AI Tutors in E-Learning: Analyzing Personalized Learning Pathways

The integration of artificial intelligence (AI) in e- learning has ushered in a transformative era, enabling person- alized learning pathways tailored to individual student needs. This research investigates the impact of AI-powered personal- ized tutors on student engagement and learning outcomes. By synthesizing insights from existing literature and conducting an empirical evaluation, this study demonstrates how AI systems dynamically adapt learning experiences, resulting in improved engagement and retention. However, challenges such as data pri- vacy, algorithmic bias, and the ethical implications of automated learning systems require attention. This paper highlights the need for robust frameworks to ensure equitable, transparent, and effective deployment in diverse educational contexts. The findings provide actionable insights for educators, policymakers, and developers aiming to maximize the benefits of personalized AI in e-learning

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Publication Details

Journal
Journal of Artificial Intelligence & Cloud Computing
Published
2025-02-04
DOI
https://doi.org/10.47363/jaicc/2025(4)e250
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

AI Tutors in E-Learning: Analyzing Personalized Learning Pathways

Syed Arham Akheel
Journal of Artificial Intelligence & Cloud Computing
Online Learning and Analytics
article

AI Tutors in E-Learning: Analyzing Personalized Learning Pathways

Syed Arham Akheel
article en

Abstract

The integration of artificial intelligence (AI) in e- learning has ushered in a transformative era, enabling person- alized learning pathways tailored to individual student needs. This research investigates the impact of AI-powered personal- ized tutors on student engagement and learning outcomes. By synthesizing insights from existing literature and conducting an empirical evaluation, this study demonstrates how AI systems dynamically adapt learning experiences, resulting in improved engagement and retention. However, challenges such as data pri- vacy, algorithmic bias, and the ethical implications of automated learning systems require attention. This paper highlights the need for robust frameworks to ensure equitable, transparent, and effective deployment in diverse educational contexts. The findings provide actionable insights for educators, policymakers, and developers aiming to maximize the benefits of personalized AI in e-learning

Journal of Artificial Intelligence & Cloud Computing
Bellevue College (US)
Openalex Percentile: Top 99%
Online Learning and Analytics
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AI Tutors in E-Learning: Analyzing Personalized Learning Pathways — Syed Arham Akheel · Journal of Artificial Intelligence & Cloud Computing (2025) | TGRS Research Map | TGRS