Title: AsseslyAI: AI–Powered Assessment Framework for Skill-Oriented Engineering Lab Education
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Ai in education, Dynamic Question Generation, Gamified Simulators, Lab Record Management

Abstract: Practical lab education in computer science often faces challenges like plagiarism, lack of proper lab records, inadequate execution and assessment, limited student engagement, and absence of progress tracking for both students and faculties causing graduates with insufficient hands-on skills. In this paper, we introduce **AsseslyAI**, it tackles these challenges through online lab allocation, unique lab problem for each student, integrates AI-proctored viva evaluations, and gamified simulators to enhance engagement and conceptual mastery. While existing platform are generating questions on the basis of topics. Our framework fine-tunes on a **10k+ Question-Answer dataset** built from AIML 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 assessment in a single pipeline, our framework advances beyond traditional automated grading tools and offers scalable path to produce genuinely skilled graduates.

TL;DR: AsseslyAI leverages AI-driven personalized assessments, proctored vivas, and gamified simulators to enhance hands-on learning and skill development in computer science education.