Leveraging .NET Core Machine Learning and AI to Enhance Education

Integrating Machine Learning and Artificial Intelligence through .NET Core is revolutionizing the educational landscape by transforming traditional teaching and learning approaches. This comprehensive article explores how ML.NET, Microsoft's open-source machine learning framework, enables educational institutions to implement AI-driven solutions that enhance student engagement, reduce administrative workload, and improve learning outcomes. The article examines the framework's impact across various educational aspects, including personalized learning experiences, early identification of at-risk students, automated assessment systems, and real-time classroom analytics. It also addresses critical implementation considerations such as data privacy, system integration, and user training while highlighting future directions in educational technology through ML.NET applications

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2024-12-27
DOI
https://doi.org/10.36948/ijfmr.2024.v06i06.33974
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveraging .NET Core Machine Learning and AI to Enhance Education

Jagdip Keshavlal Jadav
International Journal For Multidisciplinary Research
Online Learning and Analytics
article

Leveraging .NET Core Machine Learning and AI to Enhance Education

Jagdip Keshavlal Jadav
article en

Abstract

Integrating Machine Learning and Artificial Intelligence through .NET Core is revolutionizing the educational landscape by transforming traditional teaching and learning approaches. This comprehensive article explores how ML.NET, Microsoft's open-source machine learning framework, enables educational institutions to implement AI-driven solutions that enhance student engagement, reduce administrative workload, and improve learning outcomes. The article examines the framework's impact across various educational aspects, including personalized learning experiences, early identification of at-risk students, automated assessment systems, and real-time classroom analytics. It also addresses critical implementation considerations such as data privacy, system integration, and user training while highlighting future directions in educational technology through ML.NET applications

International Journal For Multidisciplinary ResearchVol. 6(6)
Quality Education
Openalex Percentile: Top 73%
Online Learning and Analytics
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