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arXiv · 2510.17309

RubiSCoT: A Framework for AI-Supported Academic Assessment

Thorsten Fröhlich·Tim Schlippe
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Abstract

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.

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Thorsten Fröhlich, Tim Schlippe. 2025-10-20. RubiSCoT: A Framework for AI-Supported Academic Assessment. https://arxiv.org/abs/2510.17309

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