Future Prospects of Large Language Models: Enabling Natural Language Processing in Educational Robotics

Abstract

Large language models (LLMs) have recently shown considerable promise in educational robotics by offering generic knowledge necessary in situations when prior programming is not possible. In general, mobile education robots cannot perform tasks like navigation or localization unless they have a working knowledge of maps. In this letter, we tackle the issue of making LLMs more applicable in the field of mobile education robots by helping them to understand Space Graph, a text-based map description. This study, which focuses on LLMs, is divided into several sections. It explores basic natural language processing (NLP) techniques and highlights how they can help create smooth education discussions. Examining the development of LLMs inside NLP systems, the paper explores the benefits and implementation issues of important models utilized in the education sector. Applications useful in educational discussions are described in depth, ranging from patient-focused tools like diagnosis and treatment recommendations to systems that support education providers. We provide thorough instructions and real-world examples for quick engineering, making LLM-based educational robotics solutions more accessible to novices. We demonstrate how LLM-guided upgrades can be easily included in education robotics applications using tutorial-level examples and structured prompt creation. This survey provides a thorough review and helpful advice for leveraging language models in automation development, acting as a road map for researchers navigating the rapidly changing field of LLM-driven educational robotics.