Authors:
S. Rubin Bose, J. Angelin Jeba, B. Judy Flavia, V. Vishwa Priya, Jouma Ali Al-Mohamad, A. Mohamed Fahadhu
Addresses:
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Computer Science and Information Technology, Vels Institute of Science, Technology and Advanced Studies, Chennai, Tamil Nadu, India. Department of Computer and Mobile Communications Engineering, Faculty of Information Engineering, Al-Shahbaa Private University, Aleppo, Aleppo Governorate, Syria. Department of Research and Development, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.
Abstract:
In today’s competitive academic landscape, aspirants face numerous challenges in preparing effectively for national and state-level competitive examinations due to the lack of personalised guidance, adaptive study plans, and real-time progress tracking. This research presents an AI-powered personalised exam preparation assistant that optimises learning outcomes by integrating Natural Language Processing (NLP), Machine Learning (ML), and Recommendation Algorithms. The system intelligently analyses a learner’s strengths, weaknesses, and progress patterns to curate customised learning paths, suggest relevant study materials, and generate adaptive quizzes based on performance. The proposed architecture leverages React.js for the front-end interface and a machine learning backend model trained using educational datasets to predict topic proficiency and recommend targeted content. Experimental evaluations indicate significant improvements in user engagement, predictive accuracy, and overall exam readiness. The assistant demonstrates the potential of artificial intelligence in transforming static learning platforms into interactive, data-driven educational companions, fostering efficiency, motivation, and confidence among competitive exam aspirants.
Keywords: Personalised Learning; Educational Technology; Exam Preparation; Machine Learning (ML); Adaptive Learning Systems; Recommendation Engine; Natural Language Processing (NLP).
Received on: 17/07/2025, Revised on: 06/09/2025, Accepted on: 27/09/2025, Published on: 05/03/2026
DOI: 10.64091/ATICR.2026.000308
AVE Trends in Intelligent Computing Research, 2026 Vol. 1 No. 1 , Pages: 24-34