The rapid growth of the Computer Science and Engineering field has resulted in a large number of academic resources, career paths, and skill development opportunities. Students often face challenges in navigating these options and making informed decisions regarding academics, projects, internships, research, and career planning. To address this, CSE Mentor is proposed as an ML-driven virtual mentoring system that delivers personalized guidance, recommendations, and resources to the students. Applying Machine Learning, Natural Language Processing, and predictive analytics, the platform analyzes students’ academic performance, skills, and interests to provide tailored suggestions for courses, project, internships, and career planning. The system includes modules for students, faculty mentors, and administrators, enabling continuous performance monitoring, targeted interventions, and real-time interaction through an AI-powered mentor interface. By offering 24/7 personalized guidance, CSE Mentor aims to enhance student engagement, reduce faculty workload, and improve career readiness, providing a scalable, intelligent, and data-driven framework for academic mentorship in the department.
Sadaf et al. (Wed,) studied this question.