# Large Language Models in Education: Vision and Opportunities

arXiv: https://arxiv.org/abs/2311.13160 · HTML: https://mlsanity.com/paper/2311.13160/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 81 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Scaling up GANs for Text-to-Image Synthesis](https://mlsanity.com/paper/2303.05511/) (2023)
- [Large Language Models Struggle to Learn Long-Tail Knowledge](https://mlsanity.com/paper/2211.08411/) (2022)
- [Rethinking Pre-training and Self-training](https://mlsanity.com/paper/2006.06882/) (2020)
- [Generating Training Data with Language Models: Towards Zero-Shot Language Understanding](https://mlsanity.com/paper/2202.04538/) (2022)
- [Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning](https://mlsanity.com/paper/2302.02662/) (2023)
- [Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do](https://mlsanity.com/paper/2103.11790/) (2021)
- [Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting](https://mlsanity.com/paper/2305.07004/) (2023)
- [Large Language Models for Robotics: A Survey](https://mlsanity.com/paper/2311.07226/) (2023)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems](https://mlsanity.com/paper/2204.03021/) (2022)
- [A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond](https://mlsanity.com/paper/2204.09269/) (2022)
- [AI-Generated Content (AIGC): A Survey](https://mlsanity.com/paper/2304.06632/) (2023)
- [A Survey on Model Compression and Acceleration for Pretrained Language Models](https://mlsanity.com/paper/2202.07105/) (2022)
- [Algorithmic Fairness in Education](https://mlsanity.com/paper/2007.05443/) (2020)
- [Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey](https://mlsanity.com/paper/2201.12438/) (2022)
- [The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative](https://mlsanity.com/paper/2210.13181/) (2022)
- [A Survey of Utility-Oriented Pattern Mining](https://mlsanity.com/paper/1805.10511/) (2018)
- [Model-as-a-Service (MaaS): A Survey](https://mlsanity.com/paper/2311.05804/) (2023)
- [A Survey of Parallel Sequential Pattern Mining](https://mlsanity.com/paper/1805.10515/) (2018)
- [Web 3.0: The Future of Internet](https://mlsanity.com/paper/2304.06032/) (2023)
- [HUOPM: High Utility Occupancy Pattern Mining](https://mlsanity.com/paper/1812.10926/) (2018)
- [Privacy Preserving Utility Mining: A Survey](https://mlsanity.com/paper/1811.07389/) (2018)
- [Metaverse Security and Privacy: An Overview](https://mlsanity.com/paper/2211.14948/) (2022)
- [Metaverse: Survey, Applications, Security, and Opportunities](https://mlsanity.com/paper/2210.07990/) (2022)
- [Internet of Behaviors: A Survey](https://mlsanity.com/paper/2211.15588/) (2022)
- [Big Data Meets Metaverse: A Survey](https://mlsanity.com/paper/2210.16282/) (2022)
- [The Human-Centric Metaverse: A Survey](https://mlsanity.com/paper/2303.11123/) (2023)

## Built on by (most cited first, 30 of 54)

- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [Video Understanding with Large Language Models: A Survey](https://mlsanity.com/paper/2312.17432/) (2023)
- [Optimization-based Prompt Injection Attack to LLM-as-a-Judge](https://mlsanity.com/paper/2403.17710/) (2024)
- [Large Language Models in Law: A Survey](https://mlsanity.com/paper/2312.03718/) (2023)
- [Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy](https://mlsanity.com/paper/2410.09102/) (2024)
- [Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors](https://mlsanity.com/paper/2412.09416/) (2024)
- [Large Language Models for Medicine: A Survey](https://mlsanity.com/paper/2405.13055/) (2024)
- [Large Language Models for Education: A Survey](https://mlsanity.com/paper/2405.13001/) (2024)
- [LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model](https://mlsanity.com/paper/2406.04614/) (2024)
- [Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment](https://mlsanity.com/paper/2507.18631/) (2025)
- [Watermarking Techniques for Large Language Models: A Survey](https://mlsanity.com/paper/2409.00089/) (2024)
- [From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility](https://mlsanity.com/paper/2402.16142/) (2024)
- [EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios](https://mlsanity.com/paper/2505.16160/) (2025)
- [MTalk-Bench: Evaluating Speech-to-Speech Models in Multi-Turn Dialogues via Arena-style and Rubrics Protocols](https://mlsanity.com/paper/2508.18240/) (2025)
- [Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges](https://mlsanity.com/paper/2401.08664/) (2023)
- [Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning](https://mlsanity.com/paper/2406.10834/) (2024)
- [FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination](https://mlsanity.com/paper/2406.10261/) (2024)
- [Evaluating the Sensitivity of LLMs to Prior Context](https://mlsanity.com/paper/2506.00069/) (2025)
- [Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models](https://mlsanity.com/paper/2408.08210/) (2024)
- [LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM](https://mlsanity.com/paper/2502.06572/) (2025)
- [Intent Matters: Enhancing AI Tutoring with Fine-Grained Pedagogical Intent Annotation](https://mlsanity.com/paper/2506.07626/) (2025)
- [Measuring Bias or Measuring the Task: Understanding the Brittle Nature of LLM Gender Biases](https://mlsanity.com/paper/2509.04373/) (2025)
- [A Survey of Inductive Reasoning for Large Language Models](https://mlsanity.com/paper/2510.10182/) (2025)
- [StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions](https://mlsanity.com/paper/2407.12423/) (2024)
- [OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence](https://mlsanity.com/paper/2503.16326/) (2025)
- [Is persona enough for personality? Using ChatGPT to reconstruct an agent's latent personality from simple descriptions](https://mlsanity.com/paper/2406.12216/) (2024)
- [Time Series Analysis for Education: Methods, Applications, and Future Directions](https://mlsanity.com/paper/2408.13960/) (2024)
- [D'ej`a Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse](https://mlsanity.com/paper/2506.14107/) (2025)
- [Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning](https://mlsanity.com/paper/2402.11432/) (2024)
- [Investigating Bias: A Multilingual Pipeline for Generating, Solving, and Evaluating Math Problems with LLMs](https://mlsanity.com/paper/2509.17701/) (2025)

## Abstract (arXiv)

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.