28, May 2026

GradElevate: Design and Implementation of an Artificial Intelligence-Driven Integrated Career Guidance and Skill Development Platform for Technology Students

VOLUME-12; ISSUE-5; MAY-2026

Author(s): 1. Sakib Ayan Sheikh, 2. Madhavi Sadu, 3. Rohan Chaware, 4. Shravani Rakhade, 5. Mohak Talodhikar, 6.Shruti Thakre

Authors Affiliations:

1Student, Department of Computer Science and Engineering, Rajiv Gandhi College of Engg, Chandrapur, India,

2Professor, Department of Computer Science and Engineering, Rajiv Gandhi College of Engg, Chandrapur, India,

3Student, Department of Computer Science and Engineering, Rajiv Gandhi College of Engg, Chandrapur, India,

4Student, Department of Computer Science and Engineering, Rajiv Gandhi College of Engg, Chandrapur, India,

5Student, Department of Computer Science and Engineering, Rajiv Gandhi College of Engg, Chandrapur, India,

6Student, Department of Computer Science and Engineering, Rajiv Gandhi College of Engg, Chandrapur, India

DOIs:10.2015/IJIRMF/202605033     |     Paper ID: IJIRMF202605033


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Students today face a real disconnect between what they learn in the classroom and what employers actually look for, and most existing tools do little to bridge that gap. This paper presents GradElevate, an AI-powered career guidance platform built specifically to address that problem. The system brings together a weighted scoring engine for career recommendations, a hybrid AI-and-rule approach to skill sequencing that respects prerequisite dependencies, a PDF resume analyzer using the Gemini 2.5 Flash language model for ATS compatibility scoring, and a live skill progress tracker. The backend runs on Java Spring Boot with a React.js frontend styled through Tailwind CSS, a PostgreSQL database, and REST-based integration with Google's Gemini 2.5 Flash. Security is handled through Spring Security 6 with stateless JWT authentication, BCrypt password hashing, and email-based account verification. A round of functional testing across sixteen distinct scenarios achieved a 100% pass rate. This paper walks through the system architecture, key design decisions, implementation details, and evaluation outcomes, situating GradElevate within the broader landscape of intelligent tutoring and AI-assisted career advisory research

AI-powered recommendation, skill gap analysis, learning roadmap, resume analysis, ATS scoring, Spring Boot, React.js, Gemini AI

Madhavi Sadu,  Sakib Ayan Sheikh,  Rohan Chaware,  Shravani Rakhade,  Mohak Talodhikar,  Shruti Thakre (2026); GradElevate: Design and Implementation of an Artificial Intelligence-Driven Integrated Career Guidance and Skill Development Platform for Technology Students, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-12, Issue-5, Available on –   https://www.ijirmf.com/

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