Management and Educational Perspective

Abstract The increasing expansion of artificial intelligence in educational systems has transformed the role of school principals from traditional management to technological and data-driven leadership. However, many artificial intelligence technologies are grounded in epistemological, cultural, and technological foundations that have been developed in non-indigenous contexts. Therefore, decolonizing the professional life of school principals, as an emerging approach, emphasizes strengthening professional autonomy, critical technological literacy, preserving cultural identity, and the conscious use of artificial intelligence.

The purpose of the present study was to explain the dimensions and components of decolonizing the professional life of school principals for working with artificial intelligence. This research was conducted using a qualitative approach and grounded theory methodology. The participants included experts in the fields of educational management, educational technology, and artificial intelligence, who were selected through purposive sampling. The sampling process continued until theoretical saturation was achieved, reaching 21 participants. Data were collected through semi-structured interviews and analyzed using open, axial, and selective coding.

Validation was conducted based on the four criteria proposed by Guba and Lincoln (1985), namely transferability, confirmability, dependability, and credibility. Overall, the findings indicated that the causal conditions of decolonizing the professional life of school principals for working with artificial intelligence consisted of five axial codes; strategic conditions included five axial codes; contextual conditions consisted of five axial codes; intervening conditions included five axial codes; and consequential conditions consisted of five axial codes.

Abstract The purpose of this research is to prioritize the antecedent factors influencing the role of artificial intelligence (AI) in human resource development. In terms of objective, the study is developmental; in terms of nature, it is descriptive; and regarding the data type, it is quantitative. The research population consists of 11 experts in human resource management, information technology, AI, and organizational management, selected through purposive sampling, with data collection continuing until theoretical saturation was reached. Data were gathered using a researcher-made Fuzzy Delphi questionnaire, developed based on a literature review and expert interviews. The Fuzzy Delphi method was employed for data analysis and was implemented in three rounds to identify, screen, and prioritize the antecedents. The identified antecedent factors include technical and infrastructural factors, human and organizational readiness, managerial and procedural dimensions, environmental conditions, and ethical and legal considerations. The findings revealed that “technological infrastructure and system integration” achieved the highest priority among the antecedents with a defuzzified value of 4.965, followed by “human and organizational readiness” (4.965) and “process organization and procedural factors” (4.114). Technological awareness, organizational culture, and data mining capability were also identified as influential antecedent dimensions. In contrast, “external conditions and environmental factors” received the lowest priority with a defuzzified value of 3.280.

Abstract The present study aims to develop a qualitative model of the interactions and outcomes of artificial intelligence-based human resource management (AI-HRM). In terms of purpose, the research is exploratory, and in terms of implementation, it adopts a qualitative approach. The study population consisted of 18 university scholars, managers, and human resource management experts who were selected through purposive sampling. Data were collected using semi-structured interviews and analyzed through thematic analysis. The findings led to the identification of 183 codes, categorized into 28 basic themes and 27 organizing themes. The results indicated that AI-based human resource management comprises four principal dimensions: interactive algorithms in the interview screening process, including the interactive enhancement of interviews, intelligent matching of applicants with organizational competencies, and interactive analysis of résumé data; dynamic performance appraisal, including interactive evaluation of work quality, interactive prediction of long-term performance, and automated continuous feedback; employee interaction with the learning and development system, including interactive career path recommendation, adaptive learning content, and soft-skills simulation; and interactive human resource planning systems, including workforce estimation, turnover intention prediction, and improvement of HR processes. These four dimensions directly affect three major outcomes: operational productivity, with emphasis on automating administrative decrees and reducing leave-processing time; improvement in HR decision-making, with emphasis on the unbiased standardization of interviews and monitoring of behavioral data; and human resource value creation, with emphasis on shared value creation and alignment with strategic objectives.

Abstract The aim of the present study is to explain the paradigmatic model of "development of mutual trust between humans and systems" in executive agencies. This research is applied in terms of purpose and qualitative in terms of methodology with a data-based theory approach. The statistical population included experts, senior managers of information technology, and public administration specialists, who were conducted using a purposive sampling method (snowball), 17 semi-structured interviews until theoretical saturation was achieved. The data analysis process was carried out using three-stage coding (open, axial, and selective) with maxquda software. The findings show that the pivotal phenomenon of "development of mutual trust" is the result of the interaction of causal conditions (governance and legal requirements, complexity of administrative tasks, need for computational transparency), contextual conditions (digital literacy and user expertise, fear of substitution, ambiguity in the legal accountability of the system), and intervening factors (complexity of algorithm logic and fear of substitution). In the strategy phase, continuous training and empowerment, participatory design of systems plays a key role in reducing organizational uncertainties. The results of this model indicate that the deployment of intelligent systems in the public sector, beyond technical aspects, requires a mental re-creation of the role of machines in decision-making processes. By providing a comprehensive framework, this research offers operational solutions for enhancing social capital and national productivity through the transition to data-driven governance.

Abstract The current research aims to validate the digital talent management model using artificial intelligence. The research methodology, considering its objective, is applicable; and in terms of execution, it is mixed (qualitative-quantitative). The statistical population of the research in the qualitative section includes 14 university professors in the field of public management and managers and experts of mining companies, selected by purposive sampling. The statistical population in the quantitative section includes active employees of mining companies, of which 215 individuals were considered based on Cochran’s formula, using a random sampling method. Data collection in the qualitative section was derived from semi-structured interviews based on theoretical foundations. The validity of the codes in the qualitative section was confirmed by two independent researchers, and its reliability was confirmed by the Cohen’s Kappa coefficient. In the quantitative section, a questionnaire was used. For data analysis, thematic analysis was used in the qualitative section, and SPSS and Lisrel software were used in the quantitative section. As a result of this process, the dimensions of the process functions (automatic screening and decision support tools), job matching (precise matching of jobs and skills, improvement of qualitative assessments), personality trait evaluation (analysis of psychological tests, identification of personality types), educational recommendations (personalized training, intelligent and dynamic training, triple synergy, and educational justice), and prediction of job turnover (job turnover analytical system and preventive diagnosis) were identified. The results of confirmatory factor analysis also indicate the validity of the final conceptual model of the research.
Introduction
Nowadays, with the advancement of technology, traditional talent management processes have become manual, time-consuming, and inefficient. Artificial intelligence (AI) has demonstrated its potential significantly across various sectors and has currently become a strategic tool for improving organizational efficiency (Abraham et al., 2025). With the rapid development of AI, big data, cloud computing, and other tools, traditional talent management methods can no longer meet the development needs of modern organizations. These tools may provide opportunities and challenges for reforming the human resource management system (Sithambaram et al., 2023). Traditional methods cannot objectively and reasonably collect in-depth data on organizational talent. These tools are incapable of accurately managing the analysis and processing of data resources such as the alignment status between talents and positions, and the matching status between talents and their superiors and subordinates. Talent selection, utilization, training and development, and retention, within digital talent pools using traditional methods, are not only unfavorable to the long-term development of organizations and talents but also, to some extent, reduce the enthusiasm of organizational talents (Saini & Tarkar, 2022). Conversely, many researchers have emphasized the transformative potential of AI in reshaping traditional approaches to talent acquisition, development, and retention, highlighting its benefits in improving recruitment processes, such as reducing bias in candidate selection and enhancing predictive analytics for identifying outstanding potential talents. Accordingly, AI simplifies administrative tasks, allowing HR professionals to focus on the strategic aspects of talent management. This is because talents are considered highly valuable resources for organizations to achieve long-term development (Weber et al., 2024). On the other hand, studies show that very few digital talent management models have been presented in this area. For example, the talent attraction and selection model proposed by Kathiravan et al. (2023) can be mentioned. This model optimizes the recruitment and selection process of employees using AI and machine learning algorithms. Talent management systems can automatically analyze resumes and identify the best candidates based on predefined criteria. This approach not only reduces recruitment time and costs but also increases selection accuracy. The AI-based talent development and training model proposed by Kaliannan et al. (2023) also aids in personalizing training and development processes. Given the aforementioned points, the main research question of the current study is: How is the digital talent management model based on artificial intelligence validated?
Theoretical Framework
Talent Management
Talent management is an integrated strategy or system designed to develop processes for recruiting, selecting, and developing individuals, and retaining those with the necessary skills, often examined with a long-term, comprehensive plan in mind (Almaaitah et al., 2020).
Artificial Intelligence
As a branch of computer science, artificial intelligence develops an intelligent machine product that, based on understanding the nature of intelligence, reacts in a manner similar to human intelligence. Research in this field primarily includes image recognition, speech recognition, language processing, expert systems, and robotics (Yanling Shi, 2020).
El Hajal et al. (2025) explored artificial intelligence and the future of talent management in tourism and hospitality. Using scenario planning, they outlined four alternative futures for AI-driven talent management, emphasizing the importance of ethics, continuous learning, and the complementary role of AI.
Kumar & Hariharasudhan (2025) investigated the role of artificial intelligence in recruitment and talent management. Through qualitative interviews, they found that AI can automate resume screening and interview scheduling stages, thereby reducing human biases.
Research Methodology
The research method, considering its objective, is applicable; and in terms of execution, it is a mixed-methods (qualitative-quantitative) approach. The statistical population of the research in the qualitative section includes 14 university professors in the field of public management and managers and experts in mining companies, selected using purposive sampling. The statistical population in the quantitative section includes active employees of mining companies, with 215 individuals determined based on Cochran’s formula, using random sampling. Data collection in the qualitative section was based on semi-structured interviews derived from theoretical foundations. The validity of the codes in the qualitative section was confirmed by two independent researchers, and its reliability was confirmed by Cohen’s Kappa coefficient. In the quantitative section, data was collected through questionnaires.
Research Findings
Thematic analysis was used to analyze the data in the qualitative section, while SPSS and Lisrel software were used for the quantitative section. As a result of this process, the identified dimensions include dimensions of process functions (automatic screening and decision support tool), job matching (precise job and skill matching, improvement of qualitative assessments), personality trait evaluation (analysis of psychological tests, identification of personality types), educational recommendations (personalized training, intelligent and dynamic training, triple synergy, and educational justice), and prediction of job turnover (job turnover analytical system and preventive detection). The results of the confirmatory factor analysis also indicate the validity of the final conceptual model of the research.
Conclusion
The present research aimed to validate the digital talent management model utilizing artificial intelligence. The results of this study align with findings from Kumar & Hariharasudhan (2025), Kambur & Akar (2022), and others such as Faqihi & Miah (2023), Yazdani & Ahmadi Qajari (2024), Heidary et al. (2023), and El Hajal et al. (2025). Faqihi & Miah (2023) demonstrated that AI-based talent management, employing machine learning and moderating technology theories, leads to better job matching and targeted training.
Based on the results obtained from this research, the following recommendations are proposed:
Other researchers are encouraged to investigate the relationship between the identified dimensions in the current study and other variables, such as attrition intention, digital emotional exhaustion, human capital management, and digital organizational commitment. To enhance the external reliability and generalizability of the proposed model, it is recommended that this model be applied in other private and public companies across various industries, with results presented comparatively. Given that the statistical sample of this study was purposively selected from employees of mining companies, caution and necessary considerations should be taken when generalizing the findings to other organizations, industries, or organizational cultures. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract The present study aimed to explore artificial intelligence and the redefinition of organizational leaders’ skills. This research was conducted through a qualitative approach and the meta‑synthesis method based on the seven‑step model of Sandelowski and Barroso (2007). The study population consisted of 360 articles published between 2021 and 2025, from which—after applying inclusion criteria—36 articles were selected for final analysis. Data analysis was performed by MAXQDA 2020 through open, axial, and selective coding. Using the data‑analysis software, 275 open codes were initially extracted. Next, axial coding was carried out in two stages, resulting in 25 codes in the first stage and 7 axial codes in the second stage. Ultimately, 3 selective codes were identified as the key leadership skills.
The findings indicated that leaders’ skills in the age of artificial intelligence fall into three broad categories: 1. Soft leadership skills, including personal skills, social skills, cognitive‑analytical skills, continuous learning, and personal development. 2. Hard skills, such as technical skills and managerial skills. 3. Multidimensional skills, such as creative and agile leadership.
Digital transformation and the growing penetration of artificial intelligence have reshaped the nature of organizational leadership. To remain effective, leaders can no longer rely solely on traditional skillsets; instead, they must cultivate a combination of soft, hard, and multidimensional skills in order to navigate the complexities of the AI era.
Introduction
In the field of leadership, artificial intelligence is defined as a tool that supports, empowers, and enhances leaders’ performance. These advanced computational systems analyze large volumes of data and provide valuable analytical insights that enable more informed, accurate, and forward‑looking decisions (Smith & Green, 2018). This creates a close interconnection between leadership and artificial intelligence that not only strengthens organizational efficiency but also reshapes the very definition of leadership skills. In this regard, Zaidi et al. (2025) emphasize that successful leaders in this dynamic environment must simultaneously develop technological intelligence, human understanding, and ethical orientation in order to remain effective.
Schmidt et al. (2023) likewise stress that despite the extensive research on leadership skills, technological transformations necessitate a serious re‑examination of leadership competencies, as management is no longer solely considered a human action but one that is understood within the context of data‑driven technologies. This point is also underscored by Zaidi et al. (2025), who argue that future research should focus on developing technology‑proficient leadership skills and competencies.
In the age of artificial intelligence, continuous learning and the recalibration of leaders’ skills have become fundamental imperatives. According to Abositta et al. (2024), to maintain competitive advantage in rapidly evolving technological environments, leaders must acquire advanced technological competencies and adaptive skills that complement AI‑driven functions. Furthermore, this era requires preserving a human‑centered approach by designing active reskilling programs that enable effective coexistence with artificial intelligence.
Given the accelerating shift of organizations toward intelligent structures and the clear gap between emerging skill requirements and existing leadership development programs, conducting this research is essential. Accordingly, the central question of this study is: What are the key leadership skills required in the age of artificial intelligence?
Theoretical Foundations of the Study
Artificial Intelligence and Leadership
Artificial intelligence is a branch of computer science that aims to develop systems capable of learning, decision‑making, and problem‑solving in ways similar to humans. The introduction of this technology into workplaces has transformed the concept of leadership. In the age of artificial intelligence, leadership entails guiding and making decisions in contexts where humans and AI systems operate synergistically. Rather than being a mere decision‑maker, the leader assumes the role of a facilitator who orchestrates the alignment between human wisdom (knowledge, intuition, ethics) and algorithmic capabilities (data mining, prediction, machine learning).
The key applications of artificial intelligence in leadership include: Automation and productivity: Performing repetitive and complex tasks intelligently to reduce cost and time. Enhancing decision‑making and leadership processes: Analyzing large‑scale data to provide precise insights and support data‑driven decisions. Strengthening human–machine collaboration: Empowering leaders through real‑time data‑driven insights and effective interaction with intelligent systems. Transforming human resource management: Utilizing big data analytics to better identify employee potential and make more scientific decisions in HR domains.
In their study titled Competencies and Skills of Leaders in the Age of Artificial Intelligence, Myszak and Dawidowicz (2025), through a comprehensive review of 17 related articles, highlight a broad set of essential skills for organizational leaders. These include critical thinking, communication skills, strategic thinking and vision, innovation and creativity, emotional intelligence, change management, ethical leadership, adaptive agility, risk awareness, and resilience in interacting with AI technologies.
Hoang (2025), through in‑depth interviews with 17 teachers in Vietnam, conducted a study titled Developing Leadership Skills for Guiding Organizations in the Digital Age. This study introduces a five‑dimensional framework of essential leadership skills, which includes: mastery of fundamental technical skills; the application of educational innovation to ensure continuous workforce development; prioritization of cross‑border digital collaboration; agile change management; and the establishment of transparent digital communication to strengthen technological literacy and organizational adaptability.
Research Methodology
The present study employed a qualitative approach using the meta‑synthesis method and was conducted based on the Sandelowski and Barroso (2007) framework. The research process began with formulating the central question, which focused on examining the impact of artificial intelligence on redefining leadership skills within organizations. A systematic review of domestic and international literature published between 2021 and 2025 was then carried out through databases such as ScienceDirect, Scopus, SAGE, Normgaz, and MagIran. From an initial set of approximately 350 articles, several stages of screening—title evaluation, abstract review, and full‑text assessment—were applied, resulting in the selection of 36 articles (including 4 domestic and 32 international sources) for detailed analysis. Relevant statements addressing the research question were carefully extracted and coded through open, axial, and selective coding. To ensure the reliability and accuracy of the findings, data triangulation—enhancing the diversity of information across time, place, and participants—along with researcher auditing was utilized.
Research Findings
Data analysis was conducted through a coding process by MAXQDA 2020 software. In the open coding stage, 275 initial codes were extracted. Subsequently, through two stages of integration, 25 axial codes were formed in the first stage and 7 axial codes in the second stage. Ultimately, three final selective codes emerged as the main framework for redefining organizational leadership skills in the age of artificial intelligence.
The first category, leaders’ soft skills in the age of artificial intelligence, refers to the personal, interpersonal, and social dimensions of leadership. This category includes several components. Social skills encompass network leadership and hybrid interactions, coaching, empowerment, trust‑building, influence and stakeholder alignment, as well as ethical leadership grounded in empathy and humility. Cognitive–analytical skills involve data‑driven decision‑making, systems thinking and holistic organizational analysis, intelligent evaluation and feedback, critical thinking, and complex problem solving. Continuous learning and personal development skills include strategic and lifelong learning, personalization of skill‑development pathways, and the transformation of learning into practice. Individual skills involve trust‑ and ethics‑based leadership, high emotional intelligence, advanced interpersonal interactions, self‑awareness, self‑regulation, and resilience.
The second category, leaders’ hard skills in the age of artificial intelligence, relates to leaders’ professional knowledge and technical capabilities. These skills are grouped into two main domains. Resource management skills include managing organizational transformation and dynamics, developing human capital, human resource management, and creating a positive work environment. Technical skills and AI literacy include developing a strategic understanding of artificial intelligence, integrating and applying AI technologies in practice, and the capability to interact effectively with these technologies.
The third category, multidimensional leadership skills in the age of artificial intelligence, represents a combination of soft and hard skills and emphasizes adaptability, innovation, and future‑oriented thinking. A key example of this category is creative and agile leadership, which encompasses organizational creativity and innovation, technological agility and adaptability, and forward‑looking, future‑oriented thinking.
Conclusion
This study was conducted with the aim of examining artificial intelligence and the redefinition of leadership skills within organizations. The core conclusion is that the emergence of artificial intelligence has fundamentally transformed traditional conceptions of leadership. As a result, leaders in the new era must intentionally develop a broader and more contemporary set of competencies. These competencies fall into three major categories, each of which is discussed below.
The first category, soft skills, represents the primary priority of modern leadership. Leaders in the age of artificial intelligence must master capabilities such as emotional intelligence, empathy, trust‑building, self‑awareness, resilience, critical thinking, and complex problem solving. These skills enable leaders to harmonize human strengths with technological systems, foster learning‑oriented cultures, and elevate decision‑making from intuition‑driven judgments to data‑informed analysis. These findings align with the research of Myszak & Dawidowicz (2025), Zaidi et al. (2025), and Thomas et al. (2024).
The second category, hard skills, has become an unavoidable necessity. Leaders must view artificial intelligence not merely as a tool, but as a strategic partner in decision‑making and process design. Consequently, proficiency in AI literacy, digital transformation management, and technology‑aligned human capital development has evolved into a set of essential leadership competencies. These results are consistent with the findings of Frimpong & Wolfs (2024), Sriharan et al. (2024), and Hoang (2025).
The third category, multidimensional skills, reflects the new profile of future leadership. This category represents an integration of soft and hard skills, equipping leaders to navigate complex and dynamic environments. Agile and creative leaders—those capable of combining technological foresight with human‑centered thinking—are more likely to enhance organizational innovation and prepare their organizations for the future. This future‑oriented orientation is grounded in strategic agility and high adaptability. These findings are supported by the studies of Bevilacqua et al. (2025) and Sriharan et al. (2024).
Based on the study’s findings, the following practical recommendations are proposed:
• Developing organizational policies to enhance AI literacy and technological skills, including facilitating leaders’ and employees’ access to AI‑related training and technological skill development opportunities.
• Utilizing modern educational technologies, such as virtual reality and augmented reality, to create engaging and interactive learning experiences aimed at strengthening leadership competencies. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract The main objective of the present study was to measure the effect of the use of artificial intelligence and smart learning on strategic thinking skills and academic performance of management students with the moderating role of individual ethics. The paradigm of the present study was positivism, its approach was quantitative, and its method was descriptive-survey. The statistical population of management students at Tehran University of Research Sciences was 10,000 students, of which 374 students were selected as a sample by cluster random method. The results obtained show that the use of artificial intelligence tools has a positive effect on strategic thinking (systemic thinking, creative thinking, forward-looking thinking, and critical thinking), smart learning, and academic performance. The mediating role of strategic thinking (critical thinking and forward-looking thinking) was confirmed in the relationship between the use of artificial intelligence and academic performance, as well as in the relationship between smart learning and academic performance. On the other hand, personal ethics, in addition to having a positive effect on smart learning, also has a moderating role in the relationship between smart learning and strategic thinking (systems thinking, critical thinking, and forward-looking thinking). The present study provides insights into the use of artificial intelligence tools in developing strategic thinking skills and performance for the first time, which researchers, managers, students, and organizations can benefit from.
Introduction
Strategic thinking skills are now considered as a key cognitive skill of managers in dealing with environmental uncertainties and succeeding in designing effective strategies (Goldman & Casey, 2010; Bonn, 2001). On the other hand, the field of education in recent years has focused on developing essential competencies and practical skills of students (Vázquez-Parra et al., 2024). Students who use artificial intelligence tools have a higher understanding of complex problems (Huang et al., 2022; Lee & Yeo, 2022). Artificial intelligence can improve academic performance through self-directed learning experiences, simplified operations, and targeted support mechanisms (Yu, 2023). In parallel with academic performance; smart learning, as a new approach to the education process, emphasizes active, self-directed, and deep learning using technological tools (Shahzad et al., 2024). The integration of artificial intelligence tools in educational environments plays an important role in transforming the landscape of learning and education (Seifi, E et al., 2024; Pourshahabi, 2023). On the other hand, with the widespread use of artificial intelligence applications in the field of education and research, one of the main concerns is the development of ethical frameworks and ethics-based educational strategies (Ayanwale et al., 2024). A review of previous studies shows that there is a gap in the existence of a study that empirically evaluates the effectiveness of the use of artificial intelligence tools in developing students' strategic thinking skills, taking into account ethical factors. Therefore, based on the gap in studies in the field of: 1. The effect of the use of artificial intelligence and smart learning on the development of strategic thinking skills; 2. The effect of strategic thinking on the development of performance of management students; and 3. The role of personal ethics in the use of artificial intelligence, the main objective of the present study is to determine the effect of the use of artificial intelligence tools and smart learning on strategic thinking and performance of management students with the moderating role of personal ethics.
Theoretical framework
Application of artificial intelligence tools
The use of artificial intelligence tools expresses how students use artificial intelligence-based tools for learning activities, innovation, creativity and experience improvement (Dahri et al., 2024). Improving decision-making and reducing costs are some of the beneficial results of using artificial intelligence tools for managing organizations and businesses. Information quality is also significant in problem solving and satisfaction, attracting students' trust and engaging them with artificial intelligence tools (Almulla, 2024; Dahri et al., 2024). Artificial intelligence as an emerging technology can play an important role in improving students' cognitive skills. Studies show that AI can help enhance critical thinking, problem-solving, and learning in students (Vázquez-Parra et al., 2024).
Smart Learning
Smart learning, as an educational paradigm, leverages advanced technologies such as AI and social media to enhance students’ learning experiences and improve learning outcomes (Shahzad et al., 2024). Learning involves acquiring new knowledge or skills, while thinking involves applying that knowledge to make judgments, decisions, or solve problems (Bonn, 2005).
Strategic Thinking
The focus of strategic thinking in this article is on creative thinking, forward-looking thinking, systems thinking, and critical thinking. Creative thinking is the ability to develop new and distinctive concepts by considering other perspectives, challenging standard approaches, and anticipating the potential value of an idea from the outset. This involves imagining new possibilities, seeking alternatives, and breaking free from limiting assumptions and beliefs. Forward-looking thinking is a cognitive skill that enables individuals to have a clear sense of purpose and direction. They have the ability to envision a distinct future, even in the face of uncertainty and limited information. These individuals have the ability to see the big picture, visualize the future, and chart a clear path toward a desired outcome (Geier, 2024). Systemic thinking takes a holistic approach to analyzing how the components of a system relate to each other, as well as how systems function over time (Vázquez-Parra et al., 2024). Systemic thinking is an important dimension of strategic thinking for business management and vision (Pisapia et al., 2011; Vázquez-Parra et al., 2024). Critical thinking is the ability to judge what should be done or the ability to believe along with thinking (Facione, 2000).
Academic performance
The use of AI tools, especially those that generate content, such as ChatGPT, has a positive impact on academic performance (Almulla, 2024; Dahri et al., 2024; Shahzad et al., 2024). These tools offer self-directed learning experiences, adaptive assessments, and intelligent instructional systems that cater to the individual needs of students and create a more engaging learning environment.
Personal Ethics
The role of ethically minded students in improving the use of artificial intelligence and reducing its negative consequences is very important, personal ethics can guide students towards the ethical and responsible use of artificial intelligence in education (Elbaz et al., 2024).
Research Methodology
The paradigm of the present study is positivism; the approach is quantitative; and the research strategy is descriptive-survey. Also, the research tools were library studies (extensive search in reliable databases) and questionnaires. The statistical population was 10,000 students of the management department of Tehran University of Science and Research, of which 374 were selected by cluster random sampling. The collected data were first analyzed by SPSS software (IBM SPSS Statistics for Windows, 2019). In the inferential statistics section, the hypotheses were tested in the form of a structural and measurement model using SmartPLS version 4 software (Ringle, C. M., Wende, S., and Becker, J.-M., 2024).
Research findings
The results of this study showed that the use of artificial intelligence tools has a positive effect on four dimensions of strategic thinking (systemic thinking, critical thinking, creative thinking, and forward-looking thinking). Also, the two dimensions of critical thinking and forward-looking thinking play a mediating role in the relationship between the use of artificial intelligence tools and academic performance and the relationship between smart learning and academic performance. Smart learning has a significant positive effect on the four dimensions of strategic thinking and the most effect on the dimensions of systemic thinking and critical thinking. Also, smart learning has a mediating role in the relationship between the use of artificial intelligence tools and the four dimensions of strategic thinking. Strategic thinking (except for the dimension of creative thinking) has a positive effect on the academic performance of management students. Among other results that were obtained for the first time in this study was the role of personal ethics, which has a moderating role in the relationship between intelligent learning and strategic thinking (except for the creative thinking dimension) and also has a positive effect on intelligent learning.
Conclusion
This study showed that the use of artificial intelligence tools increases the academic performance of management students, and smart learning also has a mediating role. These findings are consistent with the study of Shahzad et al., (2024) that measured the impact of artificial intelligence and social networks on students' academic performance. Artificial intelligence tools can have a significant impact on their academic performance by creating an interactive learning experience for students (Pang, 2024). Smart learning also gives students access to educational resources, interactive platforms, and a personalized feedback loop, and as a result, strengthens their self-directed learning (Samaha & Hawi, 2016). The positive impact of the use of artificial intelligence tools on strategic thinking was also confirmed. In this regard, previous research showed that artificial intelligence training at high educational levels leads to the development of critical thinking, computational skills, and problem solving (Heras et al., 2023). In this study, it was shown that smart learning has a positive effect on strategic thinking, and the role of smart learning as a mediator in the relationship between the use of artificial intelligence tools and the dimensions of strategic thinking was also confirmed. Therefore, it can be said that students who are placed in a smart learning environment and seek to use artificial intelligence tools acquire more strategic thinking skills. In this regard, previous research has shown that self-directed learning has a positive effect on computational thinking, which is the core of artificial intelligence and is useful for students to better understand artificial intelligence (Zhong et al., 2024). The effect of individual ethics on smart learning was also confirmed, which is consistent with the study of Shahzad et al., (2025) that examined the effect of justice and ethics of artificial intelligence on learning performance. The results of this study showed that information quality has a positive impact on the use of AI tools, which is consistent with previous studies (Almulla, 2024; Dahri et al., 2024), which emphasize the need for accurate, reliable, and relevant information provided by AI tools.
Based on the results of the present study, the following practical suggestions are presented:
- Special attention to the development of smart learning environments to engage students with this type of learning in order to improve student performance.
- Intelligent use of AI tools in the classroom of students, management and preparation of the hard and soft infrastructures required for the development of the use of these tools.
- Training students in obtaining correct and appropriate information and the correct and appropriate use of AI tools.
- Development of ethical frameworks for the use of AI in the educational system in parallel with the widespread use of these tools. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract The present study was conducted with the aim of designing a model for applying artificial intelligence in elementary school students' learning. This study was applicable in terms of its purpose, and mixed and exploratory in terms of the type of data; in such a way that the qualitative part was conducted using the paradigmatic grounded theory method and the quantitative part was conducted using a survey method. Participants in the qualitative part included academic experts and scholars from the Ministry of Education selected through conscious sampling and the snowball method; and in the quantitative part, 319 education managers and elementary school principals in Tehran were determined based on the Cochran formula. Data collection in both parts was conducted using the field method; semi-structured interviews were used in the qualitative part and a researcher-made questionnaire was used in the quantitative part. The validity and reliability of the instruments were confirmed in both parts. Qualitative data were analyzed by theoretical coding, and quantitative data by confirmatory factor analysis. The findings showed that the AI ​​application model consists of 19 components and 106 indicators in the form of six dimensions of the paradigm model. In this model, the central phenomenon includes learning enrichment and learning individualization; causal factors include technical infrastructure, intelligent support, and technology growth in the family; contextual factors include learning management, learning content, and teacher capabilities; intervening factors include ethics, information security, and educational technology; strategies include personalization of education, feedback, intelligent evaluation, content quality, and learning assistant; and outcomes include deep learning, improving teacher efficiency, high-quality evaluation, and educational innovation. Finally, the obtained model was quantitatively evaluated with confirmatory factor analysis, and the fit indices showed that the model was confirmed.
Introduction
The rapid advancement of computing and information processing methods has accelerated the expansion of AI applications, with the aim of enabling computers to perform their tasks by simulating intelligent human behaviors such as inference, analysis, and decision-making (Duan, Edwards & Dwivedi et al., 2019).
AI in education has great potential to enhance learning, teaching, and assessment by suggesting or providing personalized or tailored learning to learners, developing teachers’ understanding of the learning process, and providing anywhere-anytime search engines and immediate feedback (Xia et al., 2022). AI in learning helps learners actively construct their own knowledge by exploring and manipulating elements of the learning environment (Randhawa et al., 2020).
AI helps to increase the quality of education and not only assists in the learning process but also provides facilities such as tutoring, grading, lesson planning, and feedback to students (Mondal et al., 2019). Timms stated that collaborative robots are used in collaboration with teachers to teach everyday tasks such as spelling, pronunciation, and adjusting students’ abilities (Timms et al., 2016). Artificial intelligence helps personalize content to meet the demand for education, so it plays a significant role in the organization of course materials (Mondal et al., 2019). Therefore, it can be said that it is one of the most important phenomena in education that will be used more in the future (Rezaei et al., 2024). According to the US Department of Education, it is estimated that artificial intelligence in education in this country will increase by 47.5% from 2017 to 2021, which is expected to see a significant growth in companies developing artificial intelligence in education (Liang et al., 2020). During the emergence of Corona, educational technologies, especially artificial intelligence, were used in many countries, but in Iran, very little of artificial intelligence has entered the field of education. Despite the fact that the use of modern educational and training equipment and technologies in line with the goals of education has been considered in the document on the fundamental transformation of education, artificial intelligence has been taken for granted; and only education and training, teachers, and students use virtual spaces as modern educational technologies; while artificial intelligence has spread around the world and provides significant help to teachers and students. Accordingly, the researcher in this study seeks to answer the question: What is the model for using artificial intelligence in learning for elementary school students in Tehran province?
Theoretical foundations
Infrastructure and technological background
Educational developments based on artificial intelligence require appropriate technical infrastructure and access to modern technologies for families. Infrastructure includes servers and network storage, educational software and applications, appropriate bandwidth, multimedia tools, and information processing equipment that enable smart education and active student interaction (Li et al., 2020). In addition, parental mental preparedness, family economic and cultural status, and the use of artificial intelligence tools in daily life are effective contexts for the acceptance and use of smart systems (Chen et al., 2020).
Smart support enables access to content at any time and place, personalized services, and active student interaction with content, complementing the teacher's role in the education process, which is in line with the active learning theory (Rezavan et al., 2022).
Learning Management and Educational Content
Learning management involves identifying student needs, continuously monitoring the learning process, providing feedback and continuous evaluation; and improves the quality of education (Tsai et al., 2019). Learning content should be in line with educational objectives, utilize interactive tools such as games and videos, and enhance students’ academic achievement. Teachers’ ability to use artificial intelligence for teaching and content production has a direct impact on the effectiveness of education (Zhu et al., 2020).
Content personalization strategies, providing immediate feedback, and intelligent assessment facilitate individual and active learning and take into account individual student differences (Popenici & Kerr, 2017).
Intervenors and Ethics in Smart Education
The use of smart technologies in education is associated with ethical and security considerations. Respecting privacy, trustworthiness in the use of content, and developing security infrastructures are critical components (Livingstone, 2012). Educational technology, including artificial intelligence tools, media, and virtual reality technology, acts as an intermediary between the student and the learning process and enables continuous and personalized learning through timely intervention and resource management.
Implications of Smart Learning
The use of artificial intelligence leads to deep learning, improved teacher efficiency, accurate evaluation, and educational innovation. Deep learning is possible by strengthening cognitive skills, increasing the ability to analyze and solve problems, active student participation, and repeating material until complete learning. By continuously monitoring student progress and utilizing smart evaluation, teachers can provide timely corrections and feedback, and improve learning outcomes by combining face-to-face and virtual methods and producing multimedia content (Li et al., 2020).
Sun (2024) examined various uses of artificial intelligence in education in a study titled "Applications of Artificial Intelligence in Education." The research method of this study was a review and analysis. The results showed that AI can improve personalized learning, adaptive learning, virtual reality technology, and teaching assessment, and change the traditional learning system by providing new teaching methods and assessment systems.
Alam et al. (2024) analyzed the future role of AI in education in a study titled "Future Applications and Prospects of AI in Education". The research method of this study was analytical and descriptive. The results showed that AI, with active participation in data analysis, virtual classrooms, adaptive assessments, and personalized learning, can create more effective and efficient educational experiences and requires the participation of educators and legislators to fully realize this potential.
Research Methodology
This research is applicable in terms of purpose, and mixed exploratory (qualitative and quantitative) in terms of data. The qualitative stage was conducted based on systematic data, and the statistical population included 19 academic experts, educational technology specialists, and education managers, of which 16 interviews were used as the basis for the analysis. The quantitative phase was conducted as a cross-sectional survey with a statistical population of 1880 school principals and educational districts in Tehran, and 319 people were selected by stratified random sampling. Qualitative data were collected through semi-structured interviews and library studies, and quantitative data were collected with a researcher-made questionnaire consisting of 19 components and 106 indicators. The validity and reliability of the tools were confirmed by test-retest methods, model reliability test, and inter-coder test, respectively, at 80–86%
Research findings
The research findings showed that the application of artificial intelligence in elementary school students' learning has several basic dimensions that interact with each other in a systematic way. On one hand, technical infrastructure and intelligent support enable active, personalized, and continuous learning, and on the other hand, teacher empowerment and content management ensure that the education process proceeds with quality and effectiveness. Ethical considerations and information security, along with educational technologies, as intervening factors, ensure the safe and sustainable use of systems. Content personalization strategies, feedback, smart assessment, and learning assistants enhance students' individual and collaborative learning, leading to outcomes such as deep learning, educational innovation, improved teacher effectiveness, and quality assessment.
Discussion and Conclusion
The research findings show that implementing smart learning in Tehran elementary schools requires appropriate technical infrastructure and families’ access to modern technologies. Servers, bandwidth, software, and multimedia tools enable smart learning and active student interaction with content (Chen et al., 2020; Rezavan et al., 2022). The readiness and support of teachers and families, especially a positive attitude towards educational technologies, play a key role in the success of smart learning (Valeri et al., 2024; Alam et al., 2024).
In learning and educational content management, identifying student needs, providing continuous feedback, and regular evaluation improve the quality of education (Tsai et al., 2019). The use of interactive tools and multimedia content enhances students’ active participation and academic achievement (Zhu et al., 2020; Sun, 2024). Teachers’ ability to utilize AI for teaching and content production also has a direct impact on learning effectiveness (Chen et al., 2022).
Ethical and security considerations, including protecting students’ privacy and developing security infrastructure, are essential for the successful implementation of smart technologies (Wang et al., 2024; Omar et al., 2024). Educational technologies, through media, content production tools, and virtual reality, enable interactive and personalized learning and reduce teachers’ workload (Sun, 2024).
The implications of using AI in learning include enrichment, individualization, and active learning. Providing diverse examples, simulating real-world situations, and purposeful exercises facilitates deep and active learning (Valeri et al., 2024; Sun, 2024). Content personalization strategies, immediate feedback, and intelligent assessment enable effective learning that is responsive to individual differences (Chen et al., 2022; Zhu et al., 2020).
Finally, AI can help improve the quality of learning, increase student engagement, improve teacher efficiency, and innovate in education (Alam et al., 2024; Wang et al., 2024). This technology enables a more effective, efficient, and personalized learning experience for students. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract The aim of this study is to evaluate the effectiveness of artificial intelligence in developing students’ entrepreneurial skills. The present study is applicable in terms of its purpose, descriptive-survey in terms of data collection, and correlational in terms of the relationships between variables. The statistical population of the present study includes 110 teachers of secondary schools in Districts 11 and 12 of Tehran, selected by purposive sampling method. The collection tool in the present study includes a questionnaire which was distributed among them. The validity of the questionnaire was confirmed by experts and its reliability was confirmed by Cronbach’s alpha coefficient. SPSS and PLS software were used to analyze the findings and test the hypotheses. The results showed that artificial intelligence has an impact on the development of entrepreneurial skills such as personal skills, creative and critical skills, interpersonal skills and practical skills. Also, the GOF criterion method was used to fit the overall model; the obtained value was 0.723, which indicates a strong fit of the model.
Extended abstract
Introduction
In the era of rapid technological and digital changes, the development of entrepreneurial skills among students has become a fundamental necessity for educational systems. Entrepreneurship education for the new generation is not only a key factor in economic growth and sustainable job creation, but also a prerequisite for adapting to technological changes, innovation, and empowering young people to successfully enter the competitive environment of the future labor market (Somia, & Vecchiarini, 2024). Meanwhile, the emergence of artificial intelligence (AI) with unique capabilities in complex data processing, decision-making, personalization of learning processes, and creation of interactive content has opened new horizons for the transformation of entrepreneurship education (Wan, 2025).
The complexity of entrepreneurship education, with its interdisciplinary nature and need for practical experience, requires new approaches to personalize education, provide interactive scenarios, and simulate real business environments; capabilities that artificial intelligence can potentially provide (Bell & Bell, 2023). However, there is a lack of credible evidence and empirical studies, especially in developing countries and school settings, on the actual effectiveness of this technology in entrepreneurship education. Many existing studies have either focused on the technological dimension or have lacked the measurement of operational and behavioral effectiveness on students. This research gap has doubled the need for scientific studies and field evaluations (Bhatt et al., 2024). Focusing on "Evaluating the Effectiveness of Artificial Intelligence in Developing Students' Entrepreneurial Skills", this research attempts to answer basic questions: To what extent and in what dimensions do AI-based technologies improve entrepreneurial capabilities (including cognitive capabilities, practical skills, and personality-behavioral characteristics) among students? Which components and contextual conditions (such as school environment, digital literacy level, gender, etc.) play a key role in realizing or failing to realize the effectiveness of this process? Can AI fill the gaps in traditional entrepreneurship education and lead to strengthening students' willingness, courage, and readiness to enter the field of entrepreneurship?
Theoretical Framework
Students' Entrepreneurial Skills
Entrepreneurship skills are a multidimensional set of capabilities, attitudes, and knowledge that enable an individual to identify opportunities, create value, and manage resources in various economic, social, and technological contexts. These skills go beyond technical capabilities to include psychological traits (such as risk-taking, independence, perseverance, and self-confidence), cognitive abilities (such as creative thinking, problem-solving, gathering, analyzing, and evaluating information), and social skills (such as effective communication, teamwork, negotiation, and networking). Recent research shows that establishing and nurturing these skills from an early age, especially in educational settings, plays a decisive role in enhancing the career prospects and enabling young people to adapt to changes in the labor market (Ngwenya, 2024).
The Role of Artificial Intelligence in Improving Entrepreneurial Skills
In the last decade, Artificial Intelligence (AI) has become an unparalleled tool for reinventing the way entrepreneurship skills are taught and developed, with researchers calling it a “leapfrogging factor in the entrepreneurial empowerment of the next generation.” Integrating smart technologies into the process of educating and training entrepreneurs not only personalizes and accelerates learning, but also has a profound and constructive presence at the four main levels of entrepreneurial competencies (personal characteristics, interpersonal skills, creative/critical skills, and executive/practical skills) (Masadeh et al., 2024).
Najmabadi et al., (2025) examined the impact of artificial intelligence on the development of life skills in students. The present study, using a review approach and qualitative content analysis, examined studies published between 2022 and 2024 on the impact of artificial intelligence on teaching life skills in students. The findings of this study show that AI technology can facilitate the development of cognitive, emotional, and social skills in learners by providing smart learning environments, content personalization, enabling rapid feedback, and enhancing teacher-student interaction. On the other hand, challenges such as the lack of appropriate infrastructure in many schools, inequality in access to technology, weak technical knowledge of teachers, ethical concerns in collecting and analyzing personal data, and lack of localized educational resources are considered obstacles to the effective use of this capacity. The prospective analysis of this article indicates that AI, if properly managed, developing ethical frameworks, and empowering human resources, can play a role as a powerful tool in improving the quality of skill-based education.
Azizi (2025) studied the impact of using AI systems on learners' learning efficiency. The findings of this study show that blockchain technology, visualizations, intelligent teaching systems, adaptation and personalization systems have a positive and significant effect on the efficiency of English language learning, while the effect of assessment and evaluation and teacher robots on improving the efficiency of language learning has been rejected, and it seems that from the perspective of educators and instructors, these systems cannot directly replace the entire teaching staff.
Research Methodology
The present study is applicable in terms of its purpose, descriptive-survey in terms of data collection, and correlational in terms of the relationships between variables. The statistical population of the present study includes 110 teachers from secondary schools in Districts 11 and 12 of Tehran, selected by purposive sampling method and a questionnaire was distributed among them. The collection tool in the present study includes a standard questionnaire, the validity of which was confirmed by experts and its reliability was confirmed by Cronbach's alpha coefficient.

Research findings
SPSS and PLS software were used to analyze the findings and test the hypotheses. The results showed that artificial intelligence has an impact on the development of entrepreneurial skills such as personal skills, creative and critical skills, interpersonal skills, and practical skills. Also, the GOF criterion method was used to fit the overall model; the value obtained was 0.723, which indicates a strong fit of the model.
Conclusion
The present study was conducted with the aim of evaluating the effectiveness of artificial intelligence in developing students' entrepreneurial skills. The results of this study are consistent with the results of Najmabadi et al., (2025), Mehdizadeh Mahali (2025), Sadeghi et al., (2025), Li (2025), Wan (2025), Azizi (2025), Rostam zadeh ganji & jayervandi (2025), and Pourshahabi (2023). Najmabadi et al., (2025) showed that AI technology can pave the way for the development of cognitive, emotional, and social skills in learners by providing smart learning environments, content personalization, enabling rapid feedback, and enhancing teacher-student interaction. On the other hand, challenges such as the lack of appropriate infrastructure in many schools, inequality in access to technology, weak technical knowledge of teachers, ethical concerns in collecting and analyzing personal data, and lack of localized educational resources are considered obstacles to the effective use of this capacity. The prospective analysis of this article indicates that AI, if properly managed, developing ethical frameworks, and empowering human resources, can play a role as a powerful tool in improving the quality of skill-based education.
According to the results obtained, it is suggested that entrepreneurship education programs be designed based on blended learning and using simulated environments based on AI. These environments should represent real issues and challenges of the Iranian labor market and innovative businesses in a localized manner so that students, while interacting with realistic scenarios, practice problem solving, decision making, and analyzing real situations. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract Abstract This research was done with the aim of providing a systematic model of employee training using artificial intelligence. The research method is a combination of qualitative and quantitative methods. The statistical community in the qualitative section includes an unlimited number of experts familiar with the subject, and the statistical sample in this section is 20 people selected by the snowball method. The statistical population in the quantitative section includes all the specialists and experts related to the research subject in an unlimited number, of which 384 people were selected as a sample by a simple random method. Researcher-made questionnaires with confirmed validity and reliability were used to collect the data of this research. Data analysis in this research was done at two levels of descriptive statistics and inferential statistics. To complete the Delphi process in this research, Kendall's coefficient was used with the help of SPSS software. In order to test the research model, structural equation technique was used through Smart-PLS statistical software, and the results show the appropriate fit of the model. The findings of the research show that the inputs of the model include 1- educational data, 2- personal information, 3- educational needs, 4- user feedback, and 5- data of the work environment. The model process also includes 1- determining the needs and goals, 2- collecting data, 3- pre-processing the data, 4- training the artificial intelligence model, 5- evaluating and improving the model, 6- implementing and deploying, and 7- monitoring and updating. Finally, the outputs of the model include 1- individual feedback, 2- educational suggestions, 3- monitoring and follow-up, and 4- support and guidance. Extended abstract Introduction In 1950, Alan Turing, an English mathematician, wrote an article entitled "Computing Machines and Intelligence" and posed the question: "Can machines think?" This led to further exploration of the use of machines to support human decision-making, with the formation of a workshop in 1956. The workshop was organized by John McCarthy, an American mathematician, who focused on the "study of artificial intelligence" (Frehywot, 2023). As science and technology advance, since 2013, when Frye and Osborne estimated that nearly half of US jobs are at risk of high automation, AI has been at the top of policymakers' agendas, and now the consensus is that AI creates fundamental changes in the labor market. With the use of artificial intelligence, many skills that were important in the past become automated; many jobs are also obsolete or transformed; and artificial intelligence is increasingly used (Tuomi, 2018). Artificial intelligence includes various related technologies, often supported by machine learning algorithms, whereby it achieves set goals through supervision (human-guided) or unsupervised (autonomous machine) (Walsh et al., 2019). Today, most experts believe that the implementation of smart technology will dynamically transform work environments. There are applications of artificial intelligence in all industries and professions, and human resource management is no exception to this rule. Therefore, for the organization to remain relevant and maintain its competitive advantage, it is necessary to adopt new technological developments (Kaushal et al., 2023). Today, the surprising speed of innovation in business processes and technology requires that the employees of organizations continuously acquire new skills and be able to adapt to changing practices. Thus, educational needs become more personalized (Ford et al., 2017). Employee skill development was once done entirely by on-the-job supervisors; but now with the increasing demand for new skills, technological advances have enabled training and development on mobile platforms, such as smartphones and laptops (Maity, 2019). Artificial intelligence should be applied to organizational learning to help companies solve their training challenges. When recruiting and hiring new employees, the main challenge is to quickly and effectively make them fully aware of and understand the organization's internal policies and procedures. Machine learning features have been incorporated into various HR software systems to facilitate greater efficiency (Iqbal, 2018). Even more comprehensively, the use of various artificial intelligence technologies can help companies develop a learning organizational culture and avoid the common training design model based on traditional competency model analysis (Chen, 2023). According to the above, considering the development of digital technology, especially artificial intelligence, and the increasing demand for personalized training, the past training methods are no longer able to meet personal needs, and the adoption of artificial intelligence-based training can effectively fill the shortage of personalized training. Therefore, the main question that the researcher seeks to answer in this research is: "What is the system model of employee training using artificial intelligence?" Theoretical foundations Artificial intelligence is often defined as a computer system with the ability to perform tasks normally associated with intelligent beings. The first explicit definition of artificial intelligence came in a funding proposal to the Rockefeller Foundation in 1955. This definition states that "any aspect of learning or any other characteristic of intelligence can originally be described so sufficiently precise that a machine can be built to simulate it." This initial definition quickly led to deep discussions. In practice, the early developers of artificial intelligence interpreted intelligence and thinking as the mechanical processing of logical statements (Tuomi, 2018). In another definition, artificial intelligence is defined as "making a machine behave in a way that would be called intelligent if a human behaved." Although artificial intelligence was defined in 1955, it has recently gained worldwide recognition due to the technological revolution. Artificial intelligence is discussed as non-human intelligence designed to perform specific activities and tasks (Kaushal et al., 2023). McCarthy describes artificial intelligence as the science and engineering of building intelligent machines through algorithms or sets of rules, that the machine follows to mimic human cognitive functions, such as learning and problem-solving (Frehywot, 2023). This definition provides a combination of AI capabilities and what it is. Human resource management has undergone an early revolution with the help of artificial intelligence, which has gradually affected human resource operations. These functions, which were already performed entirely by humans, are being recreated with the help of a computer assistant. HR functions such as performance appraisal, learning and development, and talent acquisition are some of the areas where artificial intelligence has been introduced (Kaushal et al., 2023). By means of a variety of artificial intelligence technologies, it can be more comprehensive to help companies to form a learning organization culture that uses the traditional instructional design model based on traditional gap analysis. A customized curriculum can comprehensively test and locate staff levels through technical tools, and intelligently promote customized courses (Jia et al., 2018). In the process of education, AI technology can help learners automatically record learning data. Employees can simply enter learning objectives, archives and learn key points, and the course is automatically completed by the AI teacher (Jia et al., 2018). Methodology The current research is applicable in terms of its purpose. In terms of the method of data collection, it is descriptive-correlation in nature. The statistical population of this research in the qualitative part is made up of experts related to management issues (human resource management and system design) with a master's degree or higher in Sistan and Baluchistan province. In the quantitative part, all the employees of the departments of Sistan and Baluchistan province who have a bachelor's degree or higher, formed the statistical population of this research. The statistical population is considered unlimited in both qualitative and quantitative sections. Therefore, the sample size in the qualitative section is considered to be 20 people, selected by snowball method. In the quantitative part, Cochran's formula was used for unlimited communities with an error level of five percent, and the sample size of 384 people was determined and selected by simple random method. The tool of data collection in this research was researcher-made questionnaires. SPSS and Smart-PLS statistical software were used to analyze the data of this research. Findings The value of Cronbach's alpha for all variables of the research questionnaire is more than 0.7. Therefore, the research questionnaire has acceptable reliability. Also, the composite reliability value of all variables is higher than 0.7, which indicates the internal consistency of the reflective measurement model. All values related to convergent validity are also above 0.5, which indicates the similarity or internal validity of the reflective measurement model. Also, the reliability of the data collection tool and the quality of the reflective measurement model have also been confirmed. Conclusion Considering the importance and necessity of organizations to use artificial intelligence in the process of human resources management, a model for training employees using artificial intelligence was presented in this research, which has three parts according to the system mode: input, process and output. Model inputs include 1- educational data, 2- personal information, 3- educational needs, 4- user feedback, and 5- workplace data. The process of the model includes 1- determining the needs and goals, 2- collecting data, 3- pre-processing the data, 4- training the artificial intelligence model, 5- evaluating and improving the model, 6- implementing and deploying, and 7- monitoring and updating. The outputs of the model include 1- individual feedback, 2- educational suggestions, 3- monitoring and follow-up, and 4- support and guidance.