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Smart Skill Hub: An AI-Driven Platform for Personalized Learning, Career Tracking, And Skill Development
Subject area: Science,Engineering and Technology · Area of research: AI-assisted Personalized Learning
Abstract
The rapid growth of software technologies has created a significant challenge for students and early-career developers: acquiring technical skills is no longer sufficient without continuously identifying skill gaps, following structured learning paths, demonstrating practical coding ability, maintaining a professional portfolio, and preparing effective resumes. Existing learning platforms generally address individual aspects of this process, resulting in fragmented workflows in which learners use multiple applications for learning, coding practice, resume creation, GitHub analysis, portfolio development, and career guidance. This paper presents Smart Skill Hub (SSH), an integrated AI-powered skill development and career readiness platform designed to unify these activities within a single ecosystem. Smart Skill Hub combines personalized skill profiling, skill-gap analysis, AI-generated learning roadmaps, educational video discovery, coding assessment, GitHub intelligence, AI-assisted resume analysis and generation, portfolio management, analytics, and an AI-based Smart Mentor. The platform uses a modern web architecture consisting of a React and TypeScript frontend, Node.js and Express backend services, MongoDB for persistent data storage, Firebase Authentication for identity management, and centralized Large Language Model services for intelligent features. A secure Docker-based coding execution environment is used to execute programming submissions with resource and network restrictions. The platform maintains a canonical representation of learner skills and profile information so that information can be reused consistently across learning, assessment, resume, portfolio, and mentoring workflows. Resume processing supports document extraction, structured profile generation, interactive A4 editing, multiple ATS-oriented templates, AI-assisted improvement, job-specific analysis, and selectable-text PDF generation. GitHub Intelligence analyses authenticated GitHub activity to derive development insights, while analytics provide learners and administrators with measurable information about activity and system usage. Smart Skill Hub demonstrates an integrated approach to student skill development by connecting learning, assessment, professional representation, and AI-based guidance in a unified platform. The resulting system is designed to reduce fragmentation, improve personalization, and provide students with a continuous workflow from identifying skills and gaps to developing, demonstrating, and presenting those skills.
Keywords
artificial intelligence; coding assessment; career development; personalized learning; skill gap analysis
References
[1] Kumar S, Sharma R, Singh P. Artificial intelligence in education: Enhancing learning outcomes and student engagement. IEEE Access. 2022;10:112345–112356.
[2] Smith J, Wang L. Personalized learning systems using machine learning techniques. International Journal of Educational Technology. 2021;15(3):45–58.
[3] Brown M, Davis K, Wilson T. Resume classification and analysis using natural language processing. IEEE Transactions on Computational Intelligence. 2023;8(2):210–220.
[4] Patel A, Mehta R. Skill recommendation systems based on collaborative filtering. In: Proceedings of the IEEE International Conference on Data Mining (ICDM); 2020. p. 134–139.
[5] GitHub Inc. GitHub REST API documentation. 2024. https://docs.github.com/en/rest
[6] MongoDB Inc. MongoDB Atlas documentation. 2024. https://www.mongodb.com/docs/atlas/
[7] Google. Firebase Authentication documentation. 2024. https://firebase.google.com/docs/auth
[8] Johnson N, Lee P. Cloud-based scalable architectures for educational platforms. IEEE Cloud Computing. 2022;9(4):30–39.
[9] Gupta R, Verma S. Analyzing student performance using machine learning techniques. International Journal of Computer Applications. 2020;182(44):15–22.
[10] Nguyen T, Zhao H. AI-based recommendation systems for online learning platforms. IEEE Transactions on Learning Technologies. 2021;14(3):256–267.
How to cite this paper
@article{1723935,
author = {Ravi Dinni, Rajashree Pujari, Renuka Jogin, Sampatakumar V, Asst. Prof. Sourav Samanta},
title = {Smart Skill Hub: An AI-Driven Platform for Personalized Learning, Career Tracking, And Skill Development},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {2748-2769},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723935.pdf},
abstract = {The rapid growth of software technologies has created a significant challenge for students and early-career developers: acquiring technical skills is no longer sufficient without continuously identifying skill gaps, following structured learning paths, demonstrating practical coding ability, maintaining a professional portfolio, and preparing effective resumes. Existing learning platforms generally address individual aspects of this process, resulting in fragmented workflows in which learners use multiple applications for learning, coding practice, resume creation, GitHub analysis, portfolio development, and career guidance. This paper presents Smart Skill Hub (SSH), an integrated AI-powered skill development and career readiness platform designed to unify these activities within a single ecosystem. Smart Skill Hub combines personalized skill profiling, skill-gap analysis, AI-generated learning roadmaps, educational video discovery, coding assessment, GitHub intelligence, AI-assisted resume analysis and generation, portfolio management, analytics, and an AI-based Smart Mentor. The platform uses a modern web architecture consisting of a React and TypeScript frontend, Node.js and Express backend services, MongoDB for persistent data storage, Firebase Authentication for identity management, and centralized Large Language Model services for intelligent features. A secure Docker-based coding execution environment is used to execute programming submissions with resource and network restrictions. The platform maintains a canonical representation of learner skills and profile information so that information can be reused consistently across learning, assessment, resume, portfolio, and mentoring workflows. Resume processing supports document extraction, structured profile generation, interactive A4 editing, multiple ATS-oriented templates, AI-assisted improvement, job-specific analysis, and selectable-text PDF generation. GitHub Intelligence analyses authenticated GitHub activity to derive development insights, while analytics provide learners and administrators with measurable information about activity and system usage. Smart Skill Hub demonstrates an integrated approach to student skill development by connecting learning, assessment, professional representation, and AI-based guidance in a unified platform. The resulting system is designed to reduce fragmentation, improve personalization, and provide students with a continuous workflow from identifying skills and gaps to developing, demonstrating, and presenting those skills.},
keywords = {artificial intelligence; coding assessment; career development; personalized learning; skill gap analysis},
month = {October},
}