Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{184668,
        author = {Pavithran P K and Dr. Abinaya and Aathivel M and Jeya Prakash J},
        title = {An Intelligent Mentoring and Career Guidance System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {2129-2134},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184668},
        abstract = {— Career guidance in higher education plays a vital role in shaping the academic and professional future of students [3], [7]. However, in most institutions, career counselling, mentorship, and administrative services still function separately, creating a gap between student needs and the rapidly changing demands of the job market [10], [16], [22]. At the same time, administrative activities such as attendance tracking often rely on outdated manual processes, which increase faculty workload and reduce efficiency [14], [23]. To address these issues, this research presents an integrated AI-powered educational support platform that combines career guidance, mentorship, and attendance management within a single system [5], [6], [18]. The platform has been developed using the MERN stack (MongoDB, Express.js, React.js, Node.js) along with Fast API for delivering AI services, ensuring scalability and smooth user interaction [20], [30]. The attendance module uses Retina Face for facial recognition with cosine similarity for verification, supported by OpenCV-based preprocessing to handle lighting and background variations. This approach achieved an accuracy of 98.7% during testing [14], [23]. For career guidance, a machine learning recommendation engine generates personalized career pathways that align with current industry needs, offering actionable feedback to over 85% of participating students [2], [5], [7]. Additionally, a mentor-matching algorithm based on profile similarity showed a 92% success rate, improving connections between students and industry professionals [6], [17], [19]. The study confirms that the proposed platform not only enhances professional preparedness but also reduces administrative inefficiencies [8], [11]. It demonstrates a scalable and replicable model for next-generation educational technology systems, supporting the principles of Industry 4.0, where adaptability, innovation, and lifelong learning are essential [12], [21], [29].},
        keywords = {AI-powered career guidance, educational technology platforms, intelligent mentorship matching, machine learning career recommendations, MERN stack applications, student attendance management, Industry 4.0 in education, EdTech innovation, AI-driven academic planning.},
        month = {September},
        }