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REALCODEAI : An AI-Powered Real-Time Code Collaboration and Interview Platform

Author

Listed:
  • Hiware Meher Surendra
  • Harkal Swapnil Ratan
  • Ghallal Goraksh Bajirao
  • Khatal K

Abstract

REALCODEAI is an AI-powered real-time code collaboration and interview platform designed to transform the way technical assessments and collaborative programming sessions are conducted. Unlike traditional coding editors, this system allows multiple users to simultaneously edit and execute code in real time while integrating artificial intelligence for evaluation and feedback. The platform leverages technologies such as Liveblocks and Monaco Editor to enable smooth, synchronized collaboration and employs the OpenAI API to provide intelligent code suggestions, bug detection, and natural language-based assistance. It features an integrated Interview Mode powered by the Judge0 API, allowing interviewers to assign coding challenges, monitor execution, and automatically evaluate candidate per-formance. Developed using the MERN stack (MongoDB, Ex-press.js, React.js, and Node.js), the platform ensures scalabil-ity, cloud deployment readiness, and strong data security. The inclusion of AI-based plagiarism detection, Natural Language Processing (NLP) for communication analysis, and real-time monitoring further enhances fairness and efficiency in the inter-view process. The proposed system aims to reduce interviewer workload, ensure unbiased assessment, and provide an engaging and intelligent coding environment suitable for recruitment, education, and collaborative development.

Suggested Citation

  • Hiware Meher Surendra & Harkal Swapnil Ratan & Ghallal Goraksh Bajirao & Khatal K, 2025. "REALCODEAI : An AI-Powered Real-Time Code Collaboration and Interview Platform," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(6), pages 49-54, December.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i6:id:1764
    DOI: 10.32628/CSEIT2511618
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2511618
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