Mitra Employability Portal: AI-Assisted Employability Training and Assessment Platform
Authors
Department of Electronics and Telecommunication Engineering Prof. Ram Meghe Institute of Technology and Research, Badnera (India)
Department of Electronics and Telecommunication Engineering Prof. Ram Meghe Institute of Technology and Research, Badnera (India)
Department of Electronics and Telecommunication Engineering Prof. Ram Meghe Institute of Technology and Research, Badnera (India)
Department of Electronics and Telecommunication Engineering Prof. Ram Meghe Institute of Technology and Research, Badnera (India)
Department of Electronics and Telecommunication Engineering Prof. Ram Meghe Institute of Technology and Research, Badnera (India)
Article Information
DOI: 10.51244/IJRSI.2026.1310000008
Subject Category: Engineering
Volume/Issue: 13/10 | Page No: 103-110
Publication Timeline
Submitted: 2026-10-11
Accepted: 2026-10-16
Published: 2026-10-28
Abstract
Getting from college into a job is more competitive than it used to be, so preparing for placements has become a real part of a student's journey. Beyond their degree subjects, students are expected to build aptitude, reasoning, communication, coding, resume-writing, interview and domain skills. The trouble is that the resources and tests for all of this sit on different platforms, which makes a step-by-step routine hard to follow. The MITRA Employability Portal is being built as one place that supports students through placement preparation. It offers videos, notes and study material, followed by topic-wise Multiple Choice Question (MCQ) tests and full assessments. The Training module covers quantitative aptitude, logical ability, verbal ability, reasoning ability, resume building, interview preparation, coding and domain-specific knowledge. The Assessment module offers both proctored and non-proctored tests. Proctored tests use camera monitoring, tab-switch detection and third-person detection; a student gets three warnings, after which the test is locked for 24 hours. Students can also upload their own question content, and the portal pulls the questions out of it so they can build a customized MCQ test. A Psychometric module adds one more way of assessing students. Administrators can manage study materials, question sets, scheduled tests, student performance and reports. Overall, the system aims to give students a structured, flexible and centralized environment for improving their placement readiness.
Keywords
Employability, Placement Preparation, Online Assessment, MCQ Test, Online Proctoring, Psychometric Assessment, Student Performance Analysis.
Downloads
References
1. “Global employability skills in the 21st century workplace: A semi-systematic literature review,” Heliyon, vol. 9, no. 11, 2023. [Google Scholar] [Crossref]
2. C. Succi and M. Canovi, “Soft skills to enhance graduate employability: Comparing students and employers’ perceptions,” Studies in Higher Education, vol. 45, no. 9, pp. 1834–1847, 2020. [Google Scholar] [Crossref]
3. I. Grosemans, L. Coertjens, and E. Kyndt, “Exploring learning and fit in the transition from higher education to the labour market: A systematic review,” Educational Research Review, vol. 21, pp. 67–84, 2017. [Google Scholar] [Crossref]
4. Z. Sokhanvar, K. Salehi, and F. Sokhanvar, “Advantages of authentic assessment for improving the learning experience and employability skills of higher education students: A systematic literature review,” Studies in Educational Evaluation, vol. 70, 2021. [Google Scholar] [Crossref]
5. V. González-Calatayud, P. Prendes-Espinosa, and R. Roig-Vila, “Artificial intelligence for student assessment: A systematic review,” Applied Sciences, vol. 11, no. 12, Art. no. 5467, 2021. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- An Adaptive Joint Filtering Approach to Wireless Relay Network for Transmission Rate Maximization
- IoT-Integrated Mercury Substance Detection System for Cosmetic Product Safety
- Design and Implementation of Solar PV-Based Railway Microgrid for Linke Hofmann Busch Coaches
- Cost Control Techniques on Civil Engineering Projects in Oyo State, Nigeria
- Strength and Predictive Modeling of Corn Cob Ash Blended Concrete Using Multi-Output Artificial Neural Network Approach