Campus Placements play a crucial role in a student\'s academic journey. Students must become proficient in developing effective resumes, acquiring competitive interview skills by working on their communication skills and understanding company-specific recruitment processes due to rising competition in campus placements. Many students still struggle in cracking the placements not because they are weak but because they often struggle in identifying the right preparation strategy and lack structured guidance. Though several resources available on the internet none provide detailed guidance or structured material for preparing for the placements. This paper presents CareerGenie, an AI-powered platform developed to support students in each and every stage of their placement journey by improving their placement readiness with smart features and personalized guidance. The system evaluates student resumes and provides effective suggestions and customized feedback which helps to tailor the resume according to the job description by providing detail insights on the ATS Score, It also comprises an interview simulation module that allows students to practice answering questions and improve their confidence while facing the interview and communication abilities. Furthermore, the platform generates personalized preparation road maps by analyzing the student’s current skills, interests, career goals and time left assisting them focus on the high priority topics and preparation strategies for targeted companies by providing previous year questions. Careergenie is implemented using modern web technologies such as React for the front-end and Node.js for backend services, along with AI-based APIs for providing intelligent perceptions. By integrating resume evaluation, personalized road-maps , Mock Interview Simulation and guided preparation within a single platform, CareerGenie aims to provide students with a ordered and effective approach to placement preparation.
Introduction
CareerGenie is an AI-powered platform designed to support students in their career placement preparation by integrating multiple functionalities into a single system. It provides intelligent resume analysis, evaluating content, structure, and skill representation, while suggesting improvements to enhance employability. The platform also includes a mock interview simulation module, allowing students to practice technical and behavioral interviews, receive semantic feedback, and improve communication, confidence, and clarity.
A key feature is personalized preparation roadmaps, which create tailored learning paths based on students’ skills, career goals, and target companies, covering areas such as data structures, algorithms, aptitude, core CS subjects, and project development. CareerGenie also offers company-specific guidance, tracking recruitment patterns and frequently asked questions to focus preparation effectively.
The system is built on a layered architecture combining a user-friendly frontend (React.js), backend services (Node.js/Express.js), AI processing using Groq LLM, and a database for storing company-specific materials. It integrates user registration, resume evaluation, mock interviews, personalized roadmaps, and progress monitoring into a single web-based interface, bridging the gap between academic learning and industry expectations while enhancing student confidence and employability.
Conclusion
This paper introduced a CareerGenie,an AI -powered placement preparation platform designed to help students in improving their skills for campus recruitment processes.The system consists of various preparation tools such as resume analysis,mock interview simulation,personalised roadmaps, company-specific preparation resources within a single web based platform.
By uniting modern web technologies with artificial intelligence capabilities,the platform provides automatic feedback and proper guidance that assists students notice skill gaps and improve their performance in campus interviews.The implementation and testing of the system shows that CareerGenie sucessfully performs its functions.
The resume analysis module provides students with useful advices for improving quality of the resume.The mock interview module helps students to practice various interview which gives an idea for students on the questions which are often asked by the interviewer and the system also evaluates the answer provided by student and gives students a proper feedback.The personalised roadmap generator helps students oraganize their preparation based on their current skills and career goals.The company-specific preparation module allows users to practice questions asked often by different organisations.
Overall,the platform aims to meet the gap between academic learning and industry expectations by offering an smart and proper guidance.It helps students enhancing their communication skills, confidence and helps shaping them in a correct way which is required for campus placements. Therefore,CareerGenie is a wonderful platform for increasing the chances of success in placement interview.
References
[1] Kumar, R., & Raheja, A. (2018). Placement Management System: Online placement tracking system. International Journal of Computer Applications, 182(32), 12–20.
[2] Sharma, P., Gupta, S., & Verma, R. (2019). AI Career Guidance System: Career recommendations via AI. Journal of Artificial Intelligence Research, 14(4), 55–67.
[3] Hemamou, M., Sifa, R., & Gehring, S. (2019). HireNet: Predicting interview success using attention-based multimodal analysis. Proceedings of the 2019 IEEE International Conference on Multimedia & Expo, 1023–1030.
[4] Kaur, P., & Kaur, R. (2020). Resume Screening using NLP: Automating resume filtering. International Journal of Natural Language Processing, 8(2), 45–58.
[5] Verma, A., Singh, T., & Joshi, N. (2020). Job Recommendation System Using Machine Learning. International Journal of Computer Applications, 175(5), 34–42.
[6] Das, S., Roy, P., & Sen, A. (2021). Smart Skill Assessment Platform: Online skill evaluation. Journal of Educational Technology Systems, 49(3), 210–225.
[7] Agrawal, R., Mehta, S., & Sharma, K. (2021). Multimodal Interview Analytics System: Behavioral feedback in interviews. Proceedings of the 2021 International Conference on Artificial Intelligence and Soft Computing, 112–120.
[8] Patil, P., Nair, S., & Kumar, D. (2022). Elevating Performance Through AI-Driven Mock Interviews. International Journal of Human-Computer Interaction, 38(6), 545–560.
[9] Sumi, S., Reddy, P., & Kumar, A. (2022). AI-Based Interview Simulations: Role-specific AI interviews. Journal of AI & Society, 37(4), 301–316.
[10] Ravi Kumar, D. V., Pavan, Y. B. S. S., Guna Vardhan, Y., Akanksha, G., & Ramani, J. P. B. (2023). AI-Driven Placement Preparation Platform: Combines mock interviews & job portal. IJRASET, 11(5), 78–90.
[11] Thawali, B., Singh, R., & Kumar, P. (2023). Emo Confident Interviewer: Evaluates emotion, confidence, and knowledge. International Journal of Advanced Computer Science & Applications, 14(8), 102–115.
[12] Nguyen, T., Le, Q., & Tran, H. (2024). SimInterview (LLM Based): LLM-based realistic AI interviews. Proceedings of the 2024 International Conference on AI in Education, 88–99.
[13] S. Patel and R. Mehta, “An Integrated Learning Management and Interview Preparation System,” International Journal of Computer Science and Communication Networks, vol. 12, no. 2, pp. 45–52, 2022.
[14] Shafiq, Sridevi, Avula, Basi Reddy, Ahamed Shaik Khaleel, “Local Neighbour Spider Monkey Optimization-Based Resource Allocation in Cloud Computing Environment” Journal of Circuits, Systems and Computers, Vol. 35, No. 08, 2550490 (2026).
[15] Sharma Pankaj, Jay Shankar, Shaheen, Ahamed Shaik Khaleel “An efficient cyber threat prediction using a novel artificial intelligence technique” Multimedia Tools and Applications, Vol. 83, pages 66757–66773, 2024.