student_alumniDataset

Building meaningful connections between students and alumni is critical for enhancing students’ professional growth, career advice, and networking. Despite these benefits, traditional platforms often lack personalization and scalability, limiting their ability to meet diverse student needs. This paper presents an AI-driven approach to revolutionize student-alumni interactions wih career guidance by leveraging advanced recommendation systems. Using pre-trained SentenceTransformer models, we transform textual skill data into embeddings, enabling precise matches between students, alumni, and job opportunities based on cosine similarity. A multi-agent reinforcement learning (MARL) framework further personalizes recommendations, dynamically adapting to students' evolving profiles and career goals. Unlike static systems, our model continuously learns from feedback, refining mentorship and job connections for greater relevance. Additionally, the system addresses inclusivity by identifying mentors with shared experiences, ensuring equitable access to professional guidance. The proposed solution is scalable and integrates seamlessly with existing university platforms. Experimental results highlight the framework’s effectiveness in creating meaningful, personalized, and adaptive student-alumni relationships. This work underscores the potential of AI to foster long-term engagement, improve career outcomes, and strengthen the educational ecosystem.

构建学生与校友之间的有意义联系对于提升学生的职业成长、职业建议和网络构建至关重要。尽管这些益处显著，传统的平台往往缺乏个性化和可扩展性，限制了其满足多样化学生需求的能力。本文提出一种基于人工智能驱动的创新方法，旨在通过利用高级推荐系统，彻底革新学生与校友之间的职业指导互动。通过使用预训练的SentenceTransformer模型，我们将文本技能数据转换为嵌入向量，从而基于余弦相似度实现学生、校友与就业机会之间的精确匹配。多智能体强化学习（MARL）框架进一步实现个性化推荐，动态适应学生不断变化的个人档案和职业目标。与静态系统不同，我们的模型能够持续地从反馈中学习，优化导师指导和职业连接，以增强其相关性。此外，该系统通过识别具有相似经验的导师，确保了专业指导的公平获取。所提出的解决方案具有可扩展性，并能与现有大学平台无缝集成。实验结果突显了该框架在构建有意义、个性化且适应性强的学生-校友关系方面的有效性。本研究强调了人工智能在培养长期参与度、改善职业成果以及加强教育生态系统方面的潜力。