Artificial Intelligence

The primary issue of this research was to investigate the relationship between artificial intelligence (AI) and user behavior patterns on social networks, the impact of AI on voting behavior, and the connection between the use of online data analysis and students' decision-making in presidential elections. This question arises while considering the influence of AI on user behavior patterns and decision-making in presidential elections. The objective of this study was to evaluate the impact of AI variables and online data on user behavior and decision-making in various contexts. This re-search examined the study hypothesis to better understand how behavior and decision-making are shaped in the digital age and the role of modern technologies in these processes. To conduct this research, a survey method and available library resources were utilized. The study used existing sources and collected necessary data from the target population, namely students at universities in Tehran, and employed descriptive and inferential statistics for data analysis. The findings of this research indicate that demographic variables and AI play a significant role in user behavior and de-cision-making, and the use of online data analysis can influence students' decision-making in presi-dential elections. Additionally, AI can be effective in detecting user behavior patterns on social networks and influencing voting behavior. These results can be useful for policymakers, election officials, and social activists in designing effective strategies to monitor user behavior and decision-making in the digital age, contributing to a better understanding of AI's role in shaping behavior and decision-making in society.

English for Specific Purposes (ESP) education has gained prominence as a specialized field designed to address the distinct linguistic needs of learners. Although AI-enhanced instruction has been investigated within general English language learning, its implementation in ESP contexts particularly concerning vocabulary recall and retention has remained insufficiently explored. This study sought to evaluate the impact of AI-supported instruction on vocabulary recall and retention among Iranian ESP learners. Based on non-random convenience sampling, a total of 48 undergraduate students in an ESP course from a university in a southeastern province in Iran participated in the present quasi-experimental study. The participants were non-randomly assigned into two different groups: an experimental group (n=24) receiving AI-powered vocabulary instruction through Diffit, and a control group (n=24) receiving traditional print-based vocabulary instruction. Data were collected during a twelve-week intervention through AI-powered instruction of ESP vocabulary. To address the research questions, a univariate repeated measures analysis of covariate (ANCOVA) was employed. Findings revealed that AI-enhanced instruction significantly improved vocabulary recall and retention among ESP learners. The results offer robust evidence supporting the efficacy of AI-based instructional approaches in enhancing vocabulary learning outcomes within ESP settings.

Artificial Intelligence (AI), particularly with machine learning and deep learning techniques, is revolutionizing human cognitive processes. From increasing accuracy and speed in performing repetitive and complex tasks to improving decision-making processes, AI has made significant strides in various fields including healthcare, business, and law. In healthcare, AI is accelerating medical diagnoses, and in business, it serves as a powerful analytical tool, increasing efficiency. In the legal field, it has optimized justice processes by providing public legal education services. The application of AI in teaching and learning processes is also significant; this technology has the potential to personalize the learning experience, provide 24/7 access to knowledge, and assist students in completing assignments with higher quality. Moreover, by providing new opportunities for teachers to optimize teaching content and assessment tools, it enhances their performance. However, there is a need for human oversight and guidance in the application of AI to maintain professional ethics and protect privacy. Ultimately, AI has the potential to have a lasting impact on the future of education by enabling personalized and targeted learning.

Purpose : The primary objective of this research was to integrate ChatGPT, an advanced artificial intelligence language model, into distance science education. By addressing the specific challenges inherent in online learning environments, this study aims to investigate how technological advancements, such as ChatGPT, can effectively enhance the management and overall quality of distance education experiences. Method : This qualitative, exploratory study aimed to provide a comprehensive understanding of the potential applications of ChatGPT in distance science education. This study identifies the various interactions that can occur in distance education and describes and evaluates the input and output instructions associated with each interaction. The chat bot used is https://chatbot.theb.ai and A biology tutoring platform uses ChatGPT to provide personalized support to students working on biology issues or concepts. Findings : Based on the results, the artificial intelligence can play an important role in improving distance science education. Furthermore, this study highlights the importance of developing metacognitive skills among learners and encourages educators to seek professional development opportunities to enhance the quality and engagement of distance science education. This study emphasizes the need for careful planning, research, design, evaluation, management, and responsible application of AI technology in distance education. Conclusion : This study was concluded that ChatGPT could be very useful for both learners and educators in distance education. This research showcases the innovative results facilitated by ChatGPT and provides valuable insights and practical guidance for educators, researchers, and policy makers on the effective use of AI technologies in distance science education.

Purpose : The purpose of the current research is to extract the components and dimensions of digital transformation and determine the levels and sequence of dimensions in Iran's education and upbringing, for the planning and implementation roadmap. Method : This research has a mixed approach (qualitative-quantitative). The qualitative section was able to extract the components of digital transformation by searching for authentic Persian and English articles published in the last ten years. With the help of seven experts, the digital transformation components were classified into eight dimensions in the form of 109 identified sub-components using the ISM method. Dimension levels were determined by SSIM matrix calculations in the quantitative part. MICMAC software version 5.3.0 was used to calculate the impact of relationships. Findings : Based on the opinion of experts and the results of ISM calculations, the identified dimensions including: institutional, education, technology, infrastructural, social, cultural and economic were placed in four levels. Conclusion : According to experts, the institutional dimension, being placed at the fourth level, has the greatest impact on digital transformation in Iran's education. Therefore, it is one of the key dimensions to achieve digital educational transformation that is placed at the first level of the model. According to the results of ISM, the economic and cultural dimensions were placed at the third level. Also, in analyzing the impact of relationships between dimensions using MICMAC software, the results indicate that the institutional dimension has a significant impact on infrastructure development.

Artificial Intelligence has is a pivotal tool in enhancing crisis management during natural disasters such as earthquakes. This article examines the role of AI in generating real-time information, predicting stakeholder and population responses, and optimizing decision-making processes. A meta-analysis of existing literature highlights AI's potential to transform traditional crisis management approaches by enabling rapid assessment of disaster scenarios, efficient resource allocation, and communication strategies tailored to situational dynamics. However, the integration of AI in such critical domains requires not only technological advancement but also a paradigm shift in the skills and competencies of crisis management professionals. This paper argues for the necessity of continuous education for stakeholders, emphasizing training in AI tools and data literacy to ensure effective utilization of these technologies in high-stakes environments. Furthermore, it explores the ethical implications of AI use in crisis contexts, including issues of accountability, data privacy, and bias mitigation. To address these challenges through sustained education and interdisciplinary collaboration, stakeholders should harness AI’s full potential while minimizing risks.

This study explores the perceptions of Iranian MBA graduates regarding the integration of AI into business education. Drawing on survey data from 379 alumni across a range of Iranian universities, the research assesses five core dimensions: perceived usefulness of AI, ethical concerns, faculty preparedness, job market implications, and interest in AI-focused coursework. The study also investigates whether demographic factors such as age, gender, income level, and marital status significantly influence these perceptions. Results indicate a generally favorable view of AI integration, with particularly strong support for incorporating AI coursework into MBA programs. Despite broad enthusiasm, respondents expressed concerns about data privacy, algorithmic bias, and the limited readiness of faculty to teach AI-related content. Inferential tests and path analysis reveal that demographic variables had little predictive power over AI perceptions or preferences, suggesting widespread acceptance across social strata. The findings underscore both the readiness of Iranian MBA graduates for AI-related curricular reform and the need for targeted institutional responses, including faculty development and ethical frameworks.

This study explores how AI is perceived and operationalized in credit risk assessment within Iranian banking institutions, with a particular focus on the experiences of electronic banking professionals in Tehran. Drawing on grounded theory methodology and semi-structured interviews with 38 practitioners from both public and private banks, the research reveals a complex landscape of technological promise and institutional constraint. Participants emphasized the efficiency, consistency, and expanded analytical reach afforded by AI models, particularly in leveraging alternative data and enhancing fraud detection. However, these benefits are tempered by operational challenges, including fragmented data systems, outdated IT infrastructure, and opaque algorithmic outputs. Ethical and regulatory concerns—especially surrounding algorithmic bias, accountability, and the absence of formal oversight—emerged as significant barriers to responsible deployment. Moreover, organizational resistance, hierarchical decision-making structures, and cultural skepticism toward automation further complicate adoption. The findings suggest strong practitioner support for hybrid decision-making models that integrate AI capabilities with human expertise. This model offers a viable pathway toward responsible innovation, balancing the computational advantages of AI with the contextual judgment and ethical sensitivity of human agents.

Artificial intelligence is transforming all aspects of human life and healthcare industry is one of the areas of change due to introduction of AI. This systematic review examines the perspectives of doctors on the use of AI in clinical settings, synthesizing findings from peer-reviewed articles to provide a comprehensive understanding of their attitudes, experiences, and concerns. Drawing on studies conducted across diverse healthcare environments, the review identifies prevailing themes, including the perceived benefits of AI in improving diagnostic accuracy, streamlining workflows, and personalizing patient care. However, it also highlights persistent challenges, such as ethical dilemmas surrounding accountability and autonomy, concerns about data privacy, and the potential for algorithmic biases. The findings reveal that while many physicians are optimistic about the transformative potential of AI, significant gaps remain in education and infrastructure that hinder effective adoption. Doctors frequently underscore the need for explainable AI systems, robust regulatory frameworks, and targeted training programs to address these challenges. This review contributes to the growing body of literature on AI in healthcare by offering insights into how medical professionals perceive and engage with this rapidly evolving technology.

The integration of artificial intelligence into Master of Business Administration (MBA) education is reshaping the foundations of management learning worldwide. This study conducts a qualitative meta-synthesis of 17 peer-reviewed sources published between 2001 and 2021 to examine how AI technologies—ranging from generative models and adaptive learning systems to predictive analytics and federated learning—are influencing MBA curricula, pedagogy, student engagement, and institutional strategies. Findings reveal that AI holds transformative potential for enhancing personalization, academic performance, and curriculum relevance, while also addressing enrollment challenges and labor market misalignments. However, integration remains uneven and fraught with ethical, infrastructural, and pedagogical challenges, including concerns over academic integrity, faculty preparedness, and digital equity. Conceptual frameworks such as the Technology Acceptance Model (TAM), paradox theory, and interpretive structural modeling elucidate the enabling and constraining factors surrounding AI adoption. The study highlights the urgent need for business schools to pursue a holistic and ethically grounded AI strategy that balances technological innovation with humanistic leadership development. Ultimately, AI is not only redefining how MBAs are delivered, but also reorienting their purpose toward preparing future-ready leaders for complex, data-driven environments. The paper concludes with a call for adaptive, inclusive, and ethically sound educational reforms in MBA programs worldwide.

This study presents a structured literature review on the application of AI in credit risk assessment, synthesizing empirical and conceptual research published between 2016 and 2022. It critically examines a range of AI models, including artificial neural networks (ANN), support vector machines (SVM), fuzzy logic systems, and hybrid architectures, with an emphasis on their predictive accuracy, robustness, and operational applicability. The review highlights that AI-based models consistently outperform traditional statistical techniques in handling nonlinear patterns, imbalanced datasets, and complex borrower profiles. Furthermore, AI enhances the inclusivity of credit evaluation by integrating alternative data sources and adapting to dynamic financial environments. However, the study also identifies ongoing challenges related to model interpretability, fairness, and regulatory compliance. By evaluating model performance metrics and methodological innovations across multiple contexts—including emerging markets, peer-to-peer platforms, and digital banking—the study offers a nuanced understanding of AI's strengths and limitations. The paper concludes with a call for balanced integration of explainable AI tools and ethical governance to ensure responsible deployment in financial institutions.

Previous studies have predominantly examined the representation of artificial intelligence in fictional literature, revealing a notable gap in analyzing cinema as an influential medium. The period before 1990 marked the onset of fundamental shifts in communication technologies and the public’s perception of technology, while the era after 2010 witnessed artificial intelligence becoming widely integrated into everyday life. These two historical ruptures have generated significant semantic transformations in cinematic portrayals that demand precise comparative and theoretical analysisThis study aims to conduct a comparative analysis of the “representation of artificial intelligence” in world cinema during the periods before 1990 and after 2010. Its central research question investigates the conceptual, narrative, character-development, and cultural-discourse differences and similarities surrounding artificial intelligence across these two eras. Twenty films (ten from each period) were selected based on their global acclaim and the centrality of artificial intelligence in their narratives. These films were analyzed using Saussurean–Peircean semiotics alongside Stuart Hall’s constructivist approach. The findings indicate that pre-1990 cinema predominantly focused on the “threat of technology” and the “human–machine war,” whereas post-2010 works emphasize “ethical crises,” “human–machine emotional relationships,” and “responsible coexistence.” Nevertheless, in both periods, artificial intelligence consistently functions as a “challenging Other,” perceived both as a threat and as a mirror reflecting humanity’s desires, hopes, and fears. The results underscore the necessity of rethinking the cultural-communication discourse surrounding technology and the shared future of humans and machines.