用AI统一标准评估论文，减少人为差异。
RubiSCoT: A Framework for AI-Supported Academic Assessment
- 结合大模型与思维链技术，自动分析论文全流程
- 支持多维度评分与结构化报告生成
- 适合高校导师与学术评审人员使用
论文评价是高等教育的基石，确保学术严谨性与规范性。传统方法虽有效，但耗时且受评审者主观差异影响。本文提出RubiSCoT——一个面向论文从开题到终稿全过程的AI辅助评估框架。该框架利用大语言模型、检索增强生成和结构化思维链提示等自然语言处理技术，实现一致、可扩展的评估。系统包含初步评估、多维度分析、内容提取、基于量规的打分与详细报告生成等功能。我们阐述了RubiSCoT的设计与实现，并探讨其在提升学术评估一致性、可扩展性与透明度方面的潜力。
原文摘要 · Abstract (English)
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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