Recently, computer scientists have developed large language models (LLMs) by training prediction models with large-scale language corpora and human reinforcements. The LLMs have become one promising way to implement artificial intelligence with accuracy in various fields. Interestingly, recent LLMs possess emergent functional features that emulate sophisticated human cognition, especially in-context learning and the chain of thought, which were unavailable in previous prediction models. In this paper, I will examine how LLMs might contribute to moral education and development research. To achieve this goal, I will review the most recently published conference papers and ArXiv preprints to overview the novel functional features implemented in LLMs. I also intend to conduct brief experiments with ChatGPT to investigate how LLMs behave while addressing ethical dilemmas and external feedback. The results suggest that LLMs might be capable of solving dilemmas based on reasoning and revising their reasoning process with external input. I will discuss the potential implications of LLMs on research on moral education and development with the results.

翻译：近年来，计算机科学家通过大规模语料库和人类反馈训练预测模型，开发出大型语言模型（LLMs）。LLMs已成为在各领域实现精准人工智能的有前景途径之一。值得注意的是，最新LLMs具备模拟复杂人类认知的涌现功能特性，特别是上下文学习和思维链能力，这些在以往的预测模型中并不存在。本文将探讨LLMs如何为道德教育与发展研究做出贡献。为实现这一目标，我将回顾最新发表的会议论文及ArXiv预印本，概述LLMs实现的新型功能特性。同时，我计划通过简短实验与ChatGPT互动，研究LLMs在处理道德困境和外部反馈时的表现。结果表明，LLMs或能基于逻辑推理解决困境，并根据外部输入调整其推理过程。我将结合这些结果，讨论LLMs对道德教育与发展研究的潜在启示。