Emerging applications of machine learning technology in the educational system | ||
| فناوری و دانش پژوهی در تعلیم و تربیت | ||
| Articles in Press, Accepted Manuscript, Available Online from 20 December 2025 | ||
| Document Type: Original Article | ||
| DOI: 10.30473/t-edu.2025.73752.1250 | ||
| Authors | ||
| mohammad mohsen sadr* 1; mostafa akkhavan safar2 | ||
| 1Assistant Professor of Information Technology, Department of Computer and Information Technology Engineering, Payame Noor University, Tehran, Iran . | ||
| 2Assistant Professor of Information Technology, Department of Computer and Information Technology Engineering, Payame Noor University, Tehran, Iran. | ||
| Abstract | ||
| The application of machine learning in education transforms the teaching and learning process, providing educational institutions with innovative tools to monitor and enhance student performance and participation. The use of machine learning in education can help improve the quality of learning and increase the success of students. It includes various areas such as learning data analysis, personalized education, predicting academic performance, smart educational tools, diagnosing learning problems, academic guidance and counseling, etc. A personalized approach with machine learning helps make education more inclusive, accessible and engaging. The purpose of this study is to investigate how machine learning developers can improve various processes in the educational sector, especially in highly used cases and real examples. We will also examine the potential challenges of implementing machine learning and discuss strategies for addressing these challenges through the expertise of machine learning professionals. Additionally, to review the relevant literature, we analyzed articles published in the Science Direct (1,218) and Web of Science (2,794) databases. This analysis revealed that topics such as artificial intelligence, online learning, and personalization are among the most extensively studied areas. | ||
| Keywords | ||
| Education; machine learning; comparative assessment; personalized educating | ||
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