Age-Appropriate Content Assessment Framework
A comprehensive, evidence-based approach to evaluating digital content safety for children and adolescents
Aligned with IMDA Code of Practice, UNICEF Guidance on AI and Children, and the UN Convention on the Rights of the Child
The Problem
đź“śRegulations Lack Technical Implementation
While regulations like IMDA's Code of Practice for Online Safety, the EU AI Act, and UK Online Safety Act are being implemented, they are not designed in a tech-first, measurable, and automatable way. Compliance requirements remain vague, making it difficult for platforms to implement and for regulators to verify.
👶Developmental Needs Are Ignored
Current regulations treat all children as a monolithic group, failing to recognize that a 7-year-old has vastly different cognitive and emotional capacities than a 15-year-old. Age-appropriate content assessment requires understanding developmental stages, not just binary “child vs adult” distinctions.
🔧Existing Tools Ignore Children
A variety of automated tools exist for code scanning, website testing, vulnerability assessment, and even LLM assurance (like DeepTeam)—but none are designed with children's safety in mind. Child-specific harms like grooming, age-inappropriate content, and developmental impact are not measured.
⚠️Children Are the Most Vulnerable Users
Children are among the largest and most vulnerable users of technology and AI. Recent Singapore data shows 8 in 10 young people aged 13 to 17 are using Generative AI at least once a week and 70% of that use for homework or school-related tasks. Yet they face unique risks: AI-generated CSAM, deepfakes, emotional dependency on chatbots, and exposure to harmful content their developing minds cannot process.
The gap is clear: We have regulations without implementation guidance, tools without child-safety focus, and a rapidly growing population of young users exposed to unprecedented risks. This framework bridges that gap by providing a measurable, automatable, developmentally-informed approach to content assessment.
What is this framework?
This framework provides a systematic approach to assess whether web products—including LLM-based chatbots, static websites, and dynamic applications—are appropriate for children and adolescents.
Rather than simple age ratings, we match content risk/benefit profiles against age-specific developmental capacities to produce holistic appropriateness scores.
🎯 Universal Application
Works across all web products and content types (text, images, audio, video)
đź“Š Evidence-Based
Built on regulatory frameworks and child development research
🤖 AI-Ready
Includes specific safeguards for AI-generated content and chatbots
Assessment Criteria
Content is evaluated across 8 safety dimensions and 5 educational dimensions, each with specific indicators and scoring rubrics.
| Dimension | Key Indicators | Weight |
|---|---|---|
| Content Safety & Risk (60% of overall score) | ||
| Information Integrity | Source credibility, fact-checking, misinformation detection, AI-generated content disclosure | 12% |
| Privacy & Data Protection | Data collection practices, COPPA compliance, privacy-by-design, children's data agency | 18% |
| Harmful Content Detection | Sexual content (CSEM zero tolerance), violence, self-harm, cyberbullying, health misinformation, vice/crime | 25% |
| Toxicity & Bias | Hate speech, profanity, stereotypes, algorithmic fairness, non-discrimination | 12% |
| Interaction Safety | Contact risks, grooming prevention, reporting effectiveness, moderation quality, response times | 15% |
| Manipulative Design | Dark patterns, addictive features, ethical monetization, time management tools | 8% |
| Transparency & Accountability | Safety information accessibility, AI disclosure, community guidelines, annual reporting | 5% |
| AI-Specific Safety | Impact assessments, chatbot safeguards, emotional dependency prevention, deepfake detection | 5% |
| Educational & Developmental Value (40% of overall score) | ||
| Educational Content | Learning objectives, curriculum alignment, AI literacy, digital citizenship, life skills | 30% |
| Positive Messaging | Role models, prosocial themes, diversity & inclusion, well-being support | 20% |
| Creative Engagement | Creative tools, self-expression, problem-solving, collaboration features | 20% |
| Usability & Accessibility | WCAG compliance, age-appropriate design, navigation clarity, inclusive design | 15% |
| Engagement Quality | Active vs passive engagement, balanced screen time, meaningful interactions | 15% |
Scoring System: Each dimension receives a grade (A-F) and score (0-100). Final appropriateness is determined by matching content demands against child developmental capacities.
Developmental Capacity by Age Group
Different age groups have vastly different capacities to process information, recognize risks, and regulate emotions. The framework matches content demands against these developmental capacities to determine appropriateness.
Ages 0-5
Pre-literateAges 6-9
Primary SchoolAges 10-12
Transition YearsAges 13-15
Early TeensAges 16-17
Late TeensHow Content Scores Match Age Groups
The framework calculates a gap score between what the content demands and what the child can handle. For example, if content requires 70/100 critical thinking but a 10-year-old has only 45/100 capacity, the gap is 25 points—indicating significant risk.
Highly appropriate - Content demands well within child's capacities
Appropriate with minor considerations - Small gaps manageable with guidance
Appropriate with supervision - Moderate gaps require active parental involvement
Questionable - Significant gaps, requires substantial supervision and intervention
Not appropriate - Content demands far exceed child's developmental capacities
Three Implementation Approaches
The framework can be deployed in three complementary ways, each serving different stakeholders in the child safety ecosystem.
🏛️For Regulators
Post-Deployment AuditGovernment authorities like IMDA can use this framework to conduct regulatory audits of deployed platforms, similar to the 2024 Online Safety Assessment Report methodology.
Key Features:
- Mystery Shopper Testing - Automated test accounts submit harmful content to measure detection effectiveness
- Effectiveness Metrics - Response time, block rate, notification rate (targets: >90% effectiveness, <24hr response)
- Policy Assessment - Automated analysis of privacy policies, community guidelines, transparency reports
- Public Disclosure - Published safety ratings for consumer awareness and platform accountability
- Compliance Enforcement - Required actions, deadlines, and follow-up audits
👨‍💻For Developers
Pre-Deployment ScanningPlatform developers can integrate the framework into their CI/CD pipelines and content upload workflows to proactively block harmful content before it reaches users.
Key Features:
- CI/CD Integration - GitHub Actions, GitLab CI, Jenkins workflows scan content before deployment
- Upload Hooks - Real-time assessment of user-generated content with automatic blocking/review queuing
- Creator Targeting - Mandatory scanning for creators with ≥40% youth audiences or ≥1,000 youth followers
- Developer Feedback - Clear explanations of why content was blocked with improvement suggestions
- Compliance Reporting - Automated quarterly reports for regulatory submission
👨‍👩‍👧‍👦For Parents
Child-Safe BrowserA child-safe browser (in development by FutureNet) that uses the framework to provide real-time content filtering as children browse the web—going beyond simple whitelist/blacklist approaches.
Key Features:
- Intelligent Filtering - Context-aware assessment, not just domain blocking
- Adaptive Modes - Strict (block entire page), Adaptive (filter specific elements), Permissive (warn but allow)
- Real-Time Assessment - Lightweight pre-scan with progressive content analysis
- Parent Dashboard - Child profiles, activity logs, customizable thresholds, manual overrides
- Offline Capability - Local ML models for assessment without internet dependency
- Educational Approach - Explains why content is blocked to teach digital literacy
How They Work Together
| Aspect | For Regulators | For Developers | For Parents |
|---|---|---|---|
| Timing | Post-deployment | Pre-deployment | Real-time |
| Scope | Entire platform assessment | Platform's own content | Any website visited |
| Enforcement | Regulatory compliance | Automated blocking | User-side filtering |
| Effectiveness | Reactive (identifies issues) | Proactive (prevents harm) | Protective (shields user) |
Together, these three approaches create a comprehensive safety ecosystem covering prevention, verification, and protection.
Technical Implementation
The framework uses a combination of automated tools and AI models to assess content across all modalities:
Text Analysis
OpenAI Moderation API, Azure Content Safety, LLM-as-judge, reading level analysis, fact-checking APIs
Image Analysis
Google Cloud Vision, AWS Rekognition, PhotoDNA for CSEM, deepfake detection, OCR for text-in-image
Audio Analysis
Speech-to-text (Whisper), audio classification, voice deepfake detection, tone and emotion analysis
Video Analysis
Frame extraction, scene detection, motion analysis for violence, live stream monitoring, thumbnail assessment
Key Principle: While technical methods vary by content type, the assessment criteria, scoring weights, and appropriateness matching algorithm remain identical across all platforms and content types.
Learn More
This framework represents months of research into child development, regulatory standards, and technical feasibility. We're committed to creating safer digital spaces for children.