Abstract
The aim of this research is to design and validate the individualization model of education in oil company staff, with a meta-synthesis approach. This research is fundamental in terms of its objective, qualitative in terms of the data collection method, and meta-synthesis in terms of the research implementation method. The statistical population of the research includes all documents, theoretical foundations, and background related to individualization of education in domestic (2021-2024) and foreign (2000-2024) databases. The non-random sampling method is purposive and the sample size is based on systematic elimination based on the flow chart of the Prism model. The data collection tool is a systematic review of documents and literature. In order to calculate validity, a 27-item checklist based on the Prisma model was used, and Cohen's kappa coefficient was used to calculate reliability, which results indicate that the tool is valid and reliable. The data analysis method is thematic analysis including basic, organizing and comprehensive themes with MaxQDA 2018 software. The findings showed that the individualization of education in oil company staff includes three cognitive, emotional and behavioral dimensions, where the cognitive dimension includes the components of teaching method (6 indicators), educational content (3 indicators), evaluation (5 indicators), and self-regulated learning (7 indicators); the emotional dimension includes the components of motivation (7 indicators), social interactions (10 indicators), and emotional support (6 indicators); and finally the behavioral dimension includes the components of experiential learning (5 indicators), modeling (5 indicators), and practical skills (4 indicators).
Introduction
In the current changing, complex and uncertain world, the need and necessity of training and developing human capital in organizations has been accepted by managers for several reasons. In fact, the rapid and profound changes in the knowledge and technologies required by organizations in the current situation have made them inevitable to continuously learn this new knowledge and technologies and to forget and get rid of traditional habits and methods that have lost their effectiveness (Razmi et al, 2018). In recent years, significant success has been achieved using learning analytics, where data on individuals' responses to specific educational techniques, contents, and learning resources are recorded in large quantities. This data is then analyzed to recognize patterns and build predictive models to prescribe appropriate learning and training choices according to each person's learning characteristics; therefore, today's structural change towards the use of big data and learning analytics in education has led to many possibilities in the field of education personalization (Zhang & Aslan, 2021).
On the other hand, what is certain is that one of the most key elements in any organization is the human resources of that organization, which are considered a valuable asset. Effective and optimal use of human resources capabilities to increase their productivity requires the development of a set of effective strategies and actions regarding employee training (Akhavan & Kazemi-Gorji, 2019). Human resource management is not only a profit-oriented approach to employees, but also a special approach to employee relations with an emphasis on commitment and two-way communication. Human resource management refers to the policies and actions required to implement part of the management task related to aspects of employee activity, especially for recruiting, training employees, evaluating performance, rewarding, and creating a healthy and fair environment for the organization's employees (Fallah et al, 2021).
Therefore, the present study, focusing on the individualization of training in oil company staff, seeks to fill the gap in past research, and its practical results can help improve and reform the country's educational system. Thus, considering what has been said, the purpose of this research is to answer the question: what is the model of individualized education in oil company staff?
Theoretical framework
Individualized education
Individualized education is an effective strategy to strengthen individual commitment and responsibility and change attitudes and enhance learning transfer. Individualized education is a systematic effort to achieve a balance between the characteristics of the learner and the characteristics of the learning environment (Ouf et al, 2017). When individuals are given the right to choose, they gain self-control over their learning according to their interests, which leads to a sense of responsibility and greater focus on work.
Castaño & Villar-Onrubia (2023) studied the assessment of the presence of the concept of "personalized learning environment" in the web domains of Spanish higher education institutions. They pointed out the importance of the personal learning environment and its impact on learners' autonomy. The concept of "personalized learning environment" has attracted significant levels of attention in the field of educational technology. Familiarity of students and teachers with this concept is something that can help higher education institutions in which their communities make better decisions about the resources they use to develop their academic activity. Convergence is something that can help students improve their autonomy in self-regulating their learning processes and improve their agency for lifelong learning.
Yiğit & Seferoğlu (2023) investigated the effect of video feedback on students' use of feedback in an online learning environment. They emphasized the role of feedback in enhancing learning and showed that the use of technologies can bring positive results. 
Research Methodology
This research is fundamental in terms of its objective, qualitative in terms of the data collection method, and meta-synthesis in terms of the research implementation method. The statistical population of the research includes all documents, theoretical foundations, and background related to individualization of education in domestic (2021-2024) and foreign (2000-2024) databases. The non-random sampling method is purposive, and the sample size is based on systematic elimination based on the flow chart of the Prism model. The data collection tool is a systematic review of documents and literature. In order to calculate validity, a 27-item checklist based on the Prisma model was used, and Cohen's kappa coefficient was used to calculate reliability, which results indicate that the tool is valid and reliable.
Research findings
The data analysis method is thematic analysis including basic, organizing and comprehensive themes with MaxQDA 2018 software. The findings showed that the individualization of education in oil company staff includes three cognitive, emotional and behavioral dimensions, where the cognitive dimension includes the components of teaching method (6 indicators), educational content (3 indicators), evaluation (5 indicators), and self-regulated learning (7 indicators); the emotional dimension includes the components of motivation (7 indicators), social interactions (10 indicators), and emotional support (6 indicators); and finally the behavioral dimension includes the components of experiential learning (5 indicators), modeling (5 indicators), and practical skills (4 indicators).
Conclusion
The present study was conducted with the aim of designing and validating the individualization of education model in oil company staff, using a meta-synthesis approach. These results are consistent with the research results of Jones & Smith (2020), Brown & Miller (2019), Davis & Taylor (2021), Castaño & Villar-Onrubia (2023), Yiğit & Seferoğlu (2023), Bhutoria (2022), Naderi et al, (2020), Abbasi et al, (2021), and Tetzlaff et al, (2021). Bhutoria (2022) has pointed out the role of AI in individualizing education and creating educational programs tailored to the needs of learners. The success of AI in meeting the specific learning needs, learning habits and learning abilities of students and guiding them to optimal learning paths brings in all three countries. It is also evident from the literature that AI augments educational content, customizes it for each individual according to his/her needs, and raises the caution flag for anticipated learning difficulties. This re-aligns the role of instructors and also optimizes the teaching-learning environment for a better learning experience. The upward trajectory of educational development with AI opens a new horizon of personalized education for the next generation, but it also comes with challenges. Issues related to data privacy, availability of digital resources, and affordability constraints have been reported in recent literature as obstacles to promoting such technologies for daily practice.
Considering the research findings on the individualization of training of oil company staff, practical suggestions are provided for each dimension (cognitive, affective, and behavioral).
A- Suggestions for the cognitive dimension
1-   Diversity in teaching methods: Providing training courses using diverse teaching methods such as project-based learning, interactive learning, and traditional teaching. This can include holding practical workshops and using new technologies such as webinars and online courses.
B- Suggestions for the emotional dimension
1-   Creating a positive and supportive work environment: Holding motivational workshops and team meetings to encourage participation in decision-making and strengthen team spirit. This can include holding social events and group activities.
C- Suggestions for the behavioral dimension
1-   Holding practical workshops and simulations: Designing and implementing practical workshops that allow employees to strengthen their skills in real situations. Using real simulations for experiential learning can also help to deepen learning.