AI Governance Compliance

All observation chips, AI prompts, and document templates use trauma-informed, strengths-based language grounded in neuroscience and developmental psychology. We frame behaviour as communication and prioritise safety, self-regulation, and relational connection.

Cross-setting observations surface attachment and relational patterns across home, school, therapy, and community. AI-generated documents highlight secure-base relationships and avoid pathologising attachment-seeking behaviour.

Aligned with Victoria's Family Violence Multi-Agency Risk Assessment and Management Framework. Observation chips include MARAM-aligned risk indicators. AI prompts never minimise family violence and apply perpetrator-accountability principles.

Case notes and documents are structured around best interest principles: child-centred, family-focused, culturally responsive, strengths-based, and evidence-informed. Cumulative harm patterns are identified across observation timelines.

AI prompts never use “failure to protect” language. Documents partner with the non-offending parent and map perpetrator patterns of coercive control rather than blaming victim survivors.

Taughtful complies with all 11 Child Safe Standards. Our parent-as-gatekeeper consent model, audit trails, and access controls align with standards for governance, child participation, equity, and safeguarding culture.

Safety-concern observations can bypass normal consent tiers under the Child Information Sharing Scheme (CISS) and Family Violence Information Sharing Scheme (FVISS). All sharing is audit-logged with the legal basis recorded.

When observations indicate potential abuse or neglect, Taughtful prompts mandatory reporters to consider their obligations under the Children, Youth and Families Act 2005. Reporting status is tracked for audit compliance.

Observation chips include mental health awareness indicators informed by Youth Mental Health First Aid. AI has hard safety boundaries: it will never minimise suicide risk or self-harm indicators, and always recommends professional referral.

Taughtful supports affirmed names and pronouns as first-class fields. All AI-generated documents use inclusive language, respect diverse family structures, and avoid heteronormative assumptions. Aligned with Rainbow Tick Standards.

Behavioural observations are framed restoratively, not punitively. Strategy chips include restorative conversations, circles, impact exploration, and repair agreements. AI documents prioritise understanding harm and making amends.

KIS evidence documents incorporate AEL vulnerability-aware language for children in out-of-home care, Aboriginal families, and asylum seekers. Taughtful's onboarding and UX apply adult learning principles: role-specific pathways, progressive disclosure, and in-context help.