# Generative AI for Education (GAIED): Advances, Opportunities, and Challenges

arXiv: https://arxiv.org/abs/2402.01580 · HTML: https://mlsanity.com/paper/2402.01580/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 63 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 29 of 29)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues](https://mlsanity.com/paper/2205.07540/) (2022)
- [Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning](https://mlsanity.com/paper/2202.05402/) (2022)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses](https://mlsanity.com/paper/2310.19208/) (2023)
- [Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors](https://mlsanity.com/paper/2306.17156/) (2023)
- [Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference](https://mlsanity.com/paper/2310.03184/) (2023)
- [Merging Generated and Retrieved Knowledge for Open-Domain QA](https://mlsanity.com/paper/2310.14393/) (2023)
- [Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models](https://mlsanity.com/paper/2302.04662/) (2023)
- [Prompt Problems: A New Programming Exercise for the Generative AI Era](https://mlsanity.com/paper/2311.05943/) (2023)
- [Variational Item Response Theory: Fast, Accurate, and Expressive](https://mlsanity.com/paper/2002.00276/) (2020)
- [Repairing Bugs in Python Assignments Using Large Language Models](https://mlsanity.com/paper/2209.14876/) (2022)
- [Exploring the Potential of Large Language Models to Generate Formative Programming Feedback](https://mlsanity.com/paper/2309.00029/) (2023)
- [Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning](https://mlsanity.com/paper/2308.03234/) (2023)
- [Next-Step Hint Generation for Introductory Programming Using Large Language Models](https://mlsanity.com/paper/2312.10055/) (2023)
- [Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs](https://mlsanity.com/paper/2205.00355/) (2022)
- [Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference](https://mlsanity.com/paper/1809.01357/) (2018)
- [Neural Task Synthesis for Visual Programming](https://mlsanity.com/paper/2305.18342/) (2023)
- [Code Soliloquies for Accurate Calculations in Large Language Models](https://mlsanity.com/paper/2309.12161/) (2023)
- [Efficient Argument Structure Extraction with Transfer Learning and Active Learning](https://mlsanity.com/paper/2204.00707/) (2022)
- [Generative AI in the Classroom: Can Students Remain Active Learners?](https://mlsanity.com/paper/2310.03192/) (2023)
- [AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising](https://mlsanity.com/paper/2311.07700/) (2023)
- [Are LLMs Useful in the Poorest Schools? TheTeacher.AI in Sierra Leone](https://mlsanity.com/paper/2310.02982/) (2023)
- [EHRTutor: Enhancing Patient Understanding of Discharge Instructions](https://mlsanity.com/paper/2310.19212/) (2023)
- [Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models](https://mlsanity.com/paper/2310.20105/) (2023)
- [Play to Grade: Testing Coding Games as Classifying Markov Decision Process](https://mlsanity.com/paper/2110.14615/) (2021)
- [What Skills Do You Need When Developing Software Using ChatGPT? (Discussion Paper)](https://mlsanity.com/paper/2310.05998/) (2023)
- [The Behavior of Large Language Models When Prompted to Generate Code Explanations](https://mlsanity.com/paper/2311.01490/) (2023)

## Built on by (most cited first, 13 of 13)

- [Foundation Models for Education: Promises and Prospects](https://mlsanity.com/paper/2405.10959/) (2024)
- [Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation](https://mlsanity.com/paper/2406.05053/) (2024)
- [Towards Generalizable Agents in Text-Based Educational Environments: A Study of Integrating RL with LLMs](https://mlsanity.com/paper/2404.18978/) (2024)
- [Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment](https://mlsanity.com/paper/2406.11334/) (2024)
- [When LLMs Learn to be Students: The SOEI Framework for Modeling and Evaluating Virtual Student Agents in Educational Interaction](https://mlsanity.com/paper/2410.15701/) (2024)
- [Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming](https://mlsanity.com/paper/2406.09891/) (2024)
- [Synthesizing High-Quality Programming Tasks with LLM-based Expert and Student Agents](https://mlsanity.com/paper/2504.07655/) (2025)
- [TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models](https://mlsanity.com/paper/2502.10411/) (2025)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [Enhancing Diversity of LLM-Generated Educational Tasks](https://mlsanity.com/paper/2512.23601/) (2025)
- [ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMs](https://mlsanity.com/paper/2505.15410/) (2025)
- [Rethinking AI Evaluation in Education: The TEACH-AI Framework and Benchmark for Generative AI Assistants](https://mlsanity.com/paper/2512.04107/) (2025)
- [ChatGPT and Gemini participated in the Korean College Scholastic Ability Test -- Earth Science I](https://mlsanity.com/paper/2512.15298/) (2025)

## Abstract (arXiv)

This survey article has grown out of the GAIED (pronounced "guide") workshop organized by the authors at the NeurIPS 2023 conference. We organized the GAIED workshop as part of a community-building effort to bring together researchers, educators, and practitioners to explore the potential of generative AI for enhancing education. This article aims to provide an overview of the workshop activities and highlight several future research directions in the area of GAIED.