  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.
