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

2024-01-15

arXiv.org

AI Summary

Educational NLP suffers from fragmented task definitions, inconsistent evaluation protocols, and inadequate adaptation of large language models (LLMs) to pedagogical requirements. Method: We propose the first unified taxonomy covering four core educational NLP tasks—question answering, problem generation, automated assessment, and error correction—and conduct a systematic analysis of LLM-specific challenges in education, including controllability, difficulty calibration, explainability, and adaptive learning. Leveraging a comprehensive survey of 200+ papers and 50+ open-source educational datasets, we distill six actionable research directions. Contribution/Results: We release the first domain-wide open-source resource repository—comprising benchmark suites, curated datasets, and modular toolkits—to standardize evaluation and foster community-driven development in educational NLP.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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

Problem

• Surveying NLP applications for education domain problems
• Reviewing question answering and automated assessment techniques
• Exploring LLM-based systems for adaptive learning education

Innovation

• Systematic taxonomy of NLP education applications
• Cutting-edge techniques including LLM integration
• Open-source datasets and demonstrations for educators