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The contemporary workplace demonstrates increasingly diversified and specialized requirements for candidates’ professional competencies and qualifications. A substantial proportion of job seekers, particularly recent graduates, experience interview anxiety, demonstrate unfamiliarity with interview protocols and procedures, and exhibit insufficient understanding of position-specific competency requirements, thereby expressing considerable demand for interview skill enhancement. To address these challenges, an AI-powered virtual interviewer system has been conceptualized, designed, and implemented. The system’s core functionalities encompass specialized competency training for job seekers, real-time multimodal dialogue capabilities during simulated interviews, and comprehensive visualization of candidate competency profiles. From a technical perspective, the system operates as an intelligent interview training platform that leverages WebRTC technology for real-time audiovisual communication and employs a fine-tuned, domain-specific Large Language Model (LLM) optimized for interview scenarios. Developed utilizing Vue3 and Spring Boot within a decoupled frontend-backend architecture, this AI interviewer system facilitates candidate acclimatization to professional interview environments, enhances familiarity with discipline-specific assessment methodologies, and enables identification of competency deficiencies through comprehensive diagnostic analytics.

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The Design and Development of the AI Interviewer System

  • 会议论文

  • 发表日期：

    2026年

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  • Xinzhou Ye,
  • Zhehao Mou,
  • Haonan Jiang,
  • Yuefeng Cen,
  • Gang Cen

摘要

The contemporary workplace demonstrates increasingly diversified and specialized requirements for candidates’ professional competencies and qualifications. A substantial proportion of job seekers, particularly recent graduates, experience interview anxiety, demonstrate unfamiliarity with interview protocols and procedures, and exhibit insufficient understanding of position-specific competency requirements, thereby expressing considerable demand for interview skill enhancement. To address these challenges, an AI-powered virtual interviewer system has been conceptualized, designed, and implemented. The system’s core functionalities encompass specialized competency training for job seekers, real-time multimodal dialogue capabilities during simulated interviews, and comprehensive visualization of candidate competency profiles. From a technical perspective, the system operates as an intelligent interview training platform that leverages WebRTC technology for real-time audiovisual communication and employs a fine-tuned, domain-specific Large Language Model (LLM) optimized for interview scenarios. Developed utilizing Vue3 and Spring Boot within a decoupled frontend-backend architecture, this AI interviewer system facilitates candidate acclimatization to professional interview environments, enhances familiarity with discipline-specific assessment methodologies, and enables identification of competency deficiencies through comprehensive diagnostic analytics.