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A novel framework for educational Q&A: Leveraging RAG and Code Interpreters for knowledge retrieval and logical

Jin Lu1, Ji Li2

  • 1Guangdong Key Laboratory of Big Data Intelligence for Vocational Education, Shenzhen Polytechnic University, Shenzhen, Guangdong, China.

Plos One
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances educational question-answering systems using Retrieval-Augmented Generation (RAG) and Large Language Model (LLM) Code Interpreters, improving accuracy by 10-15%. The novel approach tackles knowledge gaps and complex computations for better learning experiences.

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Natural Language Processing

Background:

  • Traditional educational Q&A systems struggle with knowledge updates, reasoning accuracy, and complex computations.
  • Limitations are pronounced in domains needing multi-step reasoning or real-time, specific knowledge.
  • Large Language Models (LLMs) often face 'hallucination' and lack precise computational abilities.

Purpose of the Study:

  • To develop an enhanced educational Q&A system by integrating Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters.
  • To address challenges in knowledge currency, reasoning accuracy, and computational task handling.
  • To improve the accuracy and reliability of educational Q&A systems.

Main Methods:

  • Implemented a system combining RAG for dynamic knowledge retrieval with LLM Code Interpreters for logical reasoning and Python code execution.
  • Evaluated the system on five diverse educational datasets: AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA.
  • Compared performance against vanilla LLMs, focusing on accuracy in mathematical problems and complex queries.

Main Results:

  • The proposed RAG and Code Interpreter integration achieved an average accuracy improvement of 10-15 percentage points over vanilla LLMs.
  • GPT-4o and Gemini-pro-1.5 demonstrated superior performance, particularly in scientific reasoning and multi-step computations.
  • The system effectively mitigated LLM 'hallucination' by leveraging external, up-to-date knowledge sources.

Conclusions:

  • Integrating RAG and Code Interpreters offers a promising pathway for more accurate, transparent, and personalized educational Q&A systems.
  • The approach significantly enhances the learning experience by improving problem-solving capabilities in complex domains.
  • Future research should address remaining challenges like retrieval failures, code execution errors, and multi-modal reasoning limitations.