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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM39921
Volume:
8 |
12 |
Dec 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
An AI-enhanced educational platform can significantly improve learning outcomes by tailoring instruction to individual needs. Studies indicate that personalized learning can boost student performance by up to 30% and enhance engagement by over 60% [20]. Automated grading can reduce instructors’ grading time by nearly 40%, allowing more focus on student interaction [21] while real-time feedback has been shown to improve retention rates by approximately 50%[22], making education more adaptive and effective. This research presents an AI-enhanced educational platform designed to offer personalized learning paths, automate grading, and provide real-time feedback. The platform leverages advanced machine learning models, including Gradient Boosting, XGBoost, and Random Forest, to predict student performance and tailor educational experiences to individual needs . A dataset containing student information, including age, grade level, learning style, and academic performance, was used to train and evaluate these models. The results demonstrate the platform’s effectiveness in improving learning outcomes by adapting to students’ unique requirements. The challenges faced during implementation, such as dataset limitations, real-time feedback generation, and model interpretability [4]. Future directions for enhancing the platform and expanding its scope are also outlined.
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Publication Date
12/18/2024
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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