RubiSCoT: A Framework for AI-Supported Academic Assessment
This addresses inefficiencies in academic assessment for educators and institutions, though it appears incremental as it builds on existing AI methods.
The paper tackles the problem of time-consuming and variable thesis evaluation in higher education by introducing RubiSCoT, an AI-supported framework that uses NLP techniques to provide consistent and scalable assessment from proposal to final submission.
The evaluation of academic theses is a cornerstone of higher education, ensuring rigor and integrity. Traditional methods, though effective, are time-consuming and subject to evaluator variability. This paper presents RubiSCoT, an AI-supported framework designed to enhance thesis evaluation from proposal to final submission. Using advanced natural language processing techniques, including large language models, retrieval-augmented generation, and structured chain-of-thought prompting, RubiSCoT offers a consistent, scalable solution. The framework includes preliminary assessments, multidimensional assessments, content extraction, rubric-based scoring, and detailed reporting. We present the design and implementation of RubiSCoT, discussing its potential to optimize academic assessment processes through consistent, scalable, and transparent evaluation.