ANCHR™
Child-Safe Evaluation Framework.
A developmentally grounded framework for evaluating AI systems through the lens of children’s emotional, cognitive, and social safety.
[ FRAMEWORK ]
The Five Domains of the ANCHR Framework
[ ABOUT ]
Children interact with AI differently from adults. Their reasoning, emotional regulation, social understanding, and sense of self are still forming—yet most AI safety evaluations still treat children as scaled-down adults, prioritizing content filters and policy compliance over developmental impact.
ANCHR™, the Child-Safe Evaluation Framework (CSEF) — was created to address this gap. It is a developmentally informed framework for evaluating how AI systems interact with children across key dimensions of safety, wellbeing, and development.
Rather than asking only “Is this content allowed?”, ANCHR asks: What does this system invite a developing mind to feel, believe, learn, or become?
This page presents the public-facing structure of ANCHR: the core domains used to assess child–AI interactions. The full evaluation rubric, scoring logic, and testing protocols are proprietary to LittleShield AI and are applied through formal assessment engagements.
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How an AI system affects a child’s emotional wellbeing, including tone, framing, and responsiveness to vulnerability. This domain evaluates whether interactions support emotional regulation and psychological safety, rather than amplifying fear, shame, distress, or emotional dependence.
Why this matters: Children process emotional cues literally and intensely; even subtle misalignment can cause lasting emotional harm.
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How well an AI system’s explanations, reasoning, and outputs align with a child’s developmental stage. This domain assesses whether information is presented in ways children can meaningfully understand—without overwhelming, misleading, or distorting a developing sense of reality.
Why this matters: Children are still learning how to reason, evaluate truth, and interpret uncertainty.
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How clearly and consistently an AI system maintains appropriate boundaries with child users. This domain evaluates whether the system avoids role confusion, emotional entanglement, or social simulation, and reinforces that it is a tool—not a person, authority figure, or relationship.
Why this matters: Children form trust and attachment quickly and may not distinguish between tools and social agents.
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How effectively an AI system anticipates, detects, and mitigates potential harm to children before it escalates. This domain evaluates whether systems avoid introducing risky behaviors, respond appropriately to vulnerability signals, and reduce the likelihood of emotional, social, or behavioral harm.
Why this matters: Children have limited capacity to foresee risk or manage escalation without adult support.
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How an AI system influences a child’s emerging sense of self, belonging, and worth. This domain evaluates whether interactions support autonomy and inclusion, avoid bias or stereotyping, and do not impose identity labels, norms, or expectations a child cannot critically assess.
Why this matters: A child’s identity and self-concept are highly malleable and easily shaped by external feedback.
Interested in applying ANCHR to your product, platform, or research initiative?