Ethical Introspection for Improving Child LLM Interactions

Abstract

The widespread adoption of Large Language Models (LLMs) like ChatGPT among young users necessitates robust safe-guards for child-safe interactions. This paper presents a novel framework for evaluating and enhancing LLM responses through an ethics-driven approach specifically designed for child users. We introduce an introspection-based methodology combined with a child-centric ethical scoring rubric that systematically assesses and fine-tunes LLM outputs. Our experimental results demonstrate significant improvements in response appropriateness and safety compared to baseline models. The framework provides a scalable approach to ensuring age-appropriate, ethical AI interactions while maintaining engagement and educational value for young users.