Personalized AI Tutor: An Intelligent Adaptive Learning System for Early Education

This research presents a Personalized AI Tutor system designed for adaptive learning in early education. The proposed system utilizes artificial intelligence, machine learning, and learning analytics techniques to create a personalized educational environment for students. The platform continuously analyzes student performance, learning patterns, topic completion status, assessment scores, and interaction behavior in order to dynamically recommend suitable educational content and learning materials. Based on the understanding level of each learner, the system intelligently adjusts recommendations and learning paths to improve educational effectiveness and student engagement. The developed AI Tutor integrates adaptive assessments, intelligent recommendation systems, performance analytics, and progress tracking mechanisms to provide customized learning experiences for students. The project aims to enhance personalized education, improve accessibility to digital learning resources, and support student-centric adaptive learning methodologies. This research contributes toward the development of intelligent tutoring systems capable of supporting modern digital education platforms through AI-driven personalization and adaptive educational technologies. Project Demo:https://personalized-ai-tutor.netlify.app

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-19
DOI
https://doi.org/10.5281/zenodo.20285594
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Personalized AI Tutor: An Intelligent Adaptive Learning System for Early Education

Maniteja Aligeti, Pavan Eslavath, Nageena Shaik, Varalakshmi Aparna
Zenodo (CERN European Organization for Nuclear Research)
Intelligent Tutoring Systems and Adaptive Learning
article

Personalized AI Tutor: An Intelligent Adaptive Learning System for Early Education

Maniteja Aligeti, Pavan Eslavath, Nageena Shaik, Varalakshmi Aparna
article en

Abstract

This research presents a Personalized AI Tutor system designed for adaptive learning in early education. The proposed system utilizes artificial intelligence, machine learning, and learning analytics techniques to create a personalized educational environment for students. The platform continuously analyzes student performance, learning patterns, topic completion status, assessment scores, and interaction behavior in order to dynamically recommend suitable educational content and learning materials. Based on the understanding level of each learner, the system intelligently adjusts recommendations and learning paths to improve educational effectiveness and student engagement. The developed AI Tutor integrates adaptive assessments, intelligent recommendation systems, performance analytics, and progress tracking mechanisms to provide customized learning experiences for students. The project aims to enhance personalized education, improve accessibility to digital learning resources, and support student-centric adaptive learning methodologies. This research contributes toward the development of intelligent tutoring systems capable of supporting modern digital education platforms through AI-driven personalization and adaptive educational technologies. Project Demo:https://personalized-ai-tutor.netlify.app

Zenodo (CERN European Organization for Nuclear Research)
Memorial (RU)
Quality Education
Openalex Percentile: Top 3%
Intelligent Tutoring Systems and Adaptive Learning
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.