Large Language Models for Education: A survey and outlook

Relevance: 7/10 255 cited 2024 paper

This survey paper systematically reviews LLM applications in education from a technology-centric perspective, covering student/teacher assistance, adaptive learning, and commercial tools, while organizing related datasets, benchmarks, and identifying deployment challenges. It provides a comprehensive taxonomy of how LLMs are being applied across multiple educational contexts including tutoring, assessment, content generation, and personalized learning.

The advent of large language models (LLMs) has ushered in a new era of possibilities in the realm of education. This survey article summarizes recent progress in the application of LLMs in educational settings from multiple perspectives, including student and teacher assistance, adaptive learning, and commercial tools. Additionally, it systematically reviews technological advancements in each area, compiles related datasets and benchmarks, and identifies the risks and challenges associated with

Tool Types

AI Tutors 1-to-1 conversational tutoring systems.
Personalised Adaptive Learning Systems that adapt content and difficulty to individual learners.
Teacher Support Tools Tools that assist teachers — lesson planning, content generation, grading, analytics.

Tags

benchmark dataset education learningcomputer-sciencehighly-cited