AI-Driven Personalized Learning Systems for Gen Alpha and Beta: Opportunities and Challenges

This research paper explores the potential of AI-driven personalized learning systems for Generation Alpha (born 2010-2024) and Generation Beta (born 2025 onwards). As these digital natives enter educational institutions, there is a growing need for innovative learning approaches that cater to their unique characteristics and expectations. This study examines the opportunities and challenges associated with implementing AI-powered personalized learning systems for these generations. Through a comprehensive literature review and analysis of existing AI-driven educational technologies, we identify key factors that influence the effectiveness of personalized learning for Gen Alpha and Beta. The research also presents a conceptual framework for designing and implementing AI-driven personalized learning systems tailored to these generations’ needs. Our findings highlight the potential benefits of such systems, including improved engagement, enhanced learning outcomes, and the development of crucial 21st-century skills. However, we also discuss challenges such as data privacy concerns, the digital divide, and the need for continuous adaptation of AI algorithms. The paper concludes with recommendations for educators, policymakers, and technology developers to harness the full potential of AI-driven personalized learning for Gen Alpha and Beta while addressing associated challenges.

Authors

Institutions

Publication Details

Journal
American Journal of Innovation in Science and Engineering
Published
2025-04-21
DOI
https://doi.org/10.54536/ajise.v4i2.4644
Citations
1
Primary Topic
Biomedical and Engineering Education
Type
article
Field-Weighted Citation Impact
0.67
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-Driven Personalized Learning Systems for Gen Alpha and Beta: Opportunities and Challenges

Dharma Raj Ojha
1 citations
American Journal of Innovation in Science and Engineering
Biomedical and Engineering Education
0.67
article

AI-Driven Personalized Learning Systems for Gen Alpha and Beta: Opportunities and Challenges

Dharma Raj Ojha
article en
1 citations

Abstract

This research paper explores the potential of AI-driven personalized learning systems for Generation Alpha (born 2010-2024) and Generation Beta (born 2025 onwards). As these digital natives enter educational institutions, there is a growing need for innovative learning approaches that cater to their unique characteristics and expectations. This study examines the opportunities and challenges associated with implementing AI-powered personalized learning systems for these generations. Through a comprehensive literature review and analysis of existing AI-driven educational technologies, we identify key factors that influence the effectiveness of personalized learning for Gen Alpha and Beta. The research also presents a conceptual framework for designing and implementing AI-driven personalized learning systems tailored to these generations’ needs. Our findings highlight the potential benefits of such systems, including improved engagement, enhanced learning outcomes, and the development of crucial 21st-century skills. However, we also discuss challenges such as data privacy concerns, the digital divide, and the need for continuous adaptation of AI algorithms. The paper concludes with recommendations for educators, policymakers, and technology developers to harness the full potential of AI-driven personalized learning for Gen Alpha and Beta while addressing associated challenges.

American Journal of Innovation in Science and EngineeringVol. 4(2)
Far-western University (NP)
Openalex Percentile: Top 33%
Biomedical and Engineering Education
0.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

AI-Driven Personalized Learning Systems for Gen Alpha and Beta: Opportunities and Challenges — Dharma Raj Ojha · American Journal of Innovation in Science and Engineering (2025) | TGRS Research Map | TGRS