Introduction and Purpose: The aim of this study was to examine the role of artificial intelligence (AI) and machine learning technologies in the development of personalized education and to analyze their impacts on deep learning and educational equity in secondary education.
Methodology: This semi-experimental research employed a pre-test–post-test control group design. The statistical population consisted of urban 10th-grade students, and a cluster random sampling method was used to select the sample, which included two groups: experimental (25) and control (25). The data collection tools were an academic achievement test, a self-regulated learning inventory, and a perceived educational equity questionnaire. The experimental group received personalized instruction based on machine learning algorithms and adaptive learning systems for 12 weeks, while the control group received traditional teaching methods. Data were analyzed using analysis of covariance (ANCOVA) and independent t-tests.
Results: The findings indicated that AI-based instruction significantly increased academic achievement scores, enhanced deep conceptual learning, and improved perceived educational equity compared to the control group.
Conclusion: Based on the results, it can be concluded that the integration of artificial intelligence into education not only enhances students’ motivation and engagement but also provides fairer learning opportunities for students with diverse abilities and backgrounds. It is recommended that educational policymakers and planners prioritize the development of technical infrastructures and the training of teachers for effective implementation of these technologies.