TALENTIQ: AN AUTOMATED WEB-BASED INTERVIEW MANAGEMENT SYSTEM UTILIZING REAL-TIME PEER-TO-PEER COMMUNICATION AND SANDBOXED CODE EXECUTION ENVIRONMENTS

Authors

  • Om Anand Author
  • Anil Barik Author
  • Dr. Pratyush Ranjan Mohapatra Author

DOI:

https://doi.org/10.64751/

Abstract

In recent years, the demand for remote communication and online collaboration solutions has increased significantly, particularly in the fields of education, recruitment, and professional assessment. Organizations and educational institutions are rapidly shifting toward digital platforms to conduct interviews, technical assessments, and candidate evaluations efficiently. Traditional interview methods often involve several challenges such as geographical limitations, travel costs, scheduling conflicts, infrastructure dependency, and timeconsuming coordination processes. These issues become even more difficult when organizations need to conduct interviews with candidates located in different cities or countries. To overcome these limitations and support modern recruitment requirements, the TalentIQ Interview Management System has been developed as a smart and efficient web-based interview platform. The TalentIQ platform is designed to facilitate real-time online technical interviews directly through a web browser without requiring any external or third-party video conferencing software. The system enables interviewers and candidates to communicate seamlessly through live video calls, instant messaging, and collaborative coding environments within a single integrated platform. By providing all interview-related functionalities in one centralized system, the platform simplifies the overall interview process and improves user experience for both interviewers and candidates.

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Published

05-06-26

How to Cite

Om Anand, Anil Barik, & Dr. Pratyush Ranjan Mohapatra. (2026). TALENTIQ: AN AUTOMATED WEB-BASED INTERVIEW MANAGEMENT SYSTEM UTILIZING REAL-TIME PEER-TO-PEER COMMUNICATION AND SANDBOXED CODE EXECUTION ENVIRONMENTS. American Journal of AI Cyber Computing Management, 6(2(2), 185-193. https://doi.org/10.64751/