# Large Language Models for Education: A Survey

arXiv: https://arxiv.org/abs/2405.13001 · HTML: https://mlsanity.com/paper/2405.13001/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 129 references that are in the map.
- First posted on arXiv 12 May 2024.

## Builds on (most cited first, 30 of 57)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://mlsanity.com/paper/2205.10770/) (2022)
- [UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training](https://mlsanity.com/paper/2002.12804/) (2020)
- [Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4](https://mlsanity.com/paper/2304.03439/) (2023)
- [Can large language models provide useful feedback on research papers? A large-scale empirical analysis](https://mlsanity.com/paper/2310.01783/) (2023)
- [ART: Automatic multi-step reasoning and tool-use for large language models](https://mlsanity.com/paper/2303.09014/) (2023)
- [Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2311.13165/) (2023)
- [Fine-tuning language models to find agreement among humans with diverse preferences](https://mlsanity.com/paper/2211.15006/) (2022)
- [ImageBind-LLM: Multi-modality Instruction Tuning](https://mlsanity.com/paper/2309.03905/) (2023)
- [Large Language Models for Robotics: A Survey](https://mlsanity.com/paper/2311.07226/) (2023)
- [Proof Artifact Co-training for Theorem Proving with Language Models](https://mlsanity.com/paper/2102.06203/) (2021)
- [Large Language Models in Law: A Survey](https://mlsanity.com/paper/2312.03718/) (2023)
- [A Survey of Knowledge Enhanced Pre-trained Language Models](https://mlsanity.com/paper/2211.05994/) (2022)
- [AI-Generated Content (AIGC): A Survey](https://mlsanity.com/paper/2304.06632/) (2023)
- [Pairwise Proximal Policy Optimization: Harnessing Relative Feedback for LLM Alignment](https://mlsanity.com/paper/2310.00212/) (2023)

## Built on by (most cited first, 30 of 34)

- [A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges](https://mlsanity.com/paper/2412.11936/) (2024)
- [Watermarking Techniques for Large Language Models: A Survey](https://mlsanity.com/paper/2409.00089/) (2024)
- [Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course](https://mlsanity.com/paper/2407.05216/) (2024)
- [Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents](https://mlsanity.com/paper/2505.19997/) (2025)
- [A Survey on LLM-based Conversational User Simulation](https://mlsanity.com/paper/2604.24977/) (2026)
- [From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Education](https://mlsanity.com/paper/2509.21972/) (2025)
- [DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data Cleaning as Anomaly Detection](https://mlsanity.com/paper/2502.11546/) (2025)
- [Assessing Personalized AI Mentoring with Large Language Models in the Computing Field](https://mlsanity.com/paper/2412.08430/) (2024)
- [The Lazy Student's Dream: ChatGPT Passing an Engineering Course on Its Own](https://mlsanity.com/paper/2503.05760/) (2025)
- [Simplifications are Absolutists: How Simplified Language Reduces Word Sense Awareness in LLM-Generated Definitions](https://mlsanity.com/paper/2507.11981/) (2025)
- [Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessment](https://mlsanity.com/paper/2508.08629/) (2025)
- [Human or LLM as Standardized Patients? A Comparative Study for Medical Education](https://mlsanity.com/paper/2511.14783/) (2025)
- [LLMs as Science Journalists: Supporting Early-stage Researchers in Communicating Their Science to the Public](https://mlsanity.com/paper/2601.05821/) (2026)
- [CyberMentor: AI Powered Learning Tool Platform to Address Diverse Student Needs in Cybersecurity Education](https://mlsanity.com/paper/2501.09709/) (2025)
- [Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities](https://mlsanity.com/paper/2509.08950/) (2025)
- [Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system](https://mlsanity.com/paper/2603.24940/) (2026)
- [AFRILANGTUTOR: Advancing Language Tutoring and Culture Education in Low-Resource Languages with Large Language Models](https://mlsanity.com/paper/2604.20996/) (2026)
- [A Dialogue-Based Framework for Correcting Multimodal Errors in AI-Assisted STEM Education](https://mlsanity.com/paper/2605.04131/) (2026)
- [Polar: A Benchmark for Evaluating Political Bias in LLMs](https://mlsanity.com/paper/2606.12922/) (2026)
- [MarkSec: Capability-Aware Evaluation of Adversarial Attacks Against LLM Watermarks](https://mlsanity.com/paper/2609.16681/) (2026)
- [SAGE: Steering Dialog Generation with Future-Aware State-Action Augmentation](https://mlsanity.com/paper/2503.03040/) (2025)
- [ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities](https://mlsanity.com/paper/2508.14377/) (2025)
- [Audited Reasoning Refinement: Fine-Tuning Language Models via LLM-Guided Step-Wise Evaluation and Correction](https://mlsanity.com/paper/2509.12476/) (2025)
- [Small Language Models for Curriculum-based Guidance](https://mlsanity.com/paper/2510.02347/) (2025)
- [FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writing](https://mlsanity.com/paper/2511.22883/) (2025)
- [From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education](https://mlsanity.com/paper/2512.20714/) (2025)
- [Human-AI Interaction Alignment: Designing, Evaluating, and Evolving Value-Centered AI For Reciprocal Human-AI Futures](https://mlsanity.com/paper/2512.21551/) (2025)
- [BitFlipScope: Scalable Fault Localization and Recovery for Bit-Flip Corruptions in LLMs](https://mlsanity.com/paper/2512.22174/) (2025)
- [BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation](https://mlsanity.com/paper/2602.23580/) (2026)
- [StreamWise: Serving Multi-Modal Generation in Real-Time at Scale](https://mlsanity.com/paper/2603.05800/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) has a profound impact on traditional education. In recent years, large language models (LLMs) have been increasingly used in various applications such as natural language processing, computer vision, speech recognition, and autonomous driving. LLMs have also been applied in many fields, including recommendation, finance, government, education, legal affairs, and finance. As powerful auxiliary tools, LLMs incorporate various technologies such as deep learning, pre-training, fine-tuning, and reinforcement learning. The use of LLMs for smart education (LLMEdu) has been a significant strategic direction for countries worldwide. While LLMs have shown great promise in improving teaching quality, changing education models, and modifying teacher roles, the technologies are still facing several challenges. In this paper, we conduct a systematic review of LLMEdu, focusing on current technologies, challenges, and future developments. We first summarize the current state of LLMEdu and then introduce the characteristics of LLMs and education, as well as the benefits of integrating LLMs into education. We also review the process of integrating LLMs into the education industry, as well as the introduction of related technologies. Finally, we discuss the challenges and problems faced by LLMEdu, as well as prospects for future optimization of LLMEdu.