Scholarlink

<p>The integration of Artificial Intelligence (AI) into e-Business, particularly in human resources (HR), is revolutionizing traditional hiring processes by enhancing efficiency, objectivity, and personalization. This research presents the design and implementation of an AI copilot tailored for HR interviews, serving as an intelligent orchestrator rather than a mere automation tool. The proposed system enables HR managers to define job-specific criteria, adjust question difficulty, and schedule interviews with precision. Leveraging cutting-edge technologies, including locally deployed Large Language Models (LLMs) such as Qwen3 and sentiment analysis via pretrained deep learning models (bert-base-multilingual-uncased-sentiment), the copilot dynamically generates customized interview questions, assesses candidate responses, and computes real-time Key Performance Indicators (KPIs). A live dashboard provides interviewers with instant rankings and performance metrics, fostering seamless human–machine collaboration. The AI agent accurately ranked C01—for the Web Developer job position—as top candidate (Global Index: 3.94) by holistically scoring Hard, Soft, and Language Skills—and dynamically re-ranked candidates when skill weights were adjusted to match role priorities. We previously know that C01 (Web Expert) has the perfect background for this position without giving any information to the agent. Beyond accelerating the hiring process, this AI-driven approach enhances fairness, accuracy, and analytical depth in candidate evaluation. By harmonizing AI capabilities with human expertise, the system introduces an innovative, ethically grounded paradigm for talent acquisition—one that balances automation with strategic oversight to redefine modern recruitment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Next-gen HR interview process parametric AI copilot design based on pretrained LLM and sentiment analysis deep learning models

  • 期刊论文

  • 发表日期：

    2025年11月22日

查看全文
  • Smail Tigani

摘要

The integration of Artificial Intelligence (AI) into e-Business, particularly in human resources (HR), is revolutionizing traditional hiring processes by enhancing efficiency, objectivity, and personalization. This research presents the design and implementation of an AI copilot tailored for HR interviews, serving as an intelligent orchestrator rather than a mere automation tool. The proposed system enables HR managers to define job-specific criteria, adjust question difficulty, and schedule interviews with precision. Leveraging cutting-edge technologies, including locally deployed Large Language Models (LLMs) such as Qwen3 and sentiment analysis via pretrained deep learning models (bert-base-multilingual-uncased-sentiment), the copilot dynamically generates customized interview questions, assesses candidate responses, and computes real-time Key Performance Indicators (KPIs). A live dashboard provides interviewers with instant rankings and performance metrics, fostering seamless human–machine collaboration. The AI agent accurately ranked C01—for the Web Developer job position—as top candidate (Global Index: 3.94) by holistically scoring Hard, Soft, and Language Skills—and dynamically re-ranked candidates when skill weights were adjusted to match role priorities. We previously know that C01 (Web Expert) has the perfect background for this position without giving any information to the agent. Beyond accelerating the hiring process, this AI-driven approach enhances fairness, accuracy, and analytical depth in candidate evaluation. By harmonizing AI capabilities with human expertise, the system introduces an innovative, ethically grounded paradigm for talent acquisition—one that balances automation with strategic oversight to redefine modern recruitment.