Adaptive Learning Using Generative Artificial Intelligence


Authors : Dr. Sathish Kumar; Sandeep H T; Siva Prasath V; Vishal Veeru B; Vishnu Nivasini J

Volume/Issue : Volume 10 - 2025, Issue 4 - April


Google Scholar : https://tinyurl.com/c5v93xkx

Scribd : https://tinyurl.com/3rc8rv75

DOI : https://doi.org/10.38124/ijisrt/25apr1686

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Abstract : Modern education often lacks the personalized support needed to address individual learning styles and academic challenges. This project presents an AI-powered personalized learning assistant that combines intelligent tutoring, automated summarization, and grade prediction to support self-guided learning. The system utilizes advanced AI models for natural language understanding, document analysis, and performance forecasting to adapt responses and content delivery to each learner. AI integration facilitates real-time explanations, concise PDF-based note generation, and interactive learning feedback. These features streamline the study process, reduce cognitive load, and promote deeper understanding through customized assistance. Additionally, the assistant applied machine learning techniques to track learning patterns and improve their recommendations over time, creating a dynamic and evolving support system. Emphasizing on usability and learner-centric design, the assistant aims to close the gap between technology and effective study habits, encouraging autonomy, academic confidence, and knowledge retention. The development, deployment, and evaluation of this system are explored in this study, highlighting its potential as a transformative educational tool.

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Modern education often lacks the personalized support needed to address individual learning styles and academic challenges. This project presents an AI-powered personalized learning assistant that combines intelligent tutoring, automated summarization, and grade prediction to support self-guided learning. The system utilizes advanced AI models for natural language understanding, document analysis, and performance forecasting to adapt responses and content delivery to each learner. AI integration facilitates real-time explanations, concise PDF-based note generation, and interactive learning feedback. These features streamline the study process, reduce cognitive load, and promote deeper understanding through customized assistance. Additionally, the assistant applied machine learning techniques to track learning patterns and improve their recommendations over time, creating a dynamic and evolving support system. Emphasizing on usability and learner-centric design, the assistant aims to close the gap between technology and effective study habits, encouraging autonomy, academic confidence, and knowledge retention. The development, deployment, and evaluation of this system are explored in this study, highlighting its potential as a transformative educational tool.

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