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Large Language Models in Education: Vision and Opportunities

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arxiv 2311.13160 v1 pith:GKXIB5EB submitted 2023-11-22 cs.AI

classification cs.AI
keywords educationresearchlargellmsmodelsapplicationchallengesdevelopment
verification ladder T0 review T1 audit T2 compute T3 formal

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With the rapid development of artificial intelligence technology, large language models (LLMs) have become a hot research topic. Education plays an important role in human social development and progress. Traditional education faces challenges such as individual student differences, insufficient allocation of teaching resources, and assessment of teaching effectiveness. Therefore, the applications of LLMs in the field of digital/smart education have broad prospects. The research on educational large models (EduLLMs) is constantly evolving, providing new methods and approaches to achieve personalized learning, intelligent tutoring, and educational assessment goals, thereby improving the quality of education and the learning experience. This article aims to investigate and summarize the application of LLMs in smart education. It first introduces the research background and motivation of LLMs and explains the essence of LLMs. It then discusses the relationship between digital education and EduLLMs and summarizes the current research status of educational large models. The main contributions are the systematic summary and vision of the research background, motivation, and application of large models for education (LLM4Edu). By reviewing existing research, this article provides guidance and insights for educators, researchers, and policy-makers to gain a deep understanding of the potential and challenges of LLM4Edu. It further provides guidance for further advancing the development and application of LLM4Edu, while still facing technical, ethical, and practical challenges requiring further research and exploration.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?

    cs.CL 2024-12 conditional novelty 6.0 of 10

    A new benchmark, KnowShiftQA, shows that retrieval-augmented LLMs drop 22 to 27 points in accuracy when textbook facts are hypothetically updated to conflict with the model's parametric knowledge.

  2. Analysis of Student-LLM Interaction in a Software Engineering Project

    cs.SE 2025-02 conditional novelty 4.0 of 10

    Analysis of student-LLM conversations and code in a 13-week software engineering course finds ChatGPT preferred over Copilot and conversational prompting yields lower-complexity code.

  3. VideoLLM Benchmarks and Evaluation: A Survey

    cs.CV 2025-05 unverdicted novelty 1.0 of 10

    A survey of VideoLLM benchmarks and evaluation protocols that organizes known datasets and metrics, with proposed future benchmark designs.

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