arXiv:2605.21609v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly embedded in adolescent digital environments, mediating information seeking, advice, and emotionally sensitive interactions. Yet existing safety mechanisms remain largely grounded in adult-centric norms and operationalize safety through refusal-oriented suppression. While such approaches may reduce immediate policy violations, they can also create conversational dead-ends, limit constructive guidance, and fail to address the developmental vulnerabilities inherent in adolescent-AI interactions. We argue
As LLMs become increasingly pervasive in everyday interactions, particularly among younger demographics, the critical need for age-appropriate safety mechanisms beyond adult-centric norms is becoming evident.
This work directly addresses the vulnerabilities of a significant user base (adolescents) and highlights a growing societal challenge in deploying AI responsibly, moving beyond simple content refusal to nuanced, developmental guidance.
Current AI safety paradigms, largely based on generalized suppression, will need to evolve towards more sophisticated, context-aware, and rewrite-based approaches to be effective and beneficial for diverse user groups.
- · AI ethicists
- · Adolescent mental health organizations
- · AI developers focused on responsible deployment
- · Educational technology platforms
- · LLM developers ignoring age-specific safety
- · Platforms adopting only rudimentary content filters
AI models will integrate more sophisticated, context-aware safety mechanisms tailored to specific user demographics.
Public expectations for AI safety will shift towards demanding adaptive and developmentally appropriate responses from LLMs.
This could lead to legal and regulatory frameworks distinguishing between AI safety requirements for different age groups, particularly for minors.
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Read at arXiv cs.CL