PERSONALIZED LEARNING VIA AI EDUCATIONAL HUB

Personalized learning has become a critical requirement in modern digital education environments where learners possess diverse interests, learning speeds, and knowledge backgrounds. Traditional e-learning platforms often follow a one-size-fits-all model that fails to address individual learner needs, resulting in low engagement, poor retention rates, and ineffective knowledge acquisition. This project proposes EduMorph AI, an intelligent educational hub designed to deliver adaptive and personalized learning experiences through artificial intelligence– driven recommendation techniques. The system integrates a hybrid recommendation mechanism that combines content-based filtering and collaborative filtering to recommend relevant courses and learning resources to users. Contentbased filtering utilizes Term Frequency–Inverse Document Frequency (TF-IDF) and cosine similarity to analyze course descriptions, topics, and metadata, while collaborative filtering employs Truncated Singular Value Decomposition (SVD) to analyze user–course interaction patterns and identify latent learning preferences. This hybrid strategy improves recommendation accuracy and effectively handles cold-start problems for new users or courses. Additionally, the platform incorporates Explainable Artificial Intelligence (XAI) to provide transparent reasoning behind each recommendation through interpretable explanations and reason codes, thereby improving user trust and learning motivation. The proposed system is implemented as a web-based educational platform that allows learners to access courses, receive personalized suggestions, track learning progress, and interact with a centralized educational hub. Experimental evaluation demonstrates that the hybrid recommendation model significantly improves recommendation relevance and learner engagement compared to conventional recommendation approaches. The EduMorph AI framework therefore provides an efficient, scalable, and learner-centric solution for next-generation digital education systems.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-21
DOI
https://doi.org/10.5281/zenodo.19145369
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PERSONALIZED LEARNING VIA AI EDUCATIONAL HUB

IJDIM
Zenodo (CERN European Organization for Nuclear Research)
Recommender Systems and Techniques
article

PERSONALIZED LEARNING VIA AI EDUCATIONAL HUB

IJDIM
article en

Abstract

Personalized learning has become a critical requirement in modern digital education environments where learners possess diverse interests, learning speeds, and knowledge backgrounds. Traditional e-learning platforms often follow a one-size-fits-all model that fails to address individual learner needs, resulting in low engagement, poor retention rates, and ineffective knowledge acquisition. This project proposes EduMorph AI, an intelligent educational hub designed to deliver adaptive and personalized learning experiences through artificial intelligence– driven recommendation techniques. The system integrates a hybrid recommendation mechanism that combines content-based filtering and collaborative filtering to recommend relevant courses and learning resources to users. Contentbased filtering utilizes Term Frequency–Inverse Document Frequency (TF-IDF) and cosine similarity to analyze course descriptions, topics, and metadata, while collaborative filtering employs Truncated Singular Value Decomposition (SVD) to analyze user–course interaction patterns and identify latent learning preferences. This hybrid strategy improves recommendation accuracy and effectively handles cold-start problems for new users or courses. Additionally, the platform incorporates Explainable Artificial Intelligence (XAI) to provide transparent reasoning behind each recommendation through interpretable explanations and reason codes, thereby improving user trust and learning motivation. The proposed system is implemented as a web-based educational platform that allows learners to access courses, receive personalized suggestions, track learning progress, and interact with a centralized educational hub. Experimental evaluation demonstrates that the hybrid recommendation model significantly improves recommendation relevance and learner engagement compared to conventional recommendation approaches. The EduMorph AI framework therefore provides an efficient, scalable, and learner-centric solution for next-generation digital education systems.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 13%
Recommender Systems and Techniques
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.