هوش مصنوعی

Artificial Intelligence can present a unique opportunity for developing economies, which often face capital shortages. On one hand, it can facilitate attracting investments, and on the other, it can increase investment returns. In the transformative landscape of venture capital, AI emerges not only as an investment opportunity but also as a transformative tool that redefines investment strategies and the operational efficiency of investment funds. Leading investment funds such as A16Z, Sequoia, and Capital Tiger leverage AI to not only improve their processes but also to discover hidden gems in the vast sea of investment opportunities. This novel approach to AI, using it to find investment opportunities for developing economies like Iran, presents an opportunity to attract foreign investment and invest in better positions. Therefore, macro-policies should be geared towards creating the necessary platforms for using AI to discover investment opportunities. This issue consists of four pillars: providing the necessary data, enabling data processing with AI to find suitable investment opportunities, upgrading the daily operations of funds, and providing effective signals to foreign investors to find a local partner. Additionally, the investor should provide the opportunity to use this technology for funds by defining appropriate rules for AI, including data privacy. The policymaker can actively participate in the formation of an investment database and the production of local software for fund operations to accelerate the use of AI for investment funds.

Artificial intelligence (AI) is one of the key technologies of the current era which plays a significant role in the advancement of countries. The development of this technology calls for appropriate infrastructure, which contributes to its effective application across various sectors. Despite the advancements in developed countries, developing nations face multiple challenges such as limited access to the Internet, shortage of experts, and bureaucratic hurdles. To fill this gap, this paper aims to provide a framework for the different levels of AI development infrastructure in Iran, designed in two phases. In the first phase, a preliminary framework for infrastructure was developed by reviewing international studies and experiences. In the second phase, semi-structured interviews with domestic experts were conducted, and their content was analyzed to propose a final infrastructure framework tailored to the conditions of Iran. This framework comprises six main infrastructural levels including High-speed data networks and exchange, Computational infrastructure, Data storage infrastructure, Core models and algorithms, Software and frameworks, and Applications, at two levels of AI service provider and aggregator operators. Considering the peculiarities of developing countries, particularly Iran, proper development of AI infrastructure seems unlikely unless government intervenes in favor of the establishment of AI service providers and service aggregators operators. Our findings provide insights and implications at the policy and management level for both policymakers and managers of the ICT sector, and particularly artificial intelligence.

Artificial Intelligence (AI) has emerged as one of the most significant transformative technologies in recent years, profoundly impacting various industries. In the oil industry, particularly in the upstream sector, AI has the potential to enhance operational efficiency, improve decision-making, and reduce costs. Given this importance, along with the strategic significance of the oil industry in Iran's economy, this study was conducted to assess the organizational readiness of Iran's upstream oil industry for adopting AI technology.In this study, a questionnaire was designed based on a selected framework, encompassing five main indicators: strategic alignment, resources, knowledge, culture, and data—each further divided into sub-indicators. These sub-indicators include AI business potential, customer readiness, senior management support, organizational process alignment, financial resources, skilled workforce, IT infrastructure, AI awareness, skill enhancement, AI ethics, innovation, teamwork, change management, data accessibility, data quality, and data flow. The questionnaire was completed by a group of experienced managers and experts in AI and the oil industry.The results indicate that strategic alignment received the highest score, while the data indicator scored the lowest among the main categories. The findings highlight the necessity of developing strategic programs and strengthening data infrastructure to ensure the successful adoption of AI in the upstream oil sector.

The expansion of artificial intelligence (AI) usage and the potential for damages arising from its application in various domains necessitate the establishment of conditions for compensating losses resulting from AI decisions and actions. This includes enabling direct transactions through AI and removing legal barriers to progress in this field. Consequently, the need to recognize legal personality for AI has emerged as a reasonable solution. Most legal questions regarding AI are related to the possibility of AI acquiring rights and obligations (i.e., having legal personality). This study employs a descriptive-analytical methodology based on library research to explore this issue. The findings indicate that while granting legal personality is theoretically possible, the current regulations in Iran do not support the extension of legal personality to AI. Furthermore, this research proposes that by anticipating legal personality for AI, it would be possible to utilize this recognition in formulating effective legal policies in Iran concerning active legal engagement in the field of AI. The discourse surrounding AI's legal status reflects broader concerns about accountability, responsibility, and the implications of recognizing entities that operate independently of human oversight. As such, establishing a framework for AI's legal personality could pave the way for clearer guidelines on liability and ownership in transactions involving AI systems.

This study examines the "Terms and Conditions" (T&C) of AI-driven businesses, aiming to identify relevant ethical issues and evaluate their compliance with AI ethical frameworks. The term "Terms and Conditions" refers to a document that regulates the contractual relationship between service providers and users, typically encompassing privacy policies, terms of service, user and company rights and obligations, as well as data protection policies. This research employed a cross-sectional study design and qualitative content analysis to identify and analyze Iranian businesses operating in the AI sector. The sample was drawn from platforms such as Myket and Cafe Bazaar, complemented by Google searches, and data were collected from official websites and relevant documents. The collected data were coded and analyzed based on ethical principles. In the quantitative phase, scoring was conducted using a Likert scale, and businesses were ranked using the TOPSIS method. The findings revealed that the T&C policies often lack sufficient operational details to build user trust and fail to fully uphold principles such as the balance of power, accountability, and social responsiveness. This study highlights the necessity of revising these T&C policies, developing more comprehensive frameworks, and aligning them with international standards.

The use of artificial intelligence in library software is a significant development in the field of information retrieval. This research aimed to study the comparative performance of Sana and Pars Azarakhsh libraries' software, specifically in the context of their use of artificial intelligence in the information retrieval process. The current research is practical in terms of purpose; a descriptive method with a content analysis approach and a fuzzy Delphi technique have been used to confirm the indicators and criteria of the use of artificial intelligence in the process of information retrieval in library software, the research tool is a checklist including six main features to evaluate the capabilities of the software, and 82 is a comparative measure. The validity of the research list tool has been measured using the opinions and advice of experts and competent people who have worked with the mentioned software. The findings of the present research show that in the examination of the ability to search in different fields, Sana 95.9% and Azarsa 97.7% and the examination of the search formulas Sana 70.8 and Azarsa 95.8%, in the examination of the search facilities Sana 61.9% and Azarsa 83.3% and the examination of the display of the search results of Sana 100% and Azarsa 86.8%, also in the examination of the capability related to the auxiliary software facilities of Sana 92.8% and Azarsa 90.4% and finally in the examination of the amount The benefits of software from artificial intelligence in the information recovery process were obtained by Sana 36.7% and Azarsa 28.2%. The findings of this research have practical implications for the use of AI in library software. Sana software, with a total of 538 points, was ranked second after Azarsa software in terms of its capabilities in the process of information retrieval. However, both software did not score well in terms of the benefit of artificial intelligence in the information retrieval process, indicating a need for further improvement.

Artificial intelligence offers highly suitable solutions for numerous challenges in urban transformation and development, such as ensuring an adequate water supply, energy management, waste management, and reducing traffic congestion, noise, and pollution. Given the social and technical nature of smart cities and the applications of artificial intelligence in this field, university research has seen a significant increase in recent years. Furthermore, an analysis of the popularity of keywords "smart city" and "artificial intelligence" on Google Trends indicates that these key terms have been increasingly popular from 2014 to the present. Therefore, this article systematically examines the current status and future directions of research on the application of artificial intelligence in smart cities through a sequential review method. To this end, the databases "Scopus" and "Google Scholar" were searched, identifying a total of 3384 articles. Following a systematic review and final screening process, 61 articles were selected for analysis. The findings show an increasing trend in research publications in this area from 2018 onwards. Additionally, an examination of the thematic scope of selected research indicates that the application of artificial intelligence in smart cities is predominantly focused on urban management and sustainable development (30%), smart living and intelligent infrastructures (28%), and intelligent environment (21%). The results also reveal that 64% of studies have employed qualitative methods, 21% quantitative methods, and 15% a combination of methods. As the application of artificial intelligence in smart cities is still in the conceptualization stage, the noticeable preference for qualitative methods among researchers in this field is evident. However, the advancement and expansion of artificial intelligence applications in smart cities have led to an increased use of experimental approaches and quantitative methods in the years 2023 and 2024. Furthermore, the analyses show that these selected studies are concentrated in three paradigmatic levels, namely specific theory with 46%, analytical methods with 29%, and empirical observations with 25% focus.

Considering the nature of the technology of software robots such as Chat GPT and their relationship with processing, retrieving and communicating knowledge and information, one of the areas that are affected by them is the field of higher education. This research has been done with the aim of identifying the key challenges of using Chat GPT in higher education and the upcoming opportunities for it as one of the artificial intelligence technologies. The current research is practical in terms of its purpose and the implementation process is such that first the challenges related to the subject are identified based on the literature review and then to reach the importance, leveling and connections between the challenges raised in the use of Chat GPT using the structural modeling method. Various challenges in the field of using Chat GPT in higher education, including incorrect information and low reliability, plagiarism, inequality of hardware and software equipment, lack of skills and knowledge of professors and students, lack of skills and knowledge of professors and students, the lack of application in some sciences, and the lack of emotional relationship between chatbots and humans were widely discussed. The results of the research showed that the challenge of inequality of hardware and software equipment and lack of skills, knowledge of professors and students at the first level, the challenge of incorrect information, low reliability and plagiarism at the second level and the challenge of lack of application in some sciences and lack of emotional relationship at the third level were placed.

Smart city technologies are an important element to effectively manage the rapid industrialization of today's world, as they can help solve the economic and environmental problems caused by the increase in urban population. Smart cities integrate traditional infrastructure and public services with technology to create a more efficient, sustainable and accessible system, while meeting the needs of city residents and transforming the traditional understanding of city management. Intelligent transportation systems, which are key components of smart cities, have been developed to improve transportation system safety, reduce environmental impacts, promote sustainable transportation development, and increase productivity. These systems provide modern solutions to transportation-related problems, including traffic and accidents, and use data collected from infrastructure, networks, and vehicles to help improve the efficiency of the transportation system and ensure the safety of citizens. Intelligent transportation system as an integrated transportation management system, consisting of advanced communication, information processing and traffic management technologies, can immediately process real-time data collected from heterogeneous sources and analyze them to facilitate effective decision-making. Machine vision is one of the most prominent applied subfields of artificial intelligence, which enables systems to extract meaningful information from digital images and other visual inputs, as well as make decisions and act based on this information. Machine vision, which uses deep neural networks based on machine learning, provides solutions that can be used in the process of automating transportation systems and increasing its safety level. It is conceivable that autonomous vehicle architectures and intelligent traffic management will dominate smart cities and transform transportation systems, and in this regard, the development of machine vision techniques will play an important role. In this article, the effectiveness of deep learning methods based on machine vision in traffic management applications, including automatic recognition and recognition of license plates, recognition and identification of traffic signs, recognition and classification of cars, recognition of pedestrians and recognition of road lines, has been investigated. Research from different sources and the investigation of the effective architectures of deep neural networks along with the determination of performance evaluation criteria for each one, it has been shown how the use of machine vision techniques in traffic management can have a significant impact on making transportation systems smarter in smart cities.

Businesses affected by digital transformations are facing new employee management and development needs. Employees in these companies not only need to acquire the right technical skills, but also have the mindset to help them cope with the new challenges of the digital workforce in the modern world. These changes and needs that are subsequently created in the development path lead to a digital transformation in the training of managers, as trainers and training professionals need to transition to new work forms to find, create and use digital tools to help future managers, companies and employees. The evolving literature of electronic human resource management expresses its challenges and potential. Stone et al. (2015) found that data-driven decision-making environments in the field of human interactions have a high ability to evaluate recruitment volunteers, improve staff levels, as well as provide digital tools for employee training and development. However, most studies in electronic Human Resource Management have concluded that more innovation is needed to improve the efficiency and performance of these digital tools.In 2010, ifenthaler stated that in the not-too-distant future, when learners become active builders of their learning environments, setting individual goals and creating content structures for the knowledge and content they want to master, wemay see the emergence of the true meaning of Constructivism (Ifenthaler, 2010) and that is now when eifenthaler mentioned it 12 years ago, and on this basis, the fundamental issue of research can be seen as the mismatch of the current situation.education and human resource development with new technologies. The digital age requires digital transformation in the most important context of humanity, the platform of teaching and learning. On the other hand, although the severity of the covid-19 pandemic has decreased and training has been resumed from the virtual platform, in the digital world and the volume of available data and the moment-to-moment updating of information, it is never possible to transfer them through face-to-face training. On the other hand, a person does not have the capacity to learn all the information and data available, so it is desirable that what he learns is based on his personal development, interests and expertise to make learning deeper and more effective. So this research seeks to address or adjust these issues to take a step towards improving the education and Human Resource Development situation in the country, and this will be achieved by designing a model of AI-based digital curriculum. To this end, the current research questions include:1. What are the components of AI from the point of view of commentators?
2. What is the concept of digital curriculum from the point of view of commentators? 3. What are the coordinates of the AI-based digital curriculum model?

Methods and Materoal:

Based on the purpose, the present research is applied, and in terms of data collection, it is a qualitative design. Among the various qualitative methods, the grounded theory method of the foundation was used with the constructivist approach of Charmaz. The current research community is all specialists in the field of curriculum, educational technology, educational technology and artificial intelligence, and the samples included 23 specialists. In order to collect information, semi-structured interview, observation and study of documents were used. In order to analyze the data in this research, the three-step method of Susanne Friese including noticing, collecting and thinking was done with the help of Atlas t.i software.

What are the components of AI from the point of view of commentators?The components of artificial intelligence consisted of 5 Main and 19 sub-categories. These include charting systems (algorithm, phase logic, classification), learning systems (supervised learning ,unsupervised learning, hybrid knowledge - based systems, reinforcement learning, learning from incomplete data), semantic systems (self-learning, semantic similarity, natural language understanding, prediction), control of complex systems (dealing with nonlinear problems, expert system), neural network model (problem solving, optimization, flexibility, reasoning).2. What is the concept of digital curriculum from the point of view of commentators?The concept of digital curriculum has 6 Main and 33 sub-categories. These categories include digital curriculum objectives (increasing the capacity of program design by teachers, developing cognitive skills, meaningful learning experiences, participatory learning opportunities, educational dynamics, research-oriented, educational justice, self-learning), digital curriculum features (stable yet flexible, transforming learning into a lifelong process, balancing the learner and learning environment, using technology in the classroom, digital teaching culture, high compliance capacity), digital curriculum tools (educational games, digital laboratories, electronic libraries, simulators, environmental features of the digital curriculum (interactive, flexible, classroom Networking lessons, personalization of the learning environment), digital curriculum resources (Smart Textbooks,personalization of learning resources, web-based resources, open educationalresources, textbooks), evaluation methods in the digital curriculum (online tests, video dialogue, video recorded by the learner, online critical texts, digital evaluation tools, quizzes).3. What are the coordinates of the AI-based digital curriculum model?phase curriculum model includes phase1 curriculum (learning based on specific pattern, classification and organization of content, linear learning, learning under external supervision, reinforcement learning and mutual understanding of language), phase2 curriculum (combined knowledge in learning, optimal building learning, learning from incomplete data, reasoning-based learning, predicting the learning process and facing learning problems) and phase3 curriculum (facing non-linear problems, deep learning, unsupervised learning, expertise in learning, semantic parallelism, self-directed learning and flexibility in learning).

Digital transformations have significantly changed teaching and learning practices. The present study examines the new needs of employee development and empowerment in the digital age, identifying the components of artificial intelligence and digital curriculum. The main objective of the present study is to define the components of artificial intelligence and then apply them in the form of digital curriculum elements. In other words, the digital curriculum in the workplace is defined by the components and functions of artificial intelligence.This model is designed based on the phase logic of artificial intelligence and can help to improve the design of the digital workplace curriculum. Based on the background studies, no research was found that could organize the digital workplace curriculum in this way, and therefore, the findings of the current research and the final output were completely unique.

Given the rapid acceleration of developments in emerging technologies, particularly artificial intelligence, and its extensive and profound impact on various aspects of social life, including higher education, the need for efficient, comprehensive, and coordinated policies is felt more than ever. This article examines the development trajectory of artificial intelligence technology and analyzes the opportunities and challenges of its use in the field of higher education. Considering the unparalleled opportunities that this technology offers to the educational system, as well as the inherent risks it carries, the necessity for smart and forward-thinking policy-making to improve and enhance Iran's higher education system has been emphasized. In this article, key strategies for formulating and implementing appropriate policies to optimize the use of AI technologies in Iran's higher education are presented. These strategies can serve as a roadmap for educational officials and policymakers in the country, helping to improve the quality, efficiency, and flexibility of the higher education system. The goal of these proposals is to achieve balanced and sustainable development of higher education with a focus on productivity and quality, utilizing the capacities of AI to enhance educational processes and train skilled and specialized human resources. The implementation of these policies can play a key role in facilitating the digital transformation of higher education and strengthening Iran's position on the international stage.

The objective of this study is to map the scientific landscape of Artificial Intelligence (AI) research outputs produced by researchers from Tehran-based universities, as indexed in Scopus. This research is applied in nature and employs a descriptive-analytical methodology, utilizing bibliometric techniques. To generate the scientific maps, tools such as VOSviewer software were utilized. The study analyzed scholarly data on AI published by researchers from Tehran’s universities in the Scopus database, focusing on publications from the 10-year period between 2015 and 2025. The findings of this research reveal that Iranian researchers are predominantly active in the areas of machine learning, neural networks, and natural language processing, with significant international collaborations with researchers from various countries. Furthermore, the highest number of AI-related articles have been published by major and prestigious universities in Iran. Ultimately, the analysis indicates a significant increase in AI-related scholarly output in recent years, with expectations of continued growth in the coming years. The findings of this study could assist policymakers and researchers in enhancing research strategies and fostering scientific collaboration within this field.

The idea of creating smart machines and artificial intelligence has been around for centuries and dates back to at least the 14th century. Although the application of artificial intelligence in education is a very new field, but during the last 25 years, artificial intelligence has made achievements in some fields. Which has also affected education of course, criticisms have also been raised against excessive optimism towards contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.

Methods and Materoal:

This study was done using SWOT analysis method and its data collection method is also a library

Text In general, artificial intelligence as a catalyst for teaching and learning with the help of computers is a field with many applications. The teaching of science, technology, engineering and mathematics subjects can be enhanced with artificial intelligence-based software systems. Another potential strength is the potential of AI systems to serve learners across schools, borders, and platforms in creating ecosystems of interactive learning tools. Additionally, AI systems in education may be used to evaluate different learning models throughout the school. Without strong artificial intelligence, tutoring systems cannot provide rapid feedback to learners and enable stimulating interaction. With a realistic view, weak to moderate and strong artificial intelligence have a good ability to support teaching and learning and facilitate the daily work of teachers.Intelligent learning systems often have less artificial intelligence than expected, especially when it comes to interacting with students. Baker (2016) in a critical position classified many of the existing education systems under stupid education systems. His concept for online learning is to enhance data-driven human intelligence rather than data-driven artificial intelligence. In order to more dynamically use AI in education, there is a need for training data, one of the problems that arise is how to ensure that the data is real and free from bias. As stated by Popenici and Kerr (2017), complex AI algorithms are designed by human programmers who are likely to include their own agendas or biases in the development of the system. An important aspect of high-level machine intelligence is that it customizes learning for each student, but in doing so it intervenes by standardizing content and what is expected of the student.As reviewed by Lakin et al. (2016), it is hard to see a future where teachers are replaced by artificial intelligence systems or robots. A more positive and realistic scenario is that the role of the teacher evolves and transforms, freeing teachers from tedious daily tasks. In addition, AI in education has the potential to relieve the teacher of the burden of having all the knowledge and information that can be relevant to students. A possible use of artificial intelligence in education in the future is in the form of robots (collaborative robots) that help teachers in their daily work and tailor the learning experience to each student, for example in recording and analyzing the work of these students. And report to the teacher. The use of intelligent learning systems can provide customized instruction or instant feedback to students at any time of the day. But the depth of customization is one of the truly critical features, not superficial and personalized learning. Studies show that developers of intelligent instructional systems have been successful in their goal of adapting and surpassing computer-assisted instruction (CAI) and human teacher training in raising student test scores.The negative change in the role of the teacher may be caused by the design of stereotypical courses with low-level multiple-choice questions and the use of teachers as content developers. Most school curricula and teacher training programs are not wellprepared to take advantage of the benefits of artificial intelligence in education due to not providing artificial intelligence courses to their teachers. If teachers are not trained in the use of artificial intelligence, this can lead to misuse of the technology, for example in protecting privacy and using personal data for influence. According to Nicholas and Holmes (2018), an ethical framework should be established for the use of artificial intelligence in education, and even if adopted, it should be continuously discussed and updated to allow for the capabilities and scope of artificial intelligence and the potential use of reflect it. A growing concern among many education workers is the fear of unemployment as high-level machine intelligence systems completely take over the teaching profession. According to Popenici and Kerr (2017), artificial intelligence currently has the potential to replace a large number of teaching assistants and administrative staff in education, and therefore it is more important to investigate its impact on education. Studies show that widespread use of high-level AI systems may disrupt students' ability to learn independently and develop 21st century skills such as problem solving and critical thinking. Finally, the most severe threat to students may be AI. Surveillance cameras with built-in facial recognition. Along with machine learning, facial recognition is one area where AI is advancing much faster than AI ethics. By using this technology, schools may collect students' biometric information, for example, under the pretext of reducing the many working hours that employees spend on registration and attendance. Support using artificial intelligence systems in education and robotics is certainly an opportunity, but social robots are still in their infancy and have limited social skills. In the near future, a realistic opportunity lies in the development of robots that can provide personalized content and rapid feedback. As in the manufacturing industry, teachers will soon be able to reprogram the cobots using block programming code that doesn't require advanced programming skills. Of course, there are also threats, and for purely economic reasons, we will probably experience cases where teachers are replaced by artificial intelligence solutions in education. Universities with financial problems may be tempted to try solutions, such as Deakin University in Australia, which offers a service where any student who asks can expect tailored information and advice. However, since the common concern is how to submit assignments and how to pay for parking, such systems pose a threat to administrative staff rather than teachers. Finally, as with AI in general, ethics is a major and immediate challenge in the use of AI in education, even though the threats posed by AI in education may not be as dramatic as in other AI areas. Automatic will not be useful. Quality teaching is a complex and creative profession involving improvisation and spontaneity where humans are not easily replaced. In general evaluation, it can be said that there are many ways that artificial intelligence can help students. From identifying signs of effort to creating a more interactive and personalized learning program.Here are four ways that artificial intelligence can have a positive impact on student learning; Personalized learning: The ability to respond to personalized learning needs is one of the most positive benefits of artificial intelligence in education. Artificial intelligence technology can easily adapt to different learning styles. AI technology can analyze students' past performance and create tailored curricula and settings based on past performance. When it comes to personalized learning, AI can also point students in the right direction for resources and other useful data and information. Artificial intelligence has the ability to provide personalized study plans for students without having to wait for interventions from learning professionals. All while meeting the overall goal of making learning easier and helping students engage with content more effectively. Ultimately, where AI really helps personalized learning is in its ability to reach students on a massive scale. With overcrowded classrooms at the elementary school level and classrooms of hundreds at the secondary level, AI can help personalize education for all students at once, making it easier for everyone to succeed. Tutoring: Sometimes students need extra help, and AI allows you to access on-demand tutoring without an in-person or live tutoring session. Because the AI uses algorithms to adapt, it can quickly change to cover the areas where students need the most support. Just like a human tutor who adapts to a student's learning style and ability to absorb information, AI tutoring systems are very useful in their ability to focus on improving and deepening student learning as a whole. The main advantage of AI-based tutoring technology is the ability to help students understand complex concepts and terms on a mass level. Finally, with artificial intelligence, access to tutoring is no longer limited to those who can afford it. In addition, instructors can spend less time helping those who do not understand the concepts. Assessment and grading: A large part of teachers' time is spent grading assignments. Artificial intelligence technology can help speed up this process. Additionally, when it comes to grading assignments, AI technology can help analyze and get feedback from students on things like grammar, content, and vocabulary. By removing this part of teachers' duties, they can focus on other aspects of teaching that are more important, such as lesson planning and student engagement. Finally, one of the biggest benefits of automated assessment is that it eliminates human error, biases, and mistakes. It can also give each student an outline of where they went wrong and how they can improve, without taking up extra time from teachers. Improving student interaction: Artificial intelligence can engage students in educational content and make learning more interesting. One of the ways that educators and teachers can incorporate artificial intelligence into the classroom is through the use of catboats. The ability of catboats to personalize and adapt to students' learning styles creates more opportunities to keep students engaged, and the fact that catboats can be accessed anytime or anywhere means that students they can work at their own pace and continue their learning outside of traditional classroom time. The fact that AI improves engagement is exciting for course planners and administrators. This means they can deliver highly personalized and interactive learning in their courses, regardless of the subject, helping to amplify the impact on people's lives. Discussed how artificial intelligence can be useful for students. In addition there is great potential impact on coaches and teachers – particularly in ways it can save time.The three advantages of artificial intelligence in education for teachers are: 1- Predictive analysis an interesting and emerging area of artificial intelligence in education is prediction. AI can analyze data and predict which students might fall behind due to the educational gap. Predictive analytics is exciting for educators because it means students struggling with learning challenges can be identified earlier and given the tools they need to succeed. Additionally, early intervention means that students who otherwise fail or struggle might have the opportunity to become successful students by giving them the right tools to help them succeed. 2-Advanced educational methods one of the methods of using artificial intelligence in education is to improve teaching methods. Today, due to the vast amount of content and information, teachers often have little time to organize alternative learning methods without spending more than hours of classroom time. Using artificial intelligence technology, teachers have the ability to quickly put together games and simulations that help students practice and learn the lessons being taught without spending more time on lesson planning. It saves a lot of time for teachers. 3- Facilitating evaluations and grading if you ask any teacher, they will tell you that assessment is one of the most time-consuming parts of the job. One of the exciting areas of artificial intelligence in education is the use of artificial intelligence technology to improve and speed up the assessment and grading process. For example, assessments can be done in real time instead of lengthy home marking. This not only saves time for teachers, but also improves students' understanding of the material in the moment.

The research findings show that there are both opportunities and threats regarding the role of artificial intelligence in contemporary education. In many ways, AI appears to have a promotional mode. But like other areas of advertising, it has the potential to grow with specific applications in educational and learning activities. The results of the research show that the awareness of artificial intelligence and the study of the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence instead of using artificial intelligence in education

This research aims to investigate the digital transformation strategies implemented at Tabriz University in response to the changing landscape of global education influenced by technological advancements.

Methodlogy:

 A qualitative approach with an interpretive paradigm was utilized for this study. Data was analyzed using thematic analysis based on semi-structured interviews with seventeen professors, IT experts, and specialists in virtual education and new technologies, chosen purposefully for their expertise. Validation of the research model was conducted through external review and pluralism strategies.

The study identified key digital transformation strategies at the university, including recognition of digital education needs, targeting evolutionary development of education and research, defining digital transformation tools such as cloud computing and artificial intelligence, and creating value for the university through program management and process integration.

The results highlight the importance of digital transformation as a dynamic strategy requiring technical, process, and cultural changes for effective implementation and advancement in education and research.

Value: 

This research contributes valuable insights into developing digital transformation strategies for universities, specifically in enhancing education and research practices within the context of technological advancements.

With the rapid development of science and technology, particularly in artificial intelligence, the application of this technology across various fields, including education, has grown significantly. AI has the potential to enhance and optimize learning and teaching processes, making classroom management more efficient. Furthermore, patents related to the development of AI in education are essential, as they safeguard the intellectual property rights of inventors. A review of research indicates that artificial intelligence (AI) has significantly enhanced the quality of education and learning, with numerous studies conducted in this field. However, no research has yet utilized patents to investigate the role of AI in education. The research questions examine the growth trends and citation patterns of patents in the field of artificial intelligence (AI) in education, while also assessing the scientific and technical strength of these patents. This study investigates the role of AI in education, emphasizing a comprehensive analysis of patents. The primary objective of this study is to analyze the growth trends, citation patterns, scientific and technical connections, and the overall scientific and technical strength of patents in the field of artificial intelligence in education.

This research is a scientometric study that uses a document analysis method. To collect data, all patents registered on the Topic of AI application in education without a time limit were collected from the Lens database. A total of 1267 registered patents were examined.

The results show that the number of patents related to AI in education, especially in recent years, has been steadily increasing. The number of citations to these patents has also increased, indicating their greater impact on related research. A significant portion of these patents have also referenced scientific sources, indicating a strong connection between academic research and technological advancements in this field. Indicators of science and technology links and technical strength also show a high level of innovation and novelty in this field.

The number of AI patents in education is continually increasing, and these patents are being cited more frequently, indicating their significant impact on further research and development. Patents illustrate the strong relationship between scientific research and technological advancements, highlighting a significant level of innovation and technical novelty in this field. AI plays a significant role in transforming the educational landscape, particularly in areas such as personalized learning, adaptive assessment, and intelligent instructional systems. The integration of AI in education can revolutionize teaching and learning processes, enhance student engagement, and provide tailored educational experiences. However, to ensure optimal use and mitigate potential risks, it is essential to consider ethical factors such as data privacy and the avoidance of bias. Consequently, artificial intelligence (AI) holds tremendous potential to transform education in a more effective and personalized manner. It should be employed responsibly and ethically to empower students and promote lifelong learning.

Considering the increasing development of new technologies, including artificial intelligence, the purpose of this research is to identify the opportunities and challenges of artificial intelligence in knowledge management based on the Hicks' four-stage model.

This research in terms of practical purpose, in terms of research method and descriptive analytical data collection is of a survey type, In terms of the basis of the type of data collected, it is of a quantitative type and uses the tool of a questionnaire to identify and prioritize the opportunities and challenges of artificial intelligence in Knowledge management. The experts of this study are 15 experts in artificial intelligence, computer and knowledge management. The research sampling method is judgmental and the samples were selected based on expertise and the research criteria were extracted from the review of internal and external studies in the field of artificial intelligence and knowledge management. The current research was carried out in three stages. In the first stage, the indicators of opportunities and challenges of artificial intelligence in knowledge management were obtained through literature review and interviews with experts and were modeled based on the Hicks'knowledge management model. Then, in the next step, these criteria were screened using the fuzzy Delphi method. Finally, the chatbots studied in the research were prioritized through the most key indicators using the gray relationship analysis method.

It seems that the research findings indicate that artificial intelligence presents both opportunities and challenges in the realm of knowledge management. The study identified fifteen indicators related to opportunities, including four indicators in knowledge creation, three in knowledge sharing, five in knowledge reserve and retrieval, and three in knowledge use. On the other hand, fourteen indicators were identified as challenges, including three indicators in knowledge creation, three in knowledge sharing, three in knowledge reserves and retrieval, and five in knowledge application.

Based on the findings of the research, it was determined that both in the category of opportunities and in the category of challenges, Chat Bard was ranked first, and with a small difference, Chat GPT was ranked second, and then Chat Bing was ranked third, so that It can be said that among chatbots in the knowledge management process, chat bard can be the best option to use.However, in the opportunities category, the first rank indicates that there are more opportunities and benefits compared to the other two ranks. Similarly, in the challenges category, the first rank represents more challenges and issues compared to the other two ranks.

The article examines the scientific production of artificial intelligence (AI) in Iran and identifies the top scientific productions of AI in the field of macro-research topics at the national level.
The research method was descriptive-applied with a Scientometrics approach. The paper reviewed 804 Persian works on Artificial Intelligence (AI) in the Islamic World Science Citation Center (ISC) between 2002 and 2024. To index thematic articles with a quantitative approach, Scientometrics techniques were used to define thematic terms through semi-automatic indexing. A total of 357 keywords, repeated 2807 times, represented the research topic.
Scientific productions have been done in six subject areas: Humanities (285 articles), Engineering (182 articles), Agriculture (125 articles), Medical (102 articles), Environment and Natural Resources (62 articles), Science (48 articles). The most frequent topics in Humanities include stock market, economic development and planning, money and financial institutions, financial management, accounting, auditing and law, economics, information management, and human resources management. In Engineering, topics such as structure, hydraulic structures, transportation, water resources, information technology, marine structures, data processing, earthquake, control, and identification technology were prevalent. In Agriculture, topics covered include agriculture management, water resources, irrigation, soil science, drainage, waste management, environment, soil protection, and mechanization and mechanics of agricultural machines. Medical topics encompass general medicine, diseases, medical science, health, biotechnology, psychiatry, clinical psychology, pathology, physiology, and neuroscience. Environment and natural resources topics include watershed management, natural resources management, watersheds, environment, ecology, soil protection, forest and forestry, environment management, desert, and environment pollution. Science topics cover geology generalities, geochemistry, stratigraphy, sedimentology, applied mathematics, social aspects of sciences, geophysics, science and technology, and applied geology, which were identified as the most important topics emphasized by AI scientific productions in Iran. Humanities and Engineering were among the first fields to have scientific works in the field of AI in ISC since 2002. Since 2008, Science and Agriculture in ISC have published works in the field of AI, environment, and natural resources, succeeding in producing valid scientific works in the field of AI since 2009. Compared to other major research fields, the medical field entered the AI field with a delay, and its first productions are from 2011. In all major research areas of Iran, the trend of scientific production of AI has been growing. Humanities, as the oldest field, and Medical, as the youngest field, have shown a better growth chart than others. Water Resources Engineering Journal and Amirkabir Journal of Civil Engineering, with the most published articles in the field of AI (12 articles), were recognized as the best in this field in Iran. Signal and Data Processing and Water and Soil Science ranked second by publishing 11 articles, and the Journal of Water and Soil Conservation ranked third by publishing 10 articles in the field of AI. The University of Tehran was recognized as the most prominent university in Iran with the most AI scientific productions in Humanities, Environment, Natural Resources, and Science, while the University of Tabriz won the first rank in AI scientific productions in Engineering and Agriculture. Isfahan University of Medical Sciences achieved the highest position of AI scientific products in Medical. Roya Darabi, a prominent figure in the field of Humanities, published 8 articles; Vahid Nourani, with the publication of 8 articles, is a prominent figure in the field of Engineering; Kiyoumars Roushangar, with the publication of 4 articles, is a prominent figure in the field of Agriculture; Javad Haddadnia, with the publication of 3 articles, is a prominent figure in the medical field; Jamshid Piri, with the publication of 3 articles, is a prominent figure in the field of environment and natural resources; Ata Allah Nadiri was recognized as a prominent figure in the field of basic science in AI scientific productions by publishing 5 articles.
The findings of the current research offer insights from Iran's research perspective in the field of artificial intelligence, providing valuable information for the scientific community, technology, and innovation policy makers. The analysis, derived from observing Iran's artificial intelligence scientific productions at ISC, is of interest not only to artificial intelligence experts but also to other researchers and the general public. By identifying notable cases in the field of artificial intelligence in Iran, a strategy can be developed to facilitate further cooperation and interaction, aiming to achieve optimal results.

The purpose of this research is to deal with some artificial intelligence solutions to help improving the usability of library websites for visually impaired users.

The current research is a qualitative that was conducted with the interview and think aloud protocol. In this research, through interview with visually impaired users, the best and worst features of library websites were questioned. Users were observed while using a library website and performing daily tasks while verbally expressing their thoughts, feelings, and opinions about their interaction experience. The interview was conducted individually and online or at a specific location. 4 large library websites of the country (the National Library and Archives of Iran; Organization of Libraries, Museums and Documents Center of Astan Quds Razavi; Library, Museum and Document Center Islamic Consultative Assembly and the Central Library and Document Center of Tehran University) were selected to perform the tasks. The research community was visually impaired users who were selected using the targeted sampling method in the number of 33 people. The analysis of the users' conversations was done based on qualitative content analysis, and the MaxQDA software was used and placed in 90 final codes and 3 general categories and 8 subcategories. These codes were continuously expanded and revised while reviewing the transcripts. Another researcher participated in the content analysis and reviewed the transcripts and extracted categories, and the data were evaluated several times.

The best features of the websites from the users' point of view were: standard and accessibility of the elements on the pages, valuable content and honesty in introducing and presenting the content, logical segmentation and headings and proper organization of page elements, allocation of Alt tag for graphics, optimal organization of results search and... the worst features of the websites were: image and unknown security codes (captchas), lack of automatic correction of keywords spelling mistakes, problems of online conversations and unknown sending and receiving messages, dynamic content, lack of adjustment of elements on the page with the keyboard, not having labels for graphics and user input elements, and non-principled page design, etc.

Automatic correction, intelligent voice assistants, result clustering, intelligent filtering, intelligent question and answer, text and image processing/image description, text summarization, semantic search and natural language and user interface personalization are some of the ways to improve the usability of library websites.

In recent years, we have witnessed the emergence of a large number of smart products and services, their commercial availability, and their socio-economic impacts on the world. The use of artificial intelligence can enhance the performance of start-up businesses. Considering the role and impact of artificial intelligence in accelerating entrepreneurship and the development of start-up businesses, the aim of this research is to analyze the challenges and opportunities of artificial intelligence on the development of entrepreneurship and the growth of start-up businesses.

This research employs a qualitative approach and a descriptive purpose. It utilizesin-depth semi-structured interviews to develop and validate a conceptual framework using thematic analysis method. The statistical population consists of experts and entrepreneurs in the field of start-up businesses. A total of 12 individuals were purposefully selected to participate in this study. The number of samples follows the principle of saturation. In order to ensure the validity of the research, the respondent method was utilized. A statistical sample of individuals with the necessary knowledge and expertise in the research field was selected to minimize researchers' intervention and to calculate the reliability of the double coding method. The symbol "%" has been used, which is equivalent to 79%. Considering that the reliability level is over 60%, the coding's reliability has been confirmed. It can be asserted that the reliability level of the current interview analysis is appropriate. This article offers some ideas on how to adapt opportunities and practices to emerging AI technologies, including: protecting employee data, ensuring data quality and volume, institutionalizing the appropriate training phase for learning, identifying and modifying patterns, and preventing the input of biased data.

The current research explores the role of artificial intelligence in fostering entrepreneurship and the development of start-up businesses. It discusses the significant impact of artificial intelligence on the growth of entrepreneurship and start-up businesses. Based on the results of interviews with experts and activists, the role of artificial intelligence can be categorized into two main themes: opportunities and challenges. Additionally, there are ten organizing themes that can promote entrepreneurship and business development. The study identified the convergence of technology with entrepreneurial goals, global customer orientation, job value creation, and the resilience of start-up businesses as key factors. It also highlighted five organizing themes, including the talent gap, privacy and security of entrepreneurs, continuous maintenance, and lack of integrated capabilities. Construction and fixed applications are categorized and limited. Finally, after analyzing qualitative data, a paradigm model illustrating the role of artificial intelligence in the development of entrepreneurship and start-up businesses was presented.

The world is advancing rapidly with new technologies, making it easy for organizations to make missteps. It is advisable to exercise caution when implementing artificial intelligence services. Adequate insight is necessary for the effective and efficient management of artificial intelligence systems. Today, organizations are grappling with evolving economic, technical, social, cultural, and political conditions. Their ability to survive in such competitive and complex environments depends on their agility and timely, appropriate response to these changes. In the realm of business and startups, artificial intelligence systems are designed to address these challenges and execute tasks effectively. However, their capabilities are limited. The final model of this research can be valuable for implementing and operationalizing artificial intelligence. This study minimized selection bias by employing two independent reviewers who were responsible for study selection and data extraction, and who demonstrated very high agreement in both processes. Considering the opportunities that this study presents, it will initiate an intriguing line of research through in-depth investigation, involving systematic or more targeted experimental studies.

This study aimed to develop IoT-based machine learning algorithms care and improvement while detecting and predicting real-time epidemics.The target disease is COVID-19 due to its importance and epidemic.The research method is based on design science. The research approach is forward-looking, so the mechanism of disease transmission and its effective characteristics enable us to make predictions about the disease and thus design disease control strategies and health care.The research was carried out in a seven-step process. IoT features were extracted in the present study with experts' opinions. The features obtained in the experiment of two different algorithms, "k nearest neighbor" and "decision tree," were created on the data to determine the best model.After selecting the best depth validation of the model were performed by confusion matrix analysis.The results of running k-nearest neighborhood and Decision Tree algorithms for the prediction of COVID-19 indicated an accuracy of > 98%. Higher sensitivity (99%) was obtained in the Decision Tree algorithm, which is very important diagnosing COVID-19 and indicates the minimum number of false negatives in the test results.