Reader Leader · Neuron Learning

Compliance & governance made usable before pilot.

A practical framework for a child-facing AI reading-tutor prototype—designed to help a multidisciplinary team give clear, disciplined answers on compliance and governance before a live pilot.

  • Responsible AI
  • Governance
  • Compliance
  • FAQ Design
Reader Leader · Neuron Learning — portfolio illustration
My contribution
Designed the compliance and governance framework; created the integrated Excel register; distilled it into a team-facing FAQ and governance brief; and produced the pre-pilot compliance-status infographic.
Focus
Build Readiness

Reader Leader product intellectual property is owned by Neuron Learning (John and Joyce Kernis). This portfolio summary records Ryan’s contribution and does not claim product ownership.

The brief

Reader Leader is an AI reading-tutor prototype built for Irish and British voices. Ryan’s task was to turn a broad compliance and governance landscape into a working system that could answer real team questions without overstating what was built, tested or still unresolved. The resulting framework made pre-pilot obligations, decisions, safeguards and ownership visible—and gave the team a reliable way to respond to compliance and governance FAQs.

Practical outputs

Integrated governance register

A detailed Excel working system with 15 connected topic registers, a 42-question FAQ bank, 10 visible decisions, architecture notes and DPIA working papers.

Team FAQ & governance brief

A reviewed quick-reference briefing: give a one-line answer first, then use the supporting detail if needed—without overstating an untested or unresolved control.

Pre-pilot status infographic

A clear visual snapshot separating prototype truth from mandatory gates and dependencies before any live child-data pilot.

Governance decision language

A shared status model—Built & tested, Designed, Before pilot and Unresolved—so evidence, ownership and next actions remain visible.

The framework

Four principles. Four practical assets.

01

Set the governance lens

Centred the framework on Child First, Purposeful AI, Meaningful Human Oversight and Responsible Data—so each decision could be assessed consistently.

02

Build the working register

Mapped data protection, child safety, supplier, security, hosting, content, copyright, AI and readiness considerations into an integrated, navigable Excel system.

03

Create a team-answering system

Converted detail into frequently asked questions and answer discipline that made it easier to communicate what was known, planned or still open.

04

Make readiness visible

Created a concise status view that connected prototype reality, mandatory pre-pilot gates, owners and evidence needs without making premature claims.

What it enabled

  • A practical way to locate and answer governance questions as the product moved towards a pilot stage.
  • Clearer shared language for discussing child safety, data, human oversight and responsible adoption.
  • A transparent pre-pilot stance, in which unresolved decisions are made visible rather than disguised as complete compliance.

This is a redaction-safe portfolio summary, not legal advice. It does not disclose the original register, project briefing, implementation architecture, legal advice, supplier detail or any child/personal data.

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