International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Survey on AI Based Smart Assessment System |
|---|---|
| ABSTRACT | The automation of exam evaluation has become essential in modern education systems, where large-scale assessments require accuracy, speed, and fairness. Traditional manual and hardware-dependent OMR systems are slow, error-prone, and costly. This survey reviews ten recent research studies across AI-driven Optical Mark Recognition, computer vision-based image processing, deep learning for handwritten evaluation, and NLP-based semantic grading. The paper organizes these contributions around the five key modules of the proposed system: OMR sheet evaluation, handwritten question extraction via OCR, AI-powered feedback generation using LLMs, performance analytics, and role-based dashboards. A consolidated comparative table identifies methodologies, features addressed, and limitations of each reviewed work. The proposed AI-Based Smart Assessment Platform integrates all five areas into a unified, scalable, and intelligent evaluation ecosystem. |
| AUTHOR | JAYESH MASURE, PRATIKSHA NARSALE, ARCHANA RATHOD, SHRUTI SHINDE, S. S. NAVALE Department of Computer Engineering, Sinhgad Institute of Technology and Science, Savitribai Phule Pune University, Pune, India Guide, Sinhgad Institute of Technology and Science, Savitribai Phule Pune University, Pune, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406064 |
| pdf/64_A Survey on AI Based Smart Assessment System.pdf | |
| KEYWORDS | |
| References | [1] Y. Ju, X. Wang, and X. Chen, “Research on OMR Recognition Based on Convolutional Neural Network TensorFlow Platform,” 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), IEEE, pp. 688–691, 2019. [2] Md. A. Rahaman and H. Mahmud, “Automated Evaluation of Handwritten Answer Script Using Deep Learning Approach,” International Journal of Computer Science and Information Security (IJCSIS), vol. 20, no. 4, pp. 45–52, 2022. [3] Md. E. Alam, F. Akter, T. Alam, S. Farid, Md. I. Haque, and M. Arefin, “Approaching Computer Vision and Image Processing Technique for Optical Mark Recognition Sheet Scan and Verification,” IJERT, vol. 11, no. 2, pp. 1–6, 2022. [4] S. C. Goli, S. T. Gattu, V. V. G. Kalal, M. P. Raj, K. S. and S. K. Vittapu, “OMR Sheet Analysing Using Computer Vision,” 2025 IEEE SCEECS, IEEE, pp. 20–25, 2025. [5] International Journal of Research Publication and Reviews, “Analysis of OMR Sheet Using Advanced Intelligent Techniques,” vol. 4, no. 6, pp. 20–27, 2023. [6] R. G. Dongare, Prof. S. M. Patil, G. Y. Kakulte, M. M. Tamkar, and S. S. Tambekar, “Automated OMR Evaluation System with Integrated Feedback and Performance Analytics,” IJIRT, vol. 11, no. 11, pp. 4971–4975, April 2025. [7] J. Sanchez and A. Lopez, “Automated Multiple-Choice Test Checking System,” Journal of Information Systems Engineering and Management, vol. 7, no. 3, pp. 1–9, 2022. [8] D. Patel and K. Sharma, “NLP and OCR-Based Automatic Answer Script Evaluation System,” International Journal of Computer Applications, vol. 186, no. 42, pp. 1–7, 2024. [9] M. Kumar and S. Agarwal, “AI-Powered Exam Assessment System for Handwritten Answer Sheets,” IJISRT, vol. 10, no. 3, pp. 1–8, 2025. [10] R. Gupta and D. Jain, “Automatic Evaluation of Handwritten Descriptive Answers Using OCR and NLP,” ResearchGate Preprint, 2025. |
