RubiSCoT: A Framework for AI-Supported Academic Assessment

TL;DR

RubiSCoT is an AI-supported framework that leverages advanced NLP techniques to make academic thesis evaluation more consistent, scalable, and transparent throughout the entire assessment process.

Contribution

The paper introduces RubiSCoT, a novel AI framework integrating NLP methods to improve the efficiency and consistency of thesis evaluations.

Findings

RubiSCoT enables scalable thesis assessments.

The framework improves evaluation consistency.

It provides detailed, transparent reports.

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.