用AI帮计算机专业学生匹配职业路径，准确率达94.71%
An Integrated System for Real-Time Student Assessment and Career Guidance Using Neural Networks in Computing Disciplines
- 结合AI模型与在线评估平台，实现个性化职业推荐
- 基于真实数据训练的MLP模型，预测准确率94.71%
- 适合高校学生、导师及职业规划者使用
计算机科学（CS）和软件工程（SWE）本科生常因学业表现、能力与兴趣不匹配而难以确定合适的职业方向。为此，本文提出一个基于人工智能的学生评估与职业预测系统，整合了职业指导专家（CGE）系统与基于网络的学生评估（WBSA）平台。在集成框架中，CGE利用多层感知机（MLP）模型，基于雪球抽样法收集的大学真实学术与课外数据进行训练，验证准确率达94.71%，可精准预测个性化职业路径；同时协助毕业生识别匹配的工作岗位、研究领域及深造机会。WBSA平台采用Node.js、Next.js与PostgreSQL构建，支持实时测评、个性化任务、导师互动与安全聊天功能。系统部署于安全云基础设施，保障性能与数据安全。预调查用于评估模型有效性，后调查则收集师生反馈以优化系统可用性与效果。
原文摘要 · Abstract (English)
Many undergraduate students in Computer Science (CS) and Software Engineering (SWE) struggle to identify suitable career paths, particularly when their academic performance, abilities, and interests do not fully align. To address this issue, this study proposes an AI-driven Student Assessment and Career Prediction System that integrates a Career Guidance Expert (CGE) system with a Web-Based Student Assessment (WBSA) platform. Within the integrated framework, CGE enhances personalized career recommendations using AI while also assisting students after graduation in identifying suitable jobs, research domains, and higher study opportunities aligned with their skills and interests. The WBSA platform further strengthens interaction between students and faculty through assessments, personalized tasks, mentorship activities, and a secure real-time chat application. The CGE system employs a Multilayer Perceptron (MLP) model trained on real-world academic and extracurricular data collected using the snowball sampling method from the students of universities, achieving a validation accuracy of 94.71% in predicting personalized career paths. A pre-survey was conducted across universities to evaluate the proposed model before deployment. The WBSA system was developed as a modern web application using technologies such as Node.js, Next.js, and PostgreSQL to ensure scalability, responsiveness, and secure data management. The overall system is supported by a secure cloud-based infrastructure, the platform provides reliable performance while assisting graduates to select suitable career path in IT sector. In addition, a post-survey involving both students and faculty was conducted to gather feedback and further improve the overall effectiveness and usability of the system.
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