基于多模态大模型的智能面试系统设计与实现OA
Design and Implementation of an Intelligent Interview System for Multimodal Large Language Models
针对企业招聘人工成本高、评估主观性强及求职者缺乏沉浸式模拟训练平台的问题,设计并实现了基于多模态大语言模型的智能面试系统.采用"Java 业务中台与 Python 微服务解耦"双后端架构,集成 Qwen、Gemini 等大模型,构建了编程实战沙盒、多角色语音面试与多模态综合评估三大核心模块.测试结果表明,系统运行稳定,编程沙盒可安全完成代码执行与质量分析,语音面试能模拟多种职场风格对话,最终可生成融合多维度信息的结构化评估报告.该系统为全流程智能化人才评估与培训提供了可行方案,对推动AI赋能面试的工程化应用具有参考价值.
Aiming at the problems of high labor costs,strong subjectivity in evaluation during enterprise recruitment,and job seekers'lack of immersive simulation training platforms,this paper designs and implements an intelligent interview system based on multimodal Large Language Model.Adopting a dual-backend architecture featuring decoupling between Java business middle platform and Python microservices,the system integrates large models such as Qwen and Gemini,and constructs three core functional modules:a practical programming sandbox,multi-role voice interview,and multimodal comprehensive evaluation.Test results show that the system runs stably:the programming sandbox can execute code safely and conduct code quality analysis;the voice interview module can simulate conversations in various workplace styles;finally,the system can generate structured evaluation reports integrating multi-dimensional information.This system provides a feasible solution for full-process intelligent talent assessment and training,and has reference value for promoting the engineering application of AI-enabled interviews.
冯波;何浪;戴嘉晟
天津电子信息职业技术学院,天津 300350天津电子信息职业技术学院,天津 300350天津电子信息职业技术学院,天津 300350
信息技术与安全科学
大模型智能面试系统人工智能
Large Language Modelintelligent interview systemArtificial Intelligence
《现代信息科技》 2026 (12)
81-86,90,7
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