Placementpro AI: A Community-Driven Intelligent Placement Preparation Platform Using Large Language Models

Abstract

Campus placement preparation has become increasingly challenging due to fragmented learning resources, rapidly evolving recruitment patterns, and the lack of personalized guidance. Students frequently rely on multiple independent platforms for aptitude practice, coding preparation, interview experiences, resume building, and company-specific information, resulting in an unstructured learning process. Existing placement platforms generally provide isolated functionalities and rarely integrate community knowledge with Artificial Intelligence for adaptive learning. This paper proposes PlacementPro AI, a community-driven intelligent placement preparation platform that leverages Large Language Models (LLMs) to deliver personalized placement guidance. The platform enables students to contribute interview experiences, coding questions, aptitude resources, and company-specific preparation materials while simultaneously benefiting from AI-generated study plans tailored to their skills, career goals, and target organizations. Google Gemini is utilized to analyze user preferences, community-generated content, and learning history to produce personalized recommendations and structured preparation roadmaps.

Introduction

PlacementPro AI is a community-driven, AI-powered placement preparation platform designed to help engineering students prepare more effectively for campus recruitment. Traditional placement preparation is often fragmented across multiple platforms such as LeetCode, HackerRank, GeeksforGeeks, InterviewBit, and PrepInsta, requiring students to switch between websites for coding practice, aptitude tests, interview experiences, resume guidance, and company-specific information. This fragmented approach reduces learning efficiency and makes it difficult to access relevant preparation materials.

To address these challenges, the proposed platform integrates Google Gemini, a Large Language Model (LLM), with collaborative learning to provide personalized placement guidance. Students can contribute interview experiences, coding questions, aptitude resources, and company-specific preparation materials, creating a continuously growing repository of authentic recruitment knowledge. The AI analyzes user profiles, learning history, and community-contributed content to generate personalized study plans, company-specific roadmaps, coding recommendations, interview strategies, and adaptive learning schedules.

The system is built using the MERN technology stack (MongoDB, Express.js, React.js, and Node.js), ensuring scalability, secure authentication, responsive performance, and efficient data management. Its key contributions include:

• A centralized, community-driven placement preparation platform.
• AI-powered personalized study plans using Google Gemini.
• Intelligent recommendation of company-specific learning resources.
• Adaptive preparation modules that evolve with changing recruitment trends.
• A secure and scalable web architecture supporting thousands of users.

The literature survey highlights that existing systems mainly focus on isolated functions such as coding practice, aptitude preparation, mock interviews, or resume analysis. Although AI-based educational systems offer personalized learning, they generally do not combine collaborative knowledge sharing, company-specific preparation, and AI-driven recommendations within a unified platform. This gap motivates the development of PlacementPro AI.

The platform architecture consists of five major layers:

1. User Layer – Students, alumni, placement coordinators, and administrators.
2. Application Layer – Authentication, dashboards, coding practice, interview sharing, resume management, analytics, and AI recommendations.
3. AI Layer – Google Gemini generates personalized study plans, learning paths, interview questions, resource rankings, and summaries.
4. Database Layer – MongoDB stores user profiles, company information, interview experiences, coding questions, recommendations, and analytics.
5. Administration Layer – Handles user verification, content moderation, resource approval, and platform management.

The workflow includes secure user registration, collection of interview experiences and learning resources, AI-based semantic analysis, personalized recommendation generation, and continuous improvement as more community knowledge is added.

Compared to existing placement platforms, PlacementPro AI offers several advantages:

• AI-generated personalized learning plans.
• Automatic company-specific preparation strategies.
• Centralized repository of validated interview experiences.
• Dynamic recommendation engine powered by Google Gemini.
• Integrated coding, aptitude, resume, and interview preparation.
• Real-time progress tracking and learning analytics.
• Unified ecosystem instead of multiple separate platforms.

The platform was developed using Agile methodology with technologies including React.js, Node.js, Express.js, MongoDB, Google Gemini API, JWT authentication, GitHub, Visual Studio Code, and Postman. Major modules include authentication, dashboards, company repository, coding practice, aptitude practice, interview experience sharing, AI recommendation engine, study plan generator, resume management, analytics dashboard, and an admin panel.

Experimental evaluation demonstrated that all modules functioned successfully. Google Gemini effectively generated personalized study plans and company-specific recommendations based on user profiles and community-generated content. The collaborative knowledge-sharing module enabled students to benefit from authentic recruitment experiences, while AI-based recommendations improved the relevance of learning resources compared to traditional static repositories.

Conclusion

Campus placement preparation requires students to efficiently manage diverse learning resources while adapting to continuously changing recruitment processes. Existing placement preparation platforms provide valuable educational content but generally focus on isolated functionalities such as coding practice, aptitude assessment, or interview preparation without offering comprehensive personalized guidance. This paper presented PlacementPro AI, a community-driven intelligent placement preparation platform that integrates collaborative learning with Large Language Models to provide adaptive and personalized placement preparation. The proposed system enables students to contribute interview experiences, coding questions, and company-specific preparation materials while simultaneously benefiting from AI-generated study plans, intelligent recommendations, and personalized learning pathways. The integration of Google Gemini significantly enhances the recommendation process by analyzing user profiles, learning history, and community-generated knowledge to produce company-specific preparation strategies. The MERN-based architecture ensures scalability, responsiveness, and secure data management, making the platform suitable for deployment in educational institutions and placement training programs. Experimental evaluation and simulation results demonstrate that PlacementPro AI successfully integrates Artificial Intelligence, collaborative knowledge sharing, and adaptive learning into a unified ecosystem. The proposed platform improves learning efficiency, reduces resource fragmentation, and supports students throughout the placement preparation process. In future work, the platform can be extended by incorporating real-time coding evaluation, AI-powered resume optimization, voice-based mock interviews, emotion-aware interview analysis, predictive placement analytics, multilingual support, and integration with Learning Management Systems (LMS). These enhancements have the potential to further improve personalization, student engagement, and placement success rates.

References

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Copyright

Copyright © 2026 Harshal Revanna, Om Rokade, Om Rathod, Prajwal Rasal, Sabana S. Peerzade. 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.