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Recently, computer scientists have developed large language models (LLMs) bytraining prediction models with large-scale language corpora and humanreinforcements. The LLMs have become one promising way to implement artificialintelligence with accuracy in various fields. Interestingly, recent LLMspossess emergent functional features that emulate sophisticated humancognition, especially in-context learning and the chain of thought, which wereunavailable in previous prediction models. In this paper, I will examine howLLMs might contribute to moral education and development research. To achievethis goal, I will review the most recently published conference papers andArXiv preprints to overview the novel functional features implemented in LLMs.I also intend to conduct brief experiments with ChatGPT to investigate how LLMsbehave while addressing ethical dilemmas and external feedback. The resultssuggest that LLMs might be capable of solving dilemmas based on reasoning andrevising their reasoning process with external input. Furthermore, apreliminary experimental result from the moral exemplar test may demonstratethat exemplary stories can elicit moral elevation in LLMs as do they amonghuman participants. I will discuss the potential implications of LLMs onresearch on moral education and development with the results.

Potential Benefits of Employing Large Language Models in Research in Moral Education and Development