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

TL;DR

This paper explores how large language models can be utilized in moral education and development research, highlighting their emergent reasoning abilities and potential to address ethical dilemmas and evoke moral responses.

Contribution

It reviews recent advancements in LLMs' functional features and presents preliminary experiments demonstrating their reasoning and moral elevation capabilities in ethical contexts.

Findings

LLMs can solve ethical dilemmas through reasoning.

External feedback can help LLMs revise their moral reasoning.

Exemplary stories can induce moral elevation in LLMs.

Abstract

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.