Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education
Vaishnavi Sharma, Rakesh Thakur, Shashwat Sharma, Kritika Panjanani
TL;DR
The paper tackles authentic skill development in practical CS lab education by addressing cheating, poor records, and limited hands-on engagement. It proposes AsseslyAI, an AI-powered framework that assigns unique, keyword-driven lab problems to each student, supports AI-proctored viva, and employs gamified simulators for deeper engagement. A synthetic 10k-question ML/AI dataset underpins fine-tuning of a question generator and an AI-based answer assessor, achieving strong alignment with faculty scoring (Pearson $r=0.914$, Spearman $\rho=0.892$, Cohen's $\kappa=0.69$, RMSE $=3.37$, $R^2=0.861$) and diverse QA generation. The framework integrates end-to-end lab management, progress analytics, and a scalable path to produce genuinely skilled graduates with reduced plagiarism and improved conceptual understanding.
Abstract
Practical lab education in computer science often faces challenges such as plagiarism, lack of proper lab records, unstructured lab conduction, inadequate execution and assessment, limited practical learning, low student engagement, and absence of progress tracking for both students and faculties, resulting in graduates with insufficient hands-on skills. In this paper, we introduce AsseslyAI, which addresses these challenges through online lab allocation, a unique lab problem for each student, AI-proctored viva evaluations, and gamified simulators to enhance engagement and conceptual mastery. While existing platforms generate questions based on topics, our framework fine-tunes on a 10k+ question-answer dataset built from AI/ML lab questions to dynamically generate diverse, code-rich assessments. Validation metrics show high question-answer similarity, ensuring accurate answers and non-repetitive questions. By unifying dataset-driven question generation, adaptive difficulty, plagiarism resistance, and evaluation in a single pipeline, our framework advances beyond traditional automated grading tools and offers a scalable path to produce genuinely skilled graduates.