MENTOR PLATFORM

Mentoring is an important form of personal, academic and professional development; however, access to suitable mentors is often fragmented, dependent on informal networks and difficult to coordinate at scale. This paper presents Mentor Connect, a proposed AI-powered web platform designed to connect mentors and mentees through personalized, goal-oriented matching. The proposed system collects user profiles, areas of expertise, interests and development goals, and uses this information to recommend potentially suitable mentor–mentee connections. Beyond matching, the platform is intended to support structured mentoring tracks, session organization, feedback collection, progress visibility and engagement through gamification. Its proposed technical architecture consists of a web frontend, API gateway, modular backend services, a database, an AI matching component and a security layer. React or Angular, Node.js or Python, PostgreSQL or MongoDB, and testing tools such as Jest, Mocha or Selenium are identified as technology options. The paper discusses the problem context, existing approaches, research gap, system requirements, proposed methodology, architecture, evaluation plan, applications and future enhancements. Since the underlying project report describes a proposal rather than a completed implementation, no experimental results or performance claims are presented. The paper concludes that the proposed platform offers a unified framework whose usefulness must be validated through implementation, systematic testing and user feedback.