Artificial Intelligence

Since its inception in the late 1960s and especially following its commercialization in the mid-1990s, the Internet has experienced exponential growth. As artificial intelligence (AI) has surpassed previously inconceivable boundaries, it has become evident that AI both poses a threat to aspects of human existence and offers potential avenues for a more human-centric future. This paper contends that a digital existence, by its intrinsic nature, engenders a sense of discontentment. Paradoxically, within this digital realm, remedies emerge that elude offline experiences. While chatbots initially served as sources of amusement, their capacity to provide accessible and dependable counseling services has now become indispensable. Our assertion is that in the years to come, as chatbot technology advances further in sophistication, hundreds of millions of individuals worldwide will gain access to improved lives through the provision of free counseling services. Consequently, therapy will 

AI is developing so fast that philosophers of technology could not keep up with philosophizing it. AI promises to augment human capabilities, offering new insights and efficiencies. However, it also raises concerns about diminished autonomy and decision-making skills. Balancing AI's potential with ethical considerations is crucial to ensure it acts as a tool that enriches, rather than diminishes, human agency. In the present study, we interviewed a group of 62 tech-savvy professionals from Iran's technology sector to see how they think about the relationship between a much more powerful AI in the future and its relationship with human agency. Since these participants were -supposedly- more acquaint with AI and its capabilities, we decided interviewing them would yield important insights. After qualitatively analyzing our data, we came into five main categories of perspectives on AI and future of human agency: Augmentation and Enhancement, Displacement and Dependency, Collaboration and Partnership, Control and Ethics, and, Transformation and Transcendence. For each category, we provided examples from our interviews.

Artificial intelligence has brought about significant changes in various creative domains, sparking discussions about the nature of art and its authenticity in the era of AI. Some scholars assert that the computer monitor now serves as a canvas, a brush, a musical instrument, and even an art tutor, leading us to explore deeper connections between AI and creativity. However, in this presentation, we wish to emphasize the humanistic dimension of creative processes once more. we acknowledge the role of AI in enhancing creative endeavors, but we firmly believe that human creativity remains paramount in the production of artistic works. The current notion of machines replacing artists is, in our view, more of a media sensation than a reality. Examining the history of electronic arts, our paper argues that claims of AI's artistic superiority are not novel; they echo similar trends from the past. The current enthusiasm mirrors earlier media frenzies. While the sciences have made significant strides in unraveling the mysteries of the human brain, our understanding of the intricacies of our remarkably creative minds, their origins, and their fulfillment in our brains remains quite limited. Until these processes are thoroughly comprehended, artistic creation will continue to be a distinctly human endeavor.

The current study was conducted to identify the impact of artificial intelligence on sports marketing (applications and limitations). This research is a sequential mixed-methods study (qualitative-quantitative). The participants in qualitative phase consisted of 15 sport management experts who were interviewed. In the quantitative section, the statistical population included members of the sports management academic community that was selected through random sampling approach. Kolmogorov-Smirnov test, confirmatory factor analysis, and path analysis were used in the quantitative part of the study. The structural equation method with partial least squares method was used to measure the validity of the model, and the research hypotheses. According to the results of the study, there are 8 effective factors of artificial intelligence applications in sports marketing, including predicting sales and prices of sports products/services, identifying revenue growth opportunities, automating sales processes of sports products/services, personalized recommendation systems through analyzing customer behavior of sports products/services, tracking satisfaction with sports products/services, and advertising. Additionally, there are 5 factors identified as limitations of artificial intelligence in sports marketing, including limited understanding of human elements, concerns about data privacy, costs, and technical issues. The findings can help sports marketers to better utilize artificial intelligence for better delivery and customer satisfaction of sports products and services.

ChatGPT-assisted language instruction is gathering irresistible momentum in today’s fast-moving, AI-governed world of educational enterprises. The current scoping review article sought to analyze and synthesize the existing literature on the complementary use of ChatGPT in second/foreign language (L2/FL) education and pedagogy. Aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA‑ScR), the study adopted a systematic approach to evidential data search and analysis. A total of 28 peer-reviewed articles retrieved from well-established databases, such as Scopus and Web of Science (WoS), were reviewed to map the scope and consequences of the growing body of literature on using ChatGPT as an L2/FL learning/teaching tool. Centering around five predetermined variables, including setting, design, focus area, objective, and concluding remarks, the content or thematic analysis results helped to shape a clear picture of the literature under review. The literature-derived evidential data also referred to the fruits of ChatGPT-assisted instruction under three distinct headings: learning outcomes, pedagogical outcomes, and cognitive-affective impetus for language learning and teaching. The review results also revealed the repercussions of the instruction mainly concerned with ethical and technical concerns. Given the immature but fast-paced nature of the literature on the instructional model, this early attempt, though provided a brief sketch of the research scope and chief outcomes of the model, needs to be complemented with future systematic reviews to fully unlock the potential of language pedagogy well suited to the demands of today’s technology-governed education.

"English Language and Literature" courses are essential components of university education. They provide a significant avenue for understanding the politics, economics, and customs of English-speaking countries. These courses facilitate a mastery of English grammar, which in turn enhances students' comprehension of spoken and written English content. However, traditional modes of instruction in English Language and Literature often lack engagement and interactivity, thereby limiting the effectiveness of learning in this field. In order to boost learners' interest and efficiency in studying English, it is imperative to shift away from conventional teaching approaches. With the rapid advancement of artificial intelligence in various domains, its integration with English Language and Literature education can yield intelligent learning experiences. This study employs a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) to reform the teaching model in English Language and Literature. The results indicate that CNN and GRU methodologies offer substantial support in realizing intelligent approaches to teaching this field. These methods exhibit a high degree of similarity and accuracy in predicting linguistic features in English Language and Literature. They excel in terms of predictive and scatter error distribution, showcasing superior performance.  

Artificial intelligence enhances energy infrastructure efficiency, reduces waste in distributed energy systems, and ensures long-term durability. Additionally, green Internet of Things technology is positively linked to green innovation practices promoting sustainability. In this context, the primary objective of this study is to examine the impact of the Internet of Things and artificial intelligence on environmental sustainability performance, considering the mediating influence of green supply chain management within the hotel industry. This study is designed as applied research, and the data collection method is descriptive and correlational. The statistical population comprises hotel managers in Urmia City, with a sample size of 34 hotels determined using "Krejcie and Morgan" and "Cohen" tables. Sixty-four questionnaires were distributed among hotel managers in Urmia City. A standard questionnaire, validated by experts, was used to collect data. Its reliability was estimated using Cronbach's alpha coefficient. Structural equation modeling was used for data analysis, revealing that the Internet of Things and artificial intelligence positively impact green supply chain management. Furthermore, a positive relationship was observed between green supply chain management practices and environmental sustainability performance. This study suggests that adopting the Internet of Things and artificial intelligence technologies can enhance the efficiency of green supply chain management processes, emphasizing the importance for managers to grasp the impact of these two emerging technologies on overall supply chain performance.

This paper examines the effectiveness of implementing artificial intelligence (AI) in the Google Ads advertising service. The study analyzes the advantages and disadvantages of AI integration, focusing on attribution models and end-to-end analytics. The findings show that traditional metrics, such as CTR, CPC, and ROI, used to evaluate advertising campaign performance, exhibit significant statistical errors when AI tools are applied, with errors reaching up to 35%, exceeding typical business margins. A comparative analysis in the construction industry highlights discrepancies of 10% to 35% between traditional and AI-driven models. The study concludes that universal AI algorithms often fail to account for industry-specific dynamics, leading to inaccurate evaluations. The practical significance of this research lies in proposing an alternative approach that combines traditional evaluation methods with AI-based tools, offering a more reliable framework for assessing campaign effectiveness

In today’s world, there is a notable focus on incorporating disabled people within the community and the workplace. Advancements in artificial intelligence (AI) have the potential to significantly impact empowering individuals with disabilities to enhance inclusivity and autonomy for this demographic in the era of Society 5.0. This paper aims to offer a bibliometric analysis of the increasing number of publications addressing the potential impact of AI on disabled individuals and their future employment. To conduct this analysis, Scopus served as the bibliographic source, using Disability, Employment, and Future Workforce as the search terms, yielding 203 publications on the subject of study from 1973 to 2024. The analysis was conducted using VOS viewer. The results indicate that as Society 5.0 evolves, advancements in AI have the potential to significantly empower individuals with disabilities, enhancing inclusivity and autonomy for this demographic.

This paper provides a comprehensive overview of the evolving Artificial Intelligence (AI) threat to cybersecurity, emphasizing the urgent need for finance leaders and cybersecurity professionals to adapt their strategies and controls to effectively combat AI-powered scams and cyber-attacks. The study delves into the specific ways in which AI is being used maliciously in cybercrime, such as enhanced phishing and Business Email Compromise (BEC) attacks, the creation of synthetic media including deepfakes, targeted attacks, automated attack strategies, and the availability of black-market AI tools on the dark web. Furthermore, it highlights the critical need for enhanced cybersecurity strategies and international cooperation to combat cyber threats effectively. The findings of this study provide valuable insights for finance leaders, cybersecurity professionals, policymakers, and researchers in understanding and addressing the challenges posed by generative AI in the cyber threat landscape.

The advancement of artificial intelligence (AI) and its growing societal importance are reshaping decision-making processes and policy analysis roles. This study examines the readiness of manufacturing companies in Malaysia to embrace AI technology, considering its potential to enhance decision-making, productivity, quality control, job automation, and data analysis. Focusing on the Technology, Organization, and Environment (T-O-E) readiness framework, the research investigates the relationship between these dimensions and AI adoption readiness among manufacturing companies in Shah Alam, Selangor, Malaysia. AI adoption readiness serves as the dependent variable, while technological, organizational, and environmental readiness dimensions act as independent variables. The study applies the T-O-E framework to AI readiness and proposes a framework for assessing AI readiness at the manufacturing level. It identifies factors influencing readiness within the technological, organizational, and environmental dimensions, including relative advantage, compatibility, resources, competitive pressure, top management support, and government regulations. Through rigorous analysis, patterns, trends, and correlations are revealed, highlighting a significant link between the T-O-E readiness dimensions and AI adoption readiness. Notably, organizational readiness emerges as a key driver of AI adoption in Malaysian manufacturing companies. The results of this investigation have broad implications, offering suggestions to improve organizational preparedness and unlock AI’s potential benefits for businesses in the industrial sector. Additionally, the research lays the groundwork for further studies on AI readiness across various industries and international contexts. As AI becomes increasingly integrated into manufacturing processes, adaptive businesses gain competitive advantages on a global scale. These advantages include increased productivity, informed decision-making, streamlined quality control, improved customer satisfaction, and potential contributions to economic growth. The study concludes by recommending strategies to reinforce organizational readiness and emphasizes the need for future research to deepen understanding of AI adoption readiness in the manufacturing industry. The integration of AI technology offers benefits such as enhanced productivity, decision-making, quality control, and customer satisfaction, granting businesses a competitive edge in the digital landscape and increasing stakeholder interest.

The current research was conducted with the aim of providing a sustainable marketing model in the insurance industry with a focus on artificial intelligence. According to the type of data, the research method is mixed (qualitative-quantitative) of exploratory nature. In the statistical population, 12 people were selected from among the experts until the theoretical saturation was reached. The experts included professors of the faculty of business management and senior managers of Alborz insurance company, who were selected by non-probability, targeted and chain sampling. Quantitative part of 400 managers were insurance industry experts, using Cochran's formula, 196 people were selected by stratified random method. To collect data, semi-structured interviews and researcher-made questionnaires were used. The analysis method has been carried out in several consecutive steps. In the first stage, to identify the components of the model, the qualitative method and semi-structured interviews using the grounded theory have been used. In the next section, using interpretative structural modeling, the identified indicators were leveled. Finally, in order to fit the designed model in the qualitative part, a quantitative method with structural equation modeling approach was used with PLS4 software. The findings showed that 6 components were identified and there is a significant relationship between all components and sustainable marketing and the designed model has an acceptable fit. It can be concluded that AI can be incorporated as an essential component of a sustainable marketing strategy, enabling marketing professionals and businesses to make positive changes while increasing their brand reputation and profitability.

The purpose of this research is to provide a growth model of fixed assets based on the financial criteria of companies admitted to the Tehran Stock Exchange. The current research is applied in terms of objective classification and descriptive-correlation in terms of method. The research method is de-ductive-inductive. The statistical population of the current research is all the companies admitted to the Tehran Stock Exchange in the period from 2012-2021 and the financial information of 101 companies are use. Research hypotheses were tested using artificial intelligence algorithm. In this research, investment in fixed assets has been consider as a dependent variable, and financial criteria has been considered as primary independent variables. The results of research hypotheses testing using the methods of linear and non-linear algorithms of artificial intelligence PINSVR and KPLSR in predicting fixed asset investors of companies and by calculating the three errors criteria MAE, MSE and SMAPE in annual fixed assets. The asset forecasting in the next year of companies showed that the error difference between linear models and non-linear models is not so great that it can be claim that linear models are ineffective in predicting asset growth so that artificial intelligence algorithms are capable of predicting investment in company assets.

The purpose of this research was to evaluate whether or not Artificial Intelligence (AI) tools are effective in improving language pragmatics training for university students in Iraq who have an intermediate level of English proficiency. The research made use of a mixed-methods approach, which combines qualitative and quantitative data in order to investigate how artificial intelligence affects the acquisition of pragmatic competence (RQ1), learner perceptions (RQ2), and the effectiveness of AI in comparison to traditional methods (RQ3). The results of the study indicated that the intervention including the AI tool resulted in a statistically significant improvement in pragmatic competence scores when compared to the conventional method of instruction. Learners who were part of the experimental group also reported having favorable impressions of the effectiveness of the artificial intelligence tool. They highlighted the program's emphasis on practical application, as well as its interactive features and individualized feedback. This research makes a contribution to the field of language instruction by demonstrating the potential of artificial intelligence to develop a more interactive and individualized learning experience for pragmatics, which will ultimately lead to improved communication skills among students.

The evaluation of students' writings and the allocation of scores are traditionally time-intensive and inherently subjective, often resulting in inconsistencies among human raters. Automated essay scoring systems were introduced to address these issues; however, their development has historically been resource-intensive, restricting their application to standardized tests such as TOEFL and IELTS. Consequently, these systems were not readily accessible to educators and learners. Recent advancements in Artificial Intelligence (AI) have expanded the potential of automated scoring systems, enabling them to analyze written texts and assign scores with increased efficiency and versatility. This study aimed to compare the efficacy of an AI-based scoring system, DeepAI, with human evaluators. A quantitative approach, grounded in Corder's (1974) Error Analysis framework, was used to analyze approximately 200 essays written by Persian-speaking EFL learners. Paired sample t-tests and Pearson correlation coefficients were employed to assess the congruence between errors identified and scores assigned by the two methods. The findings revealed a moderate correlation between human and AI scores, with AI diagnosing a greater number of errors than human raters. These results underscore the potential of AI in augmenting writing assessment practices while highlighting its pedagogical implications for language instructors and learners, particularly in evaluating the essays of EFL students.

Artificial intelligence (AI) has a huge potential to transform industry and society. Its benefits are widely recognized, and it has become a tool of strategic importance for the European Union and a major driver of economic development. However, as with any technological development, it also comes with challenges that must be assessed and, if necessary, addressed by policy makers and businesses. Artificial intelligence has been a big challenge for the insurance industry for decades and is creating fundamental changes in the way this industry operates. The application of artificial intelligence to eliminate repetitive tasks and improve efficiency is visible in the market of porter insurance and commercial insurance. On the contrary, personalization of insurance through artificial intelligence is limited to personal lines and SME business. However, its application in commercial insurance including various marine levels will be widely involved in the near future. It is likely that AI will have a broad impact on the insurance value chain, from underwriting and claims management to distribution and customer service to asset management. As a result, insurance executives should be familiar with the new technologies involved in this change and how artificial intelligence can help organizations produce innovative products, gather valuable insights from new sources, streamline processes, and improve customer service. The purpose of this article is to make interested people and people involved in the insurance matter familiar with the potential benefits related to artificial intelligence applications and to motivate academics to study controversial topics in this field. For this purpose, we have only referred to the study of existing articles and research regarding the use of artificial intelligence in the insurance industry, especially marine insurance, and its effects on the compensation of losses caused by it.

This qualitative study examined the perspectives of Iranian clinical interns on the future of artificial intelligence in healthcare, with a focus on perceived opportunities, challenges, and the readiness of medical professionals for AI integration. A total of 64 clinical interns were recruited through snowball sampling from teaching hospitals across Tehran. Data were collected through semi-structured interviews and analyzed using thematic analysis to identify key insights regarding the participants' views on AI technologies in clinical practice. The findings revealed a general optimism about the potential of AI to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes. However, participants expressed concerns about ethical dilemmas, such as the impact of AI on clinical decision-making autonomy and patient privacy. Many interns highlighted the need for AI to complement rather than replace human judgment in medicine. Additionally, most participants identified significant gaps in their training regarding AI, emphasizing the necessity for curriculum reforms to better prepare future clinicians for working alongside advanced technologies.

Background: The integration of artificial intelligence into psychodynamic therapy presents a transformative opportunity to enhance mental health interventions.Aims: By consolidating evidence from diverse studies, this research underscores the theoretical and practical implications of psychodynamic AI systems and their potential to reshape therapeutic practices.Methodology: This study employs a meta-analytic approach to synthesize findings from existing research, focusing on the application of psychodynamic principles—such as unconscious processes, relational dynamics, and symbolic meaning—in AI-driven systems. Despite the dominance of cognitive-behavioral frameworks in AI therapeutic tools, psychodynamic approaches offer unique potential for fostering deeper emotional engagement and interpersonal understanding.Results: The analysis highlights advancements in AI-based systems designed to emulate psychodynamic processes and explores challenges, including technical limitations and ethical concerns. The findings emphasize the need for future research to refine AI's capacity to address complex psychodynamic phenomena while adhering to ethical standards, ensuring accessibility, and enhancing emotional intelligence.Conclusions: Ultimately, this work advocates for a balanced integration of psychodynamic principles with AI technologies to achieve scalable and effective mental health solutions.