Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education
This addresses the problem of insufficient hands-on skills for computer science graduates, offering an incremental improvement over existing automated grading tools.
The paper tackles challenges in computer science lab education, such as plagiarism and low engagement, by introducing AsseslyAI, a framework that uses AI to generate diverse, code-rich assessments and proctor evaluations, resulting in high question-answer similarity and scalable skill development.
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.