The Effectiveness of AI-Based Education Management Systems in Implementing Deep Learning Curriculum in Elementary Schools
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Abstract
The purpose of this study is to evaluate the effectiveness of artificial intelligence (AI) in supporting elementary school teachers in implementing the deep learning curriculum. Two groups from a public elementary school in Sumedang Regency participated in a quantitative, quasi-experimental design. While the control group employed traditional teaching techniques, the experimental group used AI-based support. Pretest and posttest tools for learning outcomes were used to gather data. The Shapiro-Wilk normality test, Levene's homogeneity test, Wilcoxon signed-rank test, and N-Gain effectiveness analysis were among the methods used to analyze the data. The results show that students in the experimental group outperformed those in the control group in terms of their learning outcomes. A significant difference between the pretest and posttest scores was indicated by the Wilcoxon test's significance value of 0.000. The experimental group's average N-Gain score was 0.58 (58.13%), which was considered moderate and higher than the control group's 0.37 (37.48%). These findings show that integrating AI not only increases student learning effectiveness but also gives teachers the ability to evaluate learning data in real time and modify their lessons to meet the needs of their students. It is determined that deep learning curricula in elementary schools can be successfully supported by the application of AI. To guarantee long-term implementation, schools are urged to improve their digital infrastructure and offer sufficient teacher training.
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