Thesis:

Designing a classroom management model based on artificial intelligence and evaluating its impact on the lives and academic achievement of sixth grade elementary school students in Sabzevar city

Effective classroom management is a key factor in improving student achievement and academic vitality. Advances in artificial intelligence technology have made it possible to personalize learning, provide immediate feedback, and increase student motivation and engagement. Using smart models in the classroom can create an active and engaging learning environment. Therefore, the purpose of this research was to design a classroom management model based on artificial intelligence and evaluate its impact on the vitality and academic achievement of sixth grade elementary school students. The statistical population of the present study is all sixth grade elementary school students in Sabzevar city. The sampling method was cluster sampling. This means that we divided the different areas of the city into equal parts and received a sample according to the share of each part. The sample size was measured according to the Gpower software. Therefore, the sample size was selected as 213 people, which by considering 10% more than the sample, the number of samples was considered as 234. The research tools or scales were 4 questionnaires including the Taylor Fam Academic Achievement Questionnaire, the MLQ Multifactor Management Questionnaire, the Vitality Questionnaire (SVS), and the TAM Artificial Intelligence Questionnaire. After the researcher received a letter of introduction from the faculty and completed the questionnaire, a number of questionnaires were distributed among the students. To ensure confidentiality of the information, the students were asked to answer the questions independently and accurately and honestly without writing personal information in the questionnaires. After the students completed the questionnaires, the data were entered into SPSS and Amos software. The data were analyzed in two ways: descriptive statistics (frequency, mode, percentage, and mean) and inferential statistics (testing hypotheses and explaining the current and desired status between research variables). Covariance analysis and factor analysis were used to analyze the data. The results showed that based on the analysis of covariance, artificial intelligence was confirmed with a path coefficient of 0.87 at a zero error level and a confidence level of 99% in the field of classroom management. Also, artificial intelligence was confirmed with a path coefficient of 0.66 at a zero error level and a confidence level of 99% in the field of academic achievement. Artificial intelligence was also confirmed with a path coefficient of 0.52 at a zero error level and a confidence level of 99% in the field of academic vitality. Therefore, the results of the analysis of covariance showed that there was a positive and significant relationship between artificial intelligence and classroom management, academic achievement, and academic life. Also, the results of factor analysis of all four indicators were significant at a significance level of 99% with a P value equal to zero. Overall, the results showed that the designed model had a good fit.

Keywords: Keywords: Artificial intelligence, classroom management, academic life, academic progress, student