EduNet: Leveraging MERN Stack for Career Development through Professional and Student Interaction
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https://doi.org/10.48112/bms.v3i1.1226Abstract
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In today’s technologically advanced era, individuals face increasing challenges in navigating their academic and professional pathways. Despite possessing relevant competencies, many students struggle to achieve their career goals due to inadequate mentorship and limited access to professional guidance. To address this gap, we propose EduNet, a framework designed to facilitate meaningful connections between students and professionals across diverse fields of interest. Developed using MERN Stack technology, the framework leverages a dataset comprising inputs from teaching faculty, alumni, and undergraduate students of a Software Engineering department at a leading engineering university. The system has been evaluated with a broad cohort of both students and professionals, yielding encouraging outcomes illustrated through multiple graphical analyses. The results suggest that EduNet has the potential to serve as an effective platform for enhancing career development and academic support by fostering structured interactions between professionals and students. EduNet incorporates an AI-based student assessment module to evaluate students’ academic performance and skill development in an intelligent and automated manner. The system collects data such as grades, technical skills, interests, and feedback from students, faculty, and alumni. Machine learning techniques are used to analyse this data and identify students’ strengths and weaknesses. Clustering algorithms group students with similar skill sets, while supervised learning models help assess overall competency levels. The system also analyses student feedback to understand learning behaviour and career interests. Based on this assessment, EduNet provides meaningful insights that support better mentoring and academic guidance. By using AI based approach, EduNet ensures fair, consistent, and personalised assessment of students, reducing manual effort and improving decision-making in academic and career planning.
Keywords:
AI in Educational Sector, Career Development, Mentorship, MERN Stack Development, Student-Professional NetworkingReferences
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