|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 101 |
| Published: May 2026 |
| Authors: Ashim Saha, Anshuman Laskar, Mainak Saha, Soumyajit Das, Rituraj Bhattacharjee |
10.5120/ijca9bc102a2c9e3
|
Ashim Saha, Anshuman Laskar, Mainak Saha, Soumyajit Das, Rituraj Bhattacharjee . SAARTHIAI: AN GENERATIVE AI-DRIVEN ADAPTIVE LEARNING SYSTEM FOR PERSONALIZED PROFESSIONAL LEARNING PLANS. International Journal of Computer Applications. 187, 101 (May 2026), 11-16. DOI=10.5120/ijca9bc102a2c9e3
@article{ 10.5120/ijca9bc102a2c9e3,
author = { Ashim Saha,Anshuman Laskar,Mainak Saha,Soumyajit Das,Rituraj Bhattacharjee },
title = { SAARTHIAI: AN GENERATIVE AI-DRIVEN ADAPTIVE LEARNING SYSTEM FOR PERSONALIZED PROFESSIONAL LEARNING PLANS },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 101 },
pages = { 11-16 },
doi = { 10.5120/ijca9bc102a2c9e3 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A Ashim Saha
%A Anshuman Laskar
%A Mainak Saha
%A Soumyajit Das
%A Rituraj Bhattacharjee
%T SAARTHIAI: AN GENERATIVE AI-DRIVEN ADAPTIVE LEARNING SYSTEM FOR PERSONALIZED PROFESSIONAL LEARNING PLANS%T
%J International Journal of Computer Applications
%V 187
%N 101
%P 11-16
%R 10.5120/ijca9bc102a2c9e3
%I Foundation of Computer Science (FCS), NY, USA
In today's rapidly evolving professional landscape, individuals must continuously update their skills to remain competitive. However, traditional educational systems and static e-learning platforms often fail to provide personalized learning paths tailored to each professional's goals, background, and pace. To address this challenge, we present SaarthiAI, an AI-driven adaptive learning system designed to generate customized professional learning plans and deliver targeted, interactive instruction. SaarthiAI integrates a Roadmap Generator leveraging retrieval-augmented generation (RAG) and dense vector retrieval via FAISS to construct personalized learning roadmaps from a knowledge base of industry-relevant content. It incorporates adaptive assessments powered by large language models to evaluate proficiency and dynamically adjust content difficulty. An AI Tutor chatbot module provides real-time contextual assistance and guidance. The system is implemented using Python, utilizing the Hugging Face Transformers library, MongoDB for data storage, and a RESTful API for seamless integration. Our contributions include the novel integration of RAG for roadmap generation, dynamic assessment mechanisms, and an interactive AI Tutor, collectively advancing personalized professional education.