Next-Gen Streamlining of Practical Examinations of Programming Courses with AI-Enhanced Evaluation System

Jayesh Sarwade, Afrin Shikalgar, Chaitanya Modak, Nikita Nagwade, Dnyaneshwari Devmankar, Ganesh Arbad

2025

Abstract

The final assessment of any course must reflect its goals and contents. An important goal of our foundational programming course is that the students learn a systematic approach for the development of computer programs. Since the programming process itself is a critical learning outcome, it becomes essential to incorporate it into assessments. However, traditional assessment methods e.g. oral exams, written tests, or multiple-choice questions are not well-suited for evaluating the process of programming effectively. Additionally, in educational institutes, teachers often use physical chits to distribute problem statements among students, then students perform them on computers in college labs, which is time-consuming. If essential compilers or development tools are missing on college computers, students resort to online compilers, increasing the risk of internet misuse for copying solutions. There is also a possibility of students using USB drives to share unauthorized code, compromising exam integrity. Therefore, there is a growing need for a fair and standardized evaluation process that accurately assesses students based on their coding abilities, eliminating the risk of cheating or unfair advantages. To address these challenges, this paper proposes a comprehensive software solution to modernize practical exams. The system automates problem statement allocation and integrates multi-language compilers through APIs like JDoodle and HackerRank. It ensures exam integrity by enforcing a full -screen mode using Browser Lock APIs, disabling copy-paste functionality, and adding watermarking for security. The solution includes AI-based chatbots for guidance, powered by Dialogflow, and AI-powered proctoring with OpenCV and TensorFlow, utilizing Convolutional Neural Networks for face detection. For automated and fair evaluation, machine learning models developed with scikit-learn are employed, using algorithms such as Decision Trees and Support Vector Machines. The platform is built on the MERN stack, comprising MongoDB, Express.js, React, and Node.js, to ensure a robust, scalable, and efficient examination process.

Paper Citation

in Harvard Style

Sarwade J., Shikalgar A., Modak C., Nagwade N., Devmankar D. and Arbad G. (2025). Next-Gen Streamlining of Practical Examinations of Programming Courses with AI-Enhanced Evaluation System. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 246-252. DOI: 10.5220/0013590100004664

in Bibtex Style

@conference{incoft25,
author={Jayesh Sarwade and Afrin Shikalgar and Chaitanya Modak and Nikita Nagwade and Dnyaneshwari Devmankar and Ganesh Arbad},
title={Next-Gen Streamlining of Practical Examinations of Programming Courses with AI-Enhanced Evaluation System},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={246-252},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013590100004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Next-Gen Streamlining of Practical Examinations of Programming Courses with AI-Enhanced Evaluation System
SN - 978-989-758-763-4
AU - Sarwade J.
AU - Shikalgar A.
AU - Modak C.
AU - Nagwade N.
AU - Devmankar D.
AU - Arbad G.
PY - 2025
SP - 246
EP - 252
DO - 10.5220/0013590100004664
PB - SciTePress