mlsanityData to 14 Oct 2026

Enhancing classroom teaching with LLMs and RAG

arXiv 2411.04341 ↗First posted 7 Nov 2024cs.LGcited by 2

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Abstract (arXiv)

Large Language Models have become a valuable source of information for our daily inquiries. However, after training, its data source quickly becomes out-of-date, making RAG a useful tool for providing even more recent or pertinent data. In this work, we investigate how RAG pipelines, with the course materials serving as a data source, might help students in K-12 education. The initial research utilizes Reddit as a data source for up-to-date cybersecurity information. Chunk size is evaluated to determine the optimal amount of context needed to generate accurate answers. After running the experiment for different chunk sizes, answer correctness was evaluated using RAGAs with average answer correctness not exceeding 50 percent for any chunk size. This suggests that Reddit is not a good source to mine for data for questions about cybersecurity threats. The methodology was successful in evaluating the data source, which has implications for its use to evaluate educational resources for effectiveness.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 7 Nov 2024.

Builds on

3 of 8 references.

References · 3

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2024Simulating Classroom Education with LLM-Empowered Agentscited by 127
  3. 2023CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principlescited by 48

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Surveycited by 30
  2. 2025AITEE -- Agentic Tutor for Electrical Engineeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Faculty Perspectives on the Potential of RAG in Computer Science Higher Education
  2. 2025Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study
  3. 2025Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generation
  4. 2024Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems
  5. 2024KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?
  6. 2024AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs
  7. 2025Securing RAG: A Risk Assessment and Mitigation Framework
  8. 2025RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generation