# RubiSCoT: A Framework for AI-Supported Academic Assessment

arXiv: https://arxiv.org/abs/2510.17309 · HTML: https://mlsanity.com/paper/2510.17309/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing](https://mlsanity.com/paper/2102.12060/) (2021)
- [LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts](https://mlsanity.com/paper/2501.00274/) (2024)
- [Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference](https://mlsanity.com/paper/2310.03184/) (2023)
- [A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science](https://mlsanity.com/paper/2403.14565/) (2024)
- [Grade Like a Human: Rethinking Automated Assessment with Large Language Models](https://mlsanity.com/paper/2405.19694/) (2024)
- [Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring](https://mlsanity.com/paper/2407.18328/) (2024)
- [Using Large Language Models for Automated Grading of Student Writing about Science](https://mlsanity.com/paper/2412.18719/) (2024)
- [Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large](https://mlsanity.com/paper/2405.05444/) (2024)
- ["My Grade is Wrong!": A Contestable AI Framework for Interactive Feedback in Evaluating Student Essays](https://mlsanity.com/paper/2409.07453/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI-Based Thesis Assessment: An Empirical Study of Human Evaluation Priorities and Their Impact on Automated Assessment](https://mlsanity.com/paper/2608.00717/) (2026)

## Abstract (arXiv)

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.