The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models
arXiv:2605.25510v1 Announce Type: new Abstract: Children increasingly have access to Large Language Models (LLMs), which may expose them to responses that are developmentally inappropriate or require age-sensitive safety, guidance, and boundaries. Existing LLM safety evaluations largely focus on harmful-content avoidance and do not explicitly target child-facing safety. We introduce KIDBench, a benchmark for evaluating child-facing LLM safety for ages 7--11 using a developmental-psychology-grounded LLM-as-a-Judge rubric. KIDBench contains realistic child queries across ten categories, with sin
As Large Language Models become more accessible, the need for explicit safety protocols for child users is becoming urgent, especially as current evaluations are insufficient.
This development highlights the growing societal integration of AI and the critical necessity for age-appropriate ethical frameworks, directly impacting platform liability and user trust.
The explicit focus on child safety in LLM development will likely lead to new regulatory pressures and specialized benchmarking, moving beyond general harm avoidance.
- · AI Safety Researchers
- · Child Development Experts
- · Parental Control Software Developers
- · LLM Developers ignoring child safety
- · Platforms with unrestricted LLM access for minors
Increased focus on age-gating and developmental appropriateness in LLM design.
Development of specialized 'child-safe' AI models or dedicated child-friendly interfaces.
Potential for new legislation mandating child-centric AI safety standards globally, influencing the diffusion of AI technologies.
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Read at arXiv cs.CL