This subsystem integrates a child-psychology domain field and a parenting RL application into _AugmentedIntelligence.

It is intentionally parent-facing and shadow-mode only. It does not diagnose a child, replace therapy or pediatric care, provide legal/custody advice, or autonomously message, direct, punish, discipline, or manipulate a child.

Commands

[powershell]
.\x64\Release\_AugmentedIntelligence.exe –command “child psychology status”
.\x64\Release\_AugmentedIntelligence.exe –command “child psychology domains”
.\x64\Release\_AugmentedIntelligence.exe –command “child psychology safety”

.\x64\Release\_AugmentedIntelligence.exe –command “parenting rl status”
.\x64\Release\_AugmentedIntelligence.exe –command “parenting rl curriculum”
.\x64\Release\_AugmentedIntelligence.exe –command “parenting rl actions”
.\x64\Release\_AugmentedIntelligence.exe –command “parenting rl observe bedtime refusal and caregiver is overwhelmed”
.\x64\Release\_AugmentedIntelligence.exe –command “parenting rl evaluate child is crying and scared about school”
.\x64\Release\_AugmentedIntelligence.exe –command “parenting rl train 100 12”
.\x64\Release\_AugmentedIntelligence.exe –command “parenting rl export training/parenting_rl/plan.json”
.\x64\Release\_AugmentedIntelligence.exe –command “parenting rl reset”

Windows integration picker:

[powershell]
.\windows_integration\integration-action.ps1 -Prompt -Catalog parenting -UseResident
Get-AugmentedIntegrationActions -Catalog parenting -Query safety

Domain Field

The domain covers developmentally aware parent coaching:

• age lenses: infant/toddler, preschool, school-age, early adolescent, teen
• context: safety, caregiver regulation, child distress, connection, structure, fatigue, routines, and support needs
• outputs: parent reflection prompts, calm limit scripts, routine plans, observation summaries, and professional-support escalation prompts
• exclusions: clinical diagnosis, medication guidance, therapy replacement, legal/custody advice, punishment optimization, or automated child-directed action

Safety Policy

Safety overrides optimization.

• Crisis or suspected abuse/neglect routes to emergency, professional, or reporting support.
• The subsystem does not recommend physical punishment, threats, humiliation, shaming, isolation as punishment, or withholding love.
• RL trains offline in a small simulator and produces parent-facing suggestions only.
• Real-world use should be reviewed by a qualified professional for mental health, trauma, developmental, safety, or family-violence concerns.
• Training data should be de-identified and should avoid unnecessary private child data.

Official references used for this boundary:

• CDC positive parenting and child development resources: https://www.cdc.gov/child-development/positive-parenting-tips/index.html
• CDC child abuse and neglect prevention: https://www.cdc.gov/child-abuse-neglect/prevention/index.html
• CDC overview of child abuse and neglect types: https://www.cdc.gov/child-abuse-neglect/about/index.html
• AAP policy on effective discipline: https://publications.aap.org/pediatrics/article/142/6/e20183112/37452/Effective-Discipline-to-Raise-Healthy-Children
• ChildCare.gov state/territory reporting lookup: https://www.childcare.acf.gov/report-child-abuse-neglect

RL Design

The local simulator state has six features:

• caregiver regulation
• child distress
• connection
• structure
• safety
• fatigue

The action space is intentionally narrow and parent-facing:

• pause and self-regulate
• validate the child’s emotion
• set a calm limit
• offer two acceptable choices
• collaborative problem solving
• repair and reconnect
• seek professional or community support

Rewards prefer safety, caregiver regulation, connection, appropriate structure, and reduced distress/fatigue. They penalize unsafe delay and limit-setting while the caregiver is dysregulated.

Training Curriculum

1. Guardrails: enable crisis detection, block punitive objectives, and confirm parent-facing-only operation.
2. Observation schema: log de-identified situations with age band, setting, antecedent, behavior, adult state, and outcome.
3. Offline simulator: train Q-learning in parenting_rl_shadow_sim.
4. Human labels: have an adult reviewer label outcomes as helpful, neutral, unsafe, or seek-support.
5. Reward model: train a CUDA reward model only on reviewed data; penalize shame, threats, hitting, coercion, and unsafe delay.
6. Shadow evaluation: compare suggestions to reviewer choices; require safety recall before live parent-facing coaching.
7. Parent coaching: provide scripts and reflection prompts to the adult only.
8. Monitoring: audit drift, missed crisis language, demographic bias, disability assumptions, trauma insensitivity, and overconfident advice.

Files

• ChildPsychologyParentingRL.hpp
• ChildPsychologyParentingRL.cpp
• windows_integration/commands/parenting-actions.json
• windows_integration/commands/cuda-systems.json
• training/parenting_rl/observations.jsonl
• training/parenting_rl/parenting_rl_latest.qtable

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Filed under: Uncategorized - @ July 18, 2026 6:25 am