IMPLEMENTATION OF AN AI-BASED PERSONALIZED LEARNING PLATFORM (LMS) IN THE EDUCATIONAL PROCESS OF MICROBIOLOGY AND VIROLOGY

This study explores the integration of an AI-based personalized learning management system (LMS) into the teaching of microbiology and virology. As these disciplines require a strong synthesis of theoretical and practical knowledge, traditional educational models often fail to meet the needs of diverse learners. The research presents a comparative analysis between conventional and adaptive AI-supported instruction, detailing the design and implementation of an LMS platform tailored to students’ individual progress and learning profiles. Experimental results from a controlled study involving pre- and post-testing, user feedback, and statistical analysis demonstrate a significant improvement in knowledge acquisition and engagement among students using the AI-based LMS. The findings highlight the system’s effectiveness in enhancing motivation, providing real-time feedback, and delivering virtual laboratory simulations that closely mirror real-world practice. The study concludes with pedagogical recommendations and outlines future directions for AI integration in medical education.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-30
DOI
https://doi.org/10.5281/zenodo.16601718
Primary Topic
E-Learning and COVID-19
Type
article
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article

IMPLEMENTATION OF AN AI-BASED PERSONALIZED LEARNING PLATFORM (LMS) IN THE EDUCATIONAL PROCESS OF MICROBIOLOGY AND VIROLOGY

Zenodo (CERN European Organization for Nuclear Research)
E-Learning and COVID-19
article

IMPLEMENTATION OF AN AI-BASED PERSONALIZED LEARNING PLATFORM (LMS) IN THE EDUCATIONAL PROCESS OF MICROBIOLOGY AND VIROLOGY

article en

Abstract

This study explores the integration of an AI-based personalized learning management system (LMS) into the teaching of microbiology and virology. As these disciplines require a strong synthesis of theoretical and practical knowledge, traditional educational models often fail to meet the needs of diverse learners. The research presents a comparative analysis between conventional and adaptive AI-supported instruction, detailing the design and implementation of an LMS platform tailored to students’ individual progress and learning profiles. Experimental results from a controlled study involving pre- and post-testing, user feedback, and statistical analysis demonstrate a significant improvement in knowledge acquisition and engagement among students using the AI-based LMS. The findings highlight the system’s effectiveness in enhancing motivation, providing real-time feedback, and delivering virtual laboratory simulations that closely mirror real-world practice. The study concludes with pedagogical recommendations and outlines future directions for AI integration in medical education.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 50%
E-Learning and COVID-19
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