Abstract

The aim was to design a model for the application of artificial intelligence in the learning of first-year high school students. The research was applied in terms of purpose and mixed in terms of method. The qualitative statistical population was 19 managers, experts specializing in artificial intelligence and educational management of schools, universities and education. In the quantitative part, 11,084 students from secondary schools in Tehran's District 9 were selected. The sample was calculated as 193 students and was selected in a multi-stage cluster. The data collection tool in the qualitative part was a semi-structured interview, and in the quantitative part, a researcher-made questionnaire to determine the current situation and influential factors for students. Data analysis and analysis In the qualitative part, the interviews were coded and analyzed using qualitative software. In the quantitative part, exploratory factor analysis, confirmatory factor analysis, and one-sample t-test were used. The research model was approved based on the philosophy and goals of the model, theoretical foundations, and effective factors in designing the application pattern of artificial intelligence in the learning education system based on the findings of the qualitative section, including: culture and ethics in smart learning in first place, smart learning style in second place, innovation and creativity in smart learning in third place, and educational policymaking in fourth place with 99% confidence.
The aim was to design a model for the application of artificial intelligence in the learning of first-year high school students. The research was applied in terms of purpose and mixed in terms of method. The qualitative statistical population was 19 managers, experts specializing in artificial intelligence and educational management of schools, universities and education. In the quantitative part, 11,084 students from secondary schools in Tehran's District 9 were selected. The sample was calculated as 193 students and was selected in a multi-stage cluster. The data collection tool in the qualitative part was a semi-structured interview, and in the quantitative part, a researcher-made questionnaire to determine the current situation and influential factors for students. Data analysis and analysis In the qualitative part, the interviews were coded and analyzed using qualitative software. In the quantitative part, exploratory factor analysis, confirmatory factor analysis, and one-sample t-test were used. The research model was approved based on the philosophy and goals of the model, theoretical foundations, and effective factors in designing the application pattern of artificial intelligence in the learning education system based on the findings of the qualitative section, including: culture and ethics in smart learning in first place, smart learning style in second place, innovation and creativity in smart learning in third place, and educational policymaking in fourth place with 99% confidence.
The aim was to design a model for the application of artificial intelligence in the learning of first-year high school students. The research was applied in terms of purpose and mixed in terms of method. The qualitative statistical population was 19 managers, experts specializing in artificial intelligence and educational management of schools, universities and education. In the quantitative part, 11,084 students from secondary schools in Tehran's District 9 were selected. The sample was calculated as 193 students and was selected in a multi-stage cluster. The data collection tool in the qualitative part was a semi-structured interview, and in the quantitative part, a researcher-made questionnaire to determine the current situation and influential factors for students. Data analysis and analysis In the qualitative part, the interviews were coded and analyzed using qualitative software. In the quantitative part, exploratory factor analysis, confirmatory factor analysis, and one-sample t-test were used. The research model was approved based on the philosophy and goals of the model, theoretical foundations, and effective factors in designing the application pattern of artificial intelligence in the learning education system based on the findings of the qualitative section, including: culture and ethics in smart learning in first place, smart learning style in second place, innovation and creativity in smart learning in third place, and educational policymaking in fourth place with 99% confidence.
The aim was to design a model for the application of artificial intelligence in the learning of first-year high school students. The research was applied in terms of purpose and mixed in terms of method. The qualitative statistical population was 19 managers, experts specializing in artificial intelligence and educational management of schools, universities and education. In the quantitative part, 11,084 students from secondary schools in Tehran's District 9 were selected. The sample was calculated as 193 students and was selected in a multi-stage cluster. The data collection tool in the qualitative part was a semi-structured interview, and in the quantitative part, a researcher-made questionnaire to determine the current situation and influential factors for students. Data analysis and analysis In the qualitative part, the interviews were coded and analyzed using qualitative software. In the quantitative part, exploratory factor analysis, confirmatory factor analysis, and one-sample t-test were used. The research model was approved based on the philosophy and goals of the model, theoretical foundations, and effective factors in designing the application pattern of artificial intelligence in the learning education system based on the findings of the qualitative section, including: culture and ethics in smart learning in first place, smart learning style in second place, innovation and creativity in smart learning in third place, and educational policymaking in fourth place with 99% confidence.
The aim was to design a model for the application of artificial intelligence in the learning of first-year high school students. The research was applied in terms of purpose and mixed in terms of method. The qualitative statistical population was 19 managers, experts specializing in artificial intelligence and educational management of schools, universities and education. In the quantitative part, 11,084 students from secondary schools in Tehran's District 9 were selected. The sample was calculated as 193 students and was selected in a multi-stage cluster. The data collection tool in the qualitative part was a semi-structured interview, and in the quantitative part, a researcher-made questionnaire to determine the current situation and influential factors for students. Data analysis and analysis In the qualitative part, the interviews were coded and analyzed using qualitative software. In the quantitative part, exploratory factor analysis, confirmatory factor analysis, and one-sample t-test were used. The research model was approved based on the philosophy and goals of the model, theoretical foundations, and effective factors in designing the application pattern of artificial intelligence in the learning education system based on the findings of the qualitative section, including: culture and ethics in smart learning in first place, smart learning style in second place, innovation and creativity in smart learning in third place, and educational policymaking in fourth place with 99% confidence.