Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914064592011264 |
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| author | Sharma, Vaishnavi Thakur, Rakesh Sharma, Shashwat Panjanani, Kritika |
| author_facet | Sharma, Vaishnavi Thakur, Rakesh Sharma, Shashwat Panjanani, Kritika |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25258 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education Sharma, Vaishnavi Thakur, Rakesh Sharma, Shashwat Panjanani, Kritika Computers and Society Artificial Intelligence 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. |
| title | Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2509.25258 |