PrepVision: AI Career Path Advisor

Abstract

PrepVision: AI Career Path Advisor is an intelligent, all-in-one career and placement preparation platform that leverages Artificial Intelligence (AI) to guide students and professionals toward their ideal career paths. It utilizes Natural Language Processing (NLP) and recommender systems to personalize learning journeys, suggest relevant courses from free platforms such as YouTube and Coursera, and help users bridge existing skill gaps. PrepVision incorporates an advanced Resume Analyzer that assesses resumes for Applicant Tracking System (ATS) compatibility, identifying strengths and pinpointing missing skills required for specific job roles. It provides detailed feedback to help users enhance the relevance and visibility of their resumes in automated screening systems. Furthermore, the integrated Mock Interview Generator creates realistic, domain-specific interview simulations for roles such as Software Engineer, Data Analyst, and Product Manager. It generates AI-driven questions and delivers instant, insightful feedback on user responses, enabling effective self-evaluation and continuous improvement. By combining advanced AI capabilities with real-world placement requirements, PrepVision offers a comprehensive, smart, and scalable solution for career guidance, skill enhancement, and professional growth.

Introduction

In today’s dynamic job market, traditional career counseling often fails to provide personalized guidance, leaving students and professionals struggling to navigate career paths. PrepVision.ai – AI-Driven Career Path Advisor addresses this gap by using Artificial Intelligence (AI) and Natural Language Processing (NLP) to offer adaptive, data-driven career guidance. The platform analyzes user profiles, resumes, and interests to identify strengths, skill gaps, and suitable career paths. It integrates ATS-based resume evaluation, AI-powered mock interviews, curated learning resources, and real-time labor market insights to enhance employability.

Literature Review:
Previous AI-driven career guidance systems have focused on isolated functions such as career recommendations, resume parsing, or interview preparation. While these systems improve employability, they often lack personalization, continuous skill tracking, multilingual support, and adaptability to market trends. Studies highlight the need for unified platforms that combine technical intelligence with human-centered design.

Research Gap and Contribution:
PrepVision.ai overcomes existing limitations by integrating resume analysis, career prediction, skill gap identification, mock interviews, and adaptive learning recommendations into a single platform. It supports inclusivity through multilingual interfaces and continuously adapts to changing job market trends.

Objectives:

• Suggest career paths based on user interests, education, and market trends.

• Recommend high-quality, free learning resources for skill development.

• Analyze resumes for ATS compatibility, skill gaps, and actionable improvement tips.

• Offer AI-powered mock interviews tailored to job roles.

Methodology:
The system features a modular architecture:

1. User Interface: Dashboard for profile creation and resume upload.

2. Resume Analyzer: Extracts skills, experience, and education via NLP.

3. Skill Gap Analyzer: Identifies missing competencies.

4. Recommendation Engine: Suggests careers, learning resources, and jobs using AI and real-time market data.

5. Mock Interview Module: Simulates interviews and provides feedback.

6. Database: Stores profiles, analytics, and reports for continuous tracking.

The AI-driven framework combines NLP, machine learning, ATS evaluation, and cloud data management, ensuring a seamless workflow from profile input to actionable career guidance.

System Architecture:
The architecture includes:

• Student Module: User input of personal and academic data.

• AI Career Path Advisor (CPU): Coordinates submodules—Resume Analyzer, Skill Gap Analyzer, Recommendation Engine, and Report Generator.

• Database: Secure storage for all user data and analytical results.

• Data Flow: User data → AI processing → career and learning recommendations → reports → continuous updates.

Conclusion

The AI Career Path Advisor provides a personalized and intelligent solution for career planning and placement preparation. By integrating machine learning, natural language processing, and data analytics, it analyzes student profiles, identifies skill gaps, recommends learning resources, and conducts AI-driven mock interviews. The system overcomes the limitations of traditional counseling through real-time, data-driven insights that enhance employability and support informed career decisions.

References

[1] Y. Manap, B. Amirgaliyev, A. Biloshchytskyi, Zh. Sarsenova, and Zh. Baishemirov, “Application of Artificial Intelligence in Career Counselling,” IEEE/ACM Trans. Network., vol. 85, no. 1, pp. 35–43, 2025. [2] S. H. Faruque, S. A. Khushbu, and S. Akter, “Unlocking Futures: A Natural Language Driven Career Prediction System,” arXiv preprint, 2021. [3] S. Gunwant, J. Pande, and R. K. Bisht, “A Systematic Study of Career Guidance Expert Systems for Students,” Int. J. Learning for Development, vol. 9, no. 3, pp. 210–225, 2022. [4] P. C. Siswipraptini, H. L. H. S. Warnars, A. Ramadhan, and W. Budiharto, “Personalized Career-Path Recommendation Model (CPRM),” IEEE Access, vol. 12, pp. 18210–18225, 2024. [5] E. Y. Khader, H. Khader, S. N. Alotaiby, and S. Ayad, “Smart Academic Advisors (AI Chatbots),” Proc. IEEE WiDS-PSU Conf., 2025. [6] S. Bankins, S. Jooss, S. L. D. Restubog, M. Marrone, A. C. Ocampo, and M. Shoss, “Navigating Career Stages in the Age of AI,” J. Vocational Behavior, vol. 145, 2024. [7] M. Meenakshi, A. M. Patil, A. M. Patil, H. M. Agnihotri, and N. G. Nayak, “HIREBRIDGE – An AI Driven Career Hub,” Int. J. Smart & Resilient Eng. & Mgmt., 2025. [8] P. Shenoy, R. Shiroorkar, and A. Samani, “Career Link: AI for Resumes, Interviews & Advice,” Int. J. Res. in Adv. Sci., Eng. & Tech., 2025. [9] F. Trujillo, M. Pozo, and G. Suntaxi, “AI in Education: ML Approaches in Career Prediction,” J. of Technology & Science Education, vol. 15, no. 4, pp. 512–528, 2025.

Copyright

Copyright © 2025 Anurag Wadkar, Vitthal Deshmukh, Sudarshan Magar, Tushar Thengade, Varsha Gosavi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.