Enhancing Personalized Learning: The Impact of Artificial Intelligence in Education

This research explores the transformative impact of artificial intelligence (AI) on personalized learning in educational settings. As traditional teaching methods struggle to cater to the diverse needs of students, AI offers innovative solutions that can tailor educational experiences to individual learning styles, preferences, and paces. This study investigates the various applications of AI technologies, including intelligent tutoring systems, adaptive learning platforms, and data-driven insights, to enhance personalized learning outcomes. By employing a mixed-methods approach that combines quantitative analysis of academic performance metrics with qualitative feedback from students and educators, the research aims to assess the effectiveness of AI-driven personalized learning initiatives. The findings reveal that AI not only improves student engagement and motivation but also facilitates differentiated instruction that addresses learning gaps and strengths. Additionally, the study identifies challenges such as data privacy concerns and the need for professional development for educators to effectively integrate AI solutions into their teaching practices. Ultimately, this research contributes to the ongoing discourse on the role of AI in education, offering actionable recommendations for educators and policymakers to optimize personalized learning experiences for all students.

Authors

Publication Details

Journal
Edu Spectrum
Published
2024-12-27
DOI
https://doi.org/10.70063/eduspectrum.v1i2.55
Citations
3
Primary Topic
Engineering Education and Technology
Type
article
Field-Weighted Citation Impact
2.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Personalized Learning: The Impact of Artificial Intelligence in Education

Muhammad Zailani Iman, Alfian Airlangga Asis, Aynu Uzma Zein Rahma
3 citations
Edu Spectrum
Engineering Education and Technology
2.34
article

Enhancing Personalized Learning: The Impact of Artificial Intelligence in Education

Muhammad Zailani Iman, Alfian Airlangga Asis, Aynu Uzma Zein Rahma
article en
3 citations

Abstract

This research explores the transformative impact of artificial intelligence (AI) on personalized learning in educational settings. As traditional teaching methods struggle to cater to the diverse needs of students, AI offers innovative solutions that can tailor educational experiences to individual learning styles, preferences, and paces. This study investigates the various applications of AI technologies, including intelligent tutoring systems, adaptive learning platforms, and data-driven insights, to enhance personalized learning outcomes. By employing a mixed-methods approach that combines quantitative analysis of academic performance metrics with qualitative feedback from students and educators, the research aims to assess the effectiveness of AI-driven personalized learning initiatives. The findings reveal that AI not only improves student engagement and motivation but also facilitates differentiated instruction that addresses learning gaps and strengths. Additionally, the study identifies challenges such as data privacy concerns and the need for professional development for educators to effectively integrate AI solutions into their teaching practices. Ultimately, this research contributes to the ongoing discourse on the role of AI in education, offering actionable recommendations for educators and policymakers to optimize personalized learning experiences for all students.

Edu SpectrumVol. 1(2)
Quality Education
Openalex Percentile: Top 8%
Engineering Education and Technology
2.34
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.