International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 6 Issue 6
November-December 2024
Indexing Partners
AI Enabled Personalized Learning Platform
Author(s) | Niranjan Murthy C, Kavya S, Hemalatha M R, Kousalya M D, Kavyashree M |
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Country | India |
Abstract | The AI-powered individualized learning environment designed specifically for teaching programming. The platform makes it easier for users to log in, choose a domain, take interactive tests, assess their performance, and receive personalized video recommendations based on how well students perform. Understanding the value of individualized instruction in programming, the platform dynamically adjusts to each user's skill level and learning goals. Front-end and back-end technologies are seamlessly integrated to allow users to take quizzes specific to the domain they have chosen, get instant response, and get performance reviews. The website makes recommendations for videos based on users' skill levels. The architecture, functionality, user experience, implementation specifics, evaluation outcomes, and future directions of the platform are all made clear in this document. Through providing insights into AI-powered tailored learning, this study advances teaching methodologies and gives students more confidence when it comes to programming. |
Keywords | Artificial intelligence, programming education, student involvement, algorithmic bias, moral considerations, new technologies, recommendation engines, user interfaces, and performance evaluations |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 6, Issue 3, May-June 2024 |
Published On | 2024-06-27 |
Cite This | AI Enabled Personalized Learning Platform - Niranjan Murthy C, Kavya S, Hemalatha M R, Kousalya M D, Kavyashree M - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.23253 |
DOI | https://doi.org/10.36948/ijfmr.2024.v06i03.23253 |
Short DOI | https://doi.org/gt243m |
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