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.