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.

翻译：许多计算机科学与软件工程专业的本科生难以确定合适的职业发展路径，特别是当其学业表现、能力与兴趣无法完全对齐时。针对这一问题，本研究提出了一种基于人工智能的学生评估与职业预测系统，该集成了"职业指导专家"系统与"网络化学生评估"平台。在集成框架中，CGE利用AI增强个性化职业推荐，同时协助毕业学生根据自身技能与兴趣匹配适合的工作岗位、研究领域及深造机会。WBSA平台则通过评估测试、个性化任务、导师活动及安全实时聊天应用，进一步加强师生互动。CGE系统采用多层感知器模型，基于滚雪球抽样法从高校学生中采集的真实学术与课外活动数据进行训练，在个性化职业路径预测中达到94.71%的验证准确率。为评估所提模型，部署前在数所高校开展了预调查。WBSA系统采用Node.js、Next.js及PostgreSQL等现代Web技术开发，确保可扩展性、响应速度与数据安全管理。整个系统依托安全云基础设施，在提供可靠性能的同时协助毕业生选择IT领域的合适职业方向。此外，还针对学生与教师开展了后调查以收集反馈，进一步优化系统的整体效能与可用性。