Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends

• DOI: 10.48550/arxiv.2401.07518
• Publication Date: 2024-01-01
• Authors: Yunshi Lan; Xinyuan Li; Hanyue Du; Xuesong Lu; Ming Gao; Weining Qian; Aoying Zhou
• Keywords: Relevance

Natural Language Processing (NLP) aims to analyze text or speech via techniques in the computer science field. It serves applications domains of healthcare, commerce, education and so on. Particularly, NLP has been widely applied domain its have enormous potential help teaching learning. In this survey, we review recent advances with focus on solving problems relevant domain. detail, begin introducing related background real-world scenarios where could contribute. Then, present a taxonomy highlight typical including question answering, construction, automated assessment, error correction. Next, illustrate task definition, challenges, corresponding cutting-edge based above taxonomy. particular, LLM-involved methods are included for discussion due wide usage LLMs diverse applications. After that, showcase some off-the-shelf demonstrations At last, conclude six promising directions future research, more datasets domain, controllable LLMs, intervention difficulty-level control, interpretable educational NLP, adaptive learning, integrated systems education. We organize all papers open-available Github Link better review~\url{https://github.com/LiXinyuan1015/NLP-for-Education}.