اولویت‌بندی پیشران‌های به‌کارگیری هوش مصنوعی در آموزش‌وپرورش با استفاده از روش مایرکا (مطالعه‌ی موردی: استان همدان)

Objective and Context: In the age of information, where sudden changes, contradictions, and problems in social, cultural, and political fields have become a reality, education has been greatly affected by the waves of globalization and digitalization. Artificial intelligence has become the subject of much scientific and non-scientific literature because it is growing faster than humanity can. The importance of integrating artificial intelligence (AI) into education is underscored by research that demonstrates achieving successful learning outcomes for students through an integrated set of requirements involving all stakeholders in the planning, development, and implementation processes. However, before any curriculum revision is considered, it is important to examine the barriers to entry and acceptance from the teachers' perspective. The importance and role of artificial intelligence in the education sector are clear and its acceptance is vital, but the role of teachers and education workers in the country is also vital for the acceptance and implementation of artificial intelligence technology in educational environments. This study was conducted to investigate and prioritize factors affecting the use of artificial intelligence in education. This research is necessary and often leads to recommendations for stakeholders and activists in the field of education, which directly affects the advancement of the country's educational policies. However, few researchers have comprehensively studied the factors affecting the adoption of artificial intelligence in education.

Methodology: This research is an analytical study with a completely quantitative approach, in which an attempt was made to prioritize the drivers of using artificial intelligence in education using one of the new multi-criteria decision-making methods. From the point of view of the purpose of this research, it is practical and cross-sectional in terms of time. The data was collected in the field through a questionnaire made by the researcher. The validity of the questionnaire questions was checked and confirmed by technical experts. The reliability of the tool was evaluated using Cronbach's alpha coefficient and the value was obtained and confirmed as 0.78. The statistical population of the current study was experts in the field of education and training who had sufficient familiarity with the use of artificial intelligence in education and had records of scientific research in this field. Sampling was done by snowball method among them and 11 experts were selected. MAIRKA method was used for data analysis.

Result and Conclusion: Eleven people participated in this study as experts. Six of them were educated in educational management, three in elementary education, and two in educational sciences. Their average age was 36.34, the youngest was 26 and the oldest was 58. Six of them had a bachelor's degree, four had a master's degree, and one had a specialized doctorate. Their average years of service was 23.28 years, the minimum years of service was 2 years and the maximum years of service was 30 years. Ten of the respondents were teachers employed in the Ministry of Education and one of them was a university faculty member. The geometric mean was used to convert experts' opinions into a single matrix. Before implementing the MAIRKA method, the weights of the investigated criteria were calculated using the Shannon entropy method. The obtained evidence confirms that the weight and importance of the drivers of using artificial intelligence in education are not the same, and no evidence was found to reject the hypothesis of this study. The attitude of teachers towards the phenomenon of artificial intelligence has a higher priority and importance than other drivers and has a deeper effect on the application of artificial intelligence in education. After that, the benefit they feel from using artificial intelligence can be effective in using this phenomenon.

Originality: The progress of the present study led to the identification of eight drivers of subjective norms, attitude, trust, perceived value, perceived risk, perceived usefulness, perceived ease of use, and intention to use based on scientific literature, which evidence obtained from the implementation of MAIRKA method It is confirmed that the drivers of attitude, perceived usefulness, perceived risk, trust, subjective norms, perceived ease of use, intention to use and perceived value in the use of artificial intelligence in education have priority. The findings of this study play a role in the advancement of scientific literature to identify and put together the aforementioned components in order to develop a new conceptual structure. Also, the results of examining the importance of the identified components are another aspect of the scientific contribution of the upcoming study. In addition to the above, this study has brought innovation and another aspect of scientific cooperation in the field of study by using the MAIRKA multi-criteria decision-making method as one of the latest methods.