Mohit Sachan,
Pankaj Jain,
Laxmi Poonia,
- Research Scholar, Department of Computer Science, JECRC University, Jaipur, Rajasthan,
- Assistant Professor, Department of Computer Science, JECRC University, Jaipur, Rajasthan, India
- Assistant Professor, Department of Computer Science, JECRC University, Jaipur, Rajasthan, India
Abstract
Machine learning (ML) is transforming the contemporary education by transforming it into smarter, data-driven and personalised learning. This review examines the key tendencies, advantages, and possible threats of applying ML in intelligent education. ML promotes adaptive learning, automatization of assessments, and student engagement, which is highly beneficial both to learners and educators. Nonetheless, issues like data privacy, algorithmic bias or unequal access are also a significant concern. The article emphasises the necessity of ethical and inclusive methodologies that would allow making sure that ML technologies can enhance the quality of education and make sure that the threats are contained. Digital technologies in education have resulted in the creation of smart education systems, which produce a lot of student data. These datasets provide an important source of data regarding the learning behaviour of students, academic achievement, and the levels of engagement. Machine learning can be used to give a potent tool in analysing educational data and predicting student outcomes. The early anticipation of the grades will help the teachers to detect the students with learning problems and support them accordingly. The study hypothesises a machine learning-centred system to forecast the academic performance of students through educational data including attendance, studying time, assignment grades, internal evaluation, and the past academic history. The system uses guided machine learning models such as Logistic regression, decision tree, random forest and support vector machine to identify levels of student performances. The experimental results prove that the Random Forest algorithm is the most accurate in making predictions in comparison to other algorithms. This paper emphasises the value of using data to make decisions in contemporary education or how predictive analytics can enhance teaching methods, boost student learning, and boost the success rates of students.
Keywords: Machine learning, education, adaptive systems, learning analytics, student performance prediction, personalized learning
[This article belongs to International Journal of Education Sciences ]
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International Journal of Education Sciences
| Volume | 03 | |
| Issue | 02 | |
| Received | 05/02/2026 | |
| Accepted | 13/06/2026 | |
| Published | 23/06/2026 | |
| Publication Time | 138 Days |