Generative AI’s Impact on Highly Personalized Learning

This paper explores how Generative Artificial Intelligence (AI) enhances personalized learning by dynamically adjusting content, providing immediate feedback, and tailoring educational pathways. Although personalized education is increasingly essential, traditional methods often overlook individual learner differences. Current research largely focuses on AI-driven content generation, overlooking the creation of adaptive learning experiences responsive to evolving learner needs. This study aims to address this gap by examining how Generative AI’s adaptability, feedback, and personalization capabilities influence perceived learning efficiency among learners in both higher education and corporate training contexts. Drawing from Technology Acceptance Model (TAM) and Task-Technology Fit (TTF) frameworks, the research also explores how prior AI experience moderates these relationships. Results could inform adaptive curricula and training programs, improve educational outcomes, enhance workforce readiness, and foster inclusive learning opportunities across diverse learner populations.

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Publication Details

Journal
Journal of the Association for Information Systems
Published
2025-08-15
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
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article

Generative AI’s Impact on Highly Personalized Learning

Journal of the Association for Information Systems
Intelligent Tutoring Systems and Adaptive Learning
article

Generative AI’s Impact on Highly Personalized Learning

article en

Abstract

This paper explores how Generative Artificial Intelligence (AI) enhances personalized learning by dynamically adjusting content, providing immediate feedback, and tailoring educational pathways. Although personalized education is increasingly essential, traditional methods often overlook individual learner differences. Current research largely focuses on AI-driven content generation, overlooking the creation of adaptive learning experiences responsive to evolving learner needs. This study aims to address this gap by examining how Generative AI’s adaptability, feedback, and personalization capabilities influence perceived learning efficiency among learners in both higher education and corporate training contexts. Drawing from Technology Acceptance Model (TAM) and Task-Technology Fit (TTF) frameworks, the research also explores how prior AI experience moderates these relationships. Results could inform adaptive curricula and training programs, improve educational outcomes, enhance workforce readiness, and foster inclusive learning opportunities across diverse learner populations.

Journal of the Association for Information Systems
Florida International University (US)
Quality Education
Openalex Percentile: Top 72%
Intelligent Tutoring Systems and Adaptive Learning
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Generative AI’s Impact on Highly Personalized Learning · Journal of the Association for Information Systems (2025) | TGRS Research Map | TGRS