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. Furthermore, a preliminary experimental result from the moral exemplar test may demonstrate that exemplary stories can elicit moral elevation in LLMs as do they among human participants. 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强化训练预测模型，开发出大型语言模型（LLMs）。LLMs已成为在多个领域实现高精度人工智能的重要途径。值得注意的是，当前LLMs具备模拟复杂人类认知的新兴功能特征，尤其是先前预测模型不具备的上下文学习与思维链能力。本文旨在探讨LLMs如何助力道德教育与发展研究。为实现该目标，我将回顾最新发表的会议论文及arXiv预印本，梳理LLMs中实现的新型功能特性；同时通过ChatGPT开展简要实验，研究LLMs在应对伦理困境与外部反馈时的行为表现。结果表明，LLMs可能基于推理解决道德困境，并能根据外部输入调整其推理过程。此外，道德典范测试的初步实验结果显示，典范故事能像在人类参与者中一样引发LLMs的道德升华。我将结合实验结果讨论LLMs对道德教育与发展研究的潜在启示。