaptability, contributing to more inclusive and personalized educational experiences. However, the study also uncovered challenges tied to academic integrity, such as plagiarism and cheating, which stem from incorporating AI-driven tools like ChatGPT into education. This raises concerns regarding ethical aspects, including responsible AI usage and data privacy, and highlights the need for institutions to develop guidelines and policies for AI tool implementation in education. This study's findings hold theoretical and practical implications for integrating ChatGPT into educational settings. It is the first to employ web mining and NLP techniques to analyze public opinions on ChatGPT's impact on education comprehensively.

How to promote AI in the US federal government: Insights from policy process frameworks

When it comes to routine government activities, such as immigration, justice, social welfare provision and climate change, the general perception is that the US federal government operates slowly. One potential solution to increase the productivity and efficiency of the federal government is to adopt AI technologies and devices. AI technologies and devices already provide unique capabilities, services, and products, as demonstrated by smart homes, autonomous vehicles, delivery drones, GPS navigation, Chatbots such as OpenAI's ChatGPT and Google's Bard, and virtual assistants such as Amazon's Alexa. However, incorporating massive AI into the US federal government would present several challenges, including ethical and legal concerns, outdated infrastructure, unprepared human capital, institutional obstacles, and a lack of social acceptance. How can US policymakers promote policies that increase AI usage in the face of these challenges? This will require a comprehensive strategy at the intersection of science, policy, and economics that addresses the aforementioned challenges. In this paper, we survey literature on the interrelated policy process to understand the advancement, or lack thereof, of AI in the US federal government, an emerging area of interest. To accomplish this, we examine several policy process frameworks, including the Advocacy Coalition Framework (ACF), Multiple Streams Framework (MSF), Punctuated Equilibrium Theory (PET), Internal Determinants and Diffusion (ID&D), Narrative Policy Framework (NPF), and Institutional Analysis and Development (IAD). We hope that insights from this literature will identify a set of policies to promote AI-operated functionalities in the US federal government.

Estimating the environmental impact of Generative-AI services using an LCA-based methodology

As digital services are increasingly being deployed and used in a variety of domains, the environmental impact of Information and Communication Technologies (ICTs) is a matter of concern. Artificial intelligence is driving some of this growth but its environmental cost remains scarcely studied. A recent trend in large-scale generative models such as ChatGPT has especially drawn attention since their training requires intensive use of a massive number of specialized computing resources. The inference of those models is made accessible on the web as services, and using them additionally mobilizes end-user terminals, networks, and data centers. Therefore, those services contribute to global warming, worsen metal scarcity, and increase energy consumption. This work proposes an LCA-based methodology for a multi-criteria evaluation of the environmental impact of generative AI services, considering embodied and usage costs of all the resources required for training models, inferring from them, and hosting them online. We illustrate our methodology with Stable Diffusion as a service, an open-source text-to-image generative deep-learning model accessible online. This use case is based on an experimental observation of Stable Diffusion training and inference energy consumption. Through a sensitivity analysis, various scenarios estimating the influence of usage intensity on the impact sources are explored.

Generative AI Usage and Academic Performance

Generative AI Usage and Academic Performance

Virtual Tutor, Digital Natives and AI: Analyzing the impact of ChatGPT on academia in Indonesia

One of the chatbots of AI is ChatGPT which is emerging in academia for classwork, enhancing knowledge, and promoting the concept of learning through machine learning, which requires human intelligence. This study employed sequential explanatory design and conducted multiple in-depth interviews from two public universities of Indonesia to analyze the perception of students, teachers, and staff regarding the usage of ChatGPT. The study concludes that ChatGPT has substantial promise in revolutionizing higher education by offering an array of invaluable contributions. Teachers and students use it for different purposes, including research, exam preparation, and knowing future predictions about certain issues. The adaptation of ChatGPT generates personalized as well as tailored learning styles, which may generate explanations and create preferences for material and diverse information. On one hand, the usage of ChatGPT is considered either cheating or plagiarism while on the other hand, it is gained importance with regard to higher education and learning perspectives. The study also recommends that the Higher Education Commission (Dikti) needs to introduce the principles and regulations for AI usage in universities.

Doctor Who?: Norms, Care, and Autonomy in the Attitudes of Medical Students Towards AI Pre and Post ChatGPT

This study adopts the combined TAM-TPB model to investigate attitudes and expectations of machines at a pre-career stage. We study how future doctors (medical students) expect to interact with future AI machinery, what AI usage norms will develop, and beliefs about human and machine autonomy. Semi-structured interviews were conducted. Wave one occurred six-months prior to the public release of ChatGPT; wave two occurred in the 6 months following. Three themes emerged: AI is tomorrow, wishing for the AI ouvrier, and human contrasts. Two differences were noted pre- versus post-ChatGPT: (1) participants began to view machinery instead of themselves as the controller of knowledge and (2) participants expressed increased self-confidence if collaborating with a machine. Results and implications for human-machine communication theory are discussed.

The limitations and constraints of ChatGPT: an annotated bibliography for researchers and professionals

This annotated bibliography delves into the intricate web of limitations and constraints that surround ChatGPT, a pioneering natural language processing AI model. As AI technologies continue to redefine human-computer interaction, understanding the boundaries and potential pitfalls of these systems becomes paramount. This comprehensive resource compiles a diverse array of sources, including academic studies, reports, and analyses, published between December 2022 and August 2023 and dedicated to unraveling specific facets of ChatGPT's limitations and constraints. Annotated entries not only provide succinct summaries of these limitations but also offer critical assessments, shedding light on their significance, credibility, and real-world implications. The annotated bibliography aims to be an essential guide for researchers, developers, policymakers, and users, navigating the complexities of ChatGPT's constraints while fostering responsible AI development, informed decision-making, and ethical AI usage. In an era where AI plays an increasingly integral role in our lives, understanding the limitations and constraints of such technologies is not merely an academic exercise but a crucial step toward harnessing their potential for the betterment of society

Integration of Artificial Intelligence based on Electronic Information Engineering

Our analysis of the rapid development of core technologies in the new era of "Internet plus AI" is based on research into the applications of AI technology in the manufacturing sector in recent years. Next, we suggest novel approaches, methods, and configurations for intelligent manufacturing, as well as an architecture and technology system for intelligent manufacturing. In light of this, the purpose of this article is to examine artificial intelligence as it relates to Internet of Things technology in electronic information engineering. The goal is to increase people's material well-being and happiness by better optimizing artificial intelligence and IoT technology, mastering technical concepts, and applying them logically. To better simulate life and optimize information conditions, this paper analyses the experimental results through model construction. It also suggests ways to apply artificial intelligence and IoT technology to electronic information engineering. In conclusion, this research employs sixteen typical mechanics datasets to verify the correctness and generalizability of the method and concludes that it helps improve the usage of AI and IoT technologies.

Innovative insight: ChatGPT's contribution to advanced health research articles

The current technological advancement follows a pattern of both disruptive and gradual changes. The field of communication is currently the most extensively utilized technology. The rapid development of AI has enabled those without a background in computer coding or specialized health knowledge to utilize this technology, which gives rise to numerous issues. This phenomenon elicits curiosity and engenders debate among the general public, scholars, healthcare providers, and researchers in the health industry. Utilizing AI in the composition of scientific publications necessitates explicit norms and regulations, a robust validation procedure, and effective collaboration between AI systems and human expertise. Ensuring transparency and acknowledgment of AI usage is crucial to maintaining human researchers' accountability for the ultimate outcomes of their research.

AESTHETICS OF DEEPFAKE ‒ SPHERE OF ART AND ENTERTAINMENT INDUSTRY

The usage of artificial intelligence is one of the main characteristics of contemporary society. Deepfake is one among other forms of AI that uses artificial intelligence called deep learning to make images of fake events. This technology is not just about videos it is about photos and audio, too. It could be applied in different areas such as education, health care, art, commercials, film, propaganda, etc. Authors are interested in deep fakes used in the art and entertainment industry and their effects on audiences and aesthetics. The paper aims to show some characteristics of the aesthetics of deepfake in the context of the contemporary art and entertainment industry. By focusing on the aesthetics of contemporary society, we try to make an aesthetics analysis of deepfakes (Case study 1‒ Wearing Gillian, Gillian Wearing, 2018, Case study 2 ‒ Dali Lives, The Salvador Dali Museum, 2019) to show that contemporary art is more intimate with audience, thanks to usage of AI; also engagement of people is bigger than in traditional art. The conclusion is that contemporary art remains in the entertainment industry. It looks popular and closer to an audience; also, these spheres have a common and main aesthetic value- entertainment.

Artificial intelligence and visual discourse: a multimodal critical discourse analysis of AI-generated images of “Dementia”

ABSTRACT This article examines the ideologies reproduced in AI-generated images, focusing in particular on representations of dementia. Utilising Stable Diffusion version 1.4, a text-to-image AI model, we conduct a multimodal critical discourse analysis of 171 images generated from the text prompt “dementia.” Our analysis aims to identify and contextualise the visual discourses within the generated images by comparing these with existing multimodal representations of dementia. As well as observing a general lack of visual diversity (with an over-representation of older, light-skinned individuals), we find that these images tend to depict dementia by recycling existing, prominent visual discourses surrounding the syndrome, including a biomedical focus on the disease, narratives of loss, and dementia as a “living death.” These visual discourses combine with particular semiotic choices that promote emotional distance between viewers and people with dementia. Overall, this study highlights the potential for AI-generated images to reinforce and amplify harmful stereotypes and biases. As well as demonstrating the ideological import of such imagery, and thus the need for these to be critically interrogated by (multimodal) critical discourse analysts, this study underscores the need for ethical consideration in AI design and usage, including developing more diverse and inclusive training datasets.

대학에서의 AI 활용 가이드라인 현황 및 설계 방향 연구

In this study, we analyze and compare the ‘AI utilization guidelines’ currently being produced and distributed by major universities in the Republic of Korea. Based on this analysis, we present the direction that universities should pursue and maintain in terms of their AI utilization guidelines. The analysis yielded several important fi ndings, as follows. (1) Emphasizing the independence and responsibility of instructors and learners is essential. (2) Clearly distinguishing and outlining the roles of instructors and learners is crucial. (3) Both the positive and negative aspects of using generative AI in education should be addressed. (4) The guidelines should align with the educational mission of the university and serve the public interest. (5) Including a checklist for AI utilization within the guidelines is advisable. (6) Concepts, definitions, and characteristics of AI understanding should be introduced. (7) The ability to review generative AI responses should be cultivated, with an emphasis on the need for thorough reviews. (8) Ethical usage of AI must be required and explicitly stated. (9) Security concerns need to be clearly documented. Through this study, universities will be better equipped to identify and address issues related to the rapidly evolving fi eld of generative AI, including technologies like ChatGPT.