This research establishes a comprehensive framework to mitigate risks when children interact with Large Language Models (LLMs), addressing critical safety gaps in AI education applications.

• Identifies key safety concerns including toxic content, bias, and cultural insensitivity in child-LLM interactions
• Analyzes both parental and empirical evidence of potential harms in AI outputs
• Proposes standardized protection protocols to evaluate and improve LLM safety for children
• Balances educational benefits with necessary safeguards for responsible AI deployment

This work provides essential security guidance for developers and educators implementing AI learning tools, ensuring technology advancement doesn't come at the expense of childhood safety and wellbeing.

LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction