Year: 2025
Type: conference-paper
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.
Author: Arya R. Sarukkai
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
Funder: Rice University
OpenAlex ID: W4410822718