Potential benefits of employing large language models in research in moral education and development

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.

KEYWORDS:

• Large language models
• artificial intelligence
• moral reasoning
• moral exemplar
• simulation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Hyemin Han

Hyemin Han is an associate professor in educational psychology and educational neuroscience, and a program coordinator for the educational psychology MA/PhD program at the University of Alabama. His research interests include moral development, moral education, social neuroscience and computational modeling.