An AI-Linked Global Framework for a Self-Sustainable Engineering Education System

Education forms the foundation for a nation’s development. In today’s technology-driven era, educators are innovating to enhance learning methodologies. Integrating with Artificial Intelligence (AI), a globally adaptive education framework can facilitate self-sustainable education models that align with global dynamics. This study analyzes students' adaptability in online learning environments across government and non-government institutions, employing four deep learning algorithms to assess adaptability factors. Performance comparisons showed that the Random Forest Classifier achieved the highest accuracy at 87%, followed by Support Vector Classifier at 84%. Additionally, this research proposes an AI-enhanced education model to foster self-sustainable engineering education, ensuring equitable learning opportunities worldwide.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2025-04-08
DOI
https://doi.org/10.36948/ijfmr.2025.v07i02.41086
Primary Topic
Engineering Education and Technology
Type
article
Field-Weighted Citation Impact
0.00
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article

An AI-Linked Global Framework for a Self-Sustainable Engineering Education System

Latha Ramamoorthy
International Journal For Multidisciplinary Research
Engineering Education and Technology
article

An AI-Linked Global Framework for a Self-Sustainable Engineering Education System

Latha Ramamoorthy
article en

Abstract

Education forms the foundation for a nation’s development. In today’s technology-driven era, educators are innovating to enhance learning methodologies. Integrating with Artificial Intelligence (AI), a globally adaptive education framework can facilitate self-sustainable education models that align with global dynamics. This study analyzes students' adaptability in online learning environments across government and non-government institutions, employing four deep learning algorithms to assess adaptability factors. Performance comparisons showed that the Random Forest Classifier achieved the highest accuracy at 87%, followed by Support Vector Classifier at 84%. Additionally, this research proposes an AI-enhanced education model to foster self-sustainable engineering education, ensuring equitable learning opportunities worldwide.

International Journal For Multidisciplinary ResearchVol. 7(2)
Openalex Percentile: Top 91%
Engineering Education and Technology
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