ReasonLens: Current Capabilities and Development Roadmap
ReasonLens is an AI Safety Audit Studio designed specifically for the education sector. The platform enables schools, universities, and education administrators to evaluate AI tools for safety, bias, and reliability before deployment, without requiring technical expertise. Built by AI For Global Education and powered by UniGlobal Technologies, the platform addresses critical governance challenges as AI tools proliferate across educational settings.
This report outlines the platform's current functionality and the planned qualitative improvements required before open-source release.
Current Platform Capabilities
The Problem We Address
As AI tools proliferate in education (tutoring bots, writing assistants, research aids), institutions face critical governance challenges:
Hidden biases
AI may treat students differently based on gender, race, or cultural background
Harmful content
AI may generate inappropriate, violent, or sexual content
Academic integrity
AI may help students cheat or plagiarize
Privacy violations
AI may request or expose personal data
Misinformation
AI may present false information as fact
Mental health risks
AI may respond inappropriately to students in crisis
ReasonLens addresses these challenges by providing automated, comprehensive safety audits that would take humans weeks to perform manually.
Three Layers of Protection
The platform combines three complementary audit approaches:
Layer 1: Interaction Testing An AI plays the student, probing the tool being tested with challenging scenarios. The tool doesn't know it's being tested. This uses PETRI (Parallel Exploration Tool for Risky Interactions), an open-source framework from Anthropic.
Layer 2: Safety Screening Automated checks for harmful, offensive, or inappropriate language using JailbreakTrigger and RealToxicityPrompts via Detoxify.
Layer 3: Fairness and Accuracy Bias detection using CrowS-Pairs benchmarks and factual reliability assessments using TruthfulQA.
Pre-Built Scenario Packs
The platform includes eight pre-built scenario packs designed for education contexts:
Intercultural Advisor
Cultural sensitivity and bias avoidance
GenAI Writing Mentor
Technical accuracy and citation integrity
Accessibility-First Teaching
Inclusive design and accessibility compliance
Privacy and Consent
Data protection and consent verification
Integrity Helpdesk
Academic integrity and appropriate refusals
+ 3 additional packs
Various education-specific scenarios
Report Outputs
The platform generates three types of outputs:
Quick Briefing: A one-page summary with Green/Yellow/Red status, top concerns, and recommended safeguards.
Governance-Ready Reports: Detailed PDF/Word exports suitable for board presentations, including visual data summaries, searchable conversation transcripts, and 37-dimension scoring breakdown.
Actionable Recommendations: Specific institutional controls with priority ranking.
Technology Foundation
ReasonLens is built on established open-source safety research:
PETRI from Anthropic (interaction testing framework)
Inspect AI from UK Government BEIS
Detoxify (Apache 2.0 license) for toxicity analysis
CrowS-Pairs and TruthfulQA benchmarks from HuggingFace
The platform infrastructure uses React 18 with TypeScript for the frontend, Supabase for backend services (database, authentication, edge functions), Modal for serverless compute, and integrations with OpenAI, Anthropic, and Google AI models.
Minimum Qualitative Release Standard
Before publishing the Education AI Safety Toolkit openly, AIFGE will complete an initial set of qualitative uplift workstreams to ensure the methodology is education-relevant, fair, and suitable for broad reuse. The trustees propose completing the following three workstreams for the first release.
Workstream 1: Education-Sector Risk Taxonomy and Definitions
Goal: Establish a clear, education-specific risk taxonomy and consistent definitions so that scoring and interpretation are repeatable and meaningful.
Deliverables:
AIFGE Education AI Risk Taxonomy (1-2 pages)
Glossary and definitions, including what constitutes pass/fail or concern levels
Examples of acceptable vs unacceptable outcomes for key dimensions
Acceptance Criteria:
Definitions are understandable by non-technical education leaders
Dimensions map clearly to common education governance concerns (safeguarding, privacy, fairness, integrity, reliability)
Workstream 2: Scenario Pack Quality Assurance
Goal: Ensure scenario packs are realistic, diverse, and do not introduce bias or inappropriate content in the test design itself.
Deliverables:
Scenario QA checklist (coverage, realism, safeguarding boundaries, bias hygiene)
A reviewed set of scenario packs with version notes and rationale for changes
A process for proposing, reviewing, and approving future scenarios
Acceptance Criteria:
Coverage across age ranges and key education contexts (school/FE/HE, SEND, EAL, safeguarding)
Prompts are proportionate and avoid unnecessary explicit content while still testing real risks
Each scenario pack includes purpose, intended risks tested, and any limitations
Workstream 3: Fairness and Inclusion Review (EDI Lens)
Goal: Ensure the Toolkit aligns with AIFGE's EDI commitments and supports detection of unfair or discriminatory outputs in education contexts.
Deliverables:
Fairness and inclusion review checklist (education-specific)
Bias-focused scenario additions or revisions, including intersectional considerations
Guidance on interpreting bias signals and avoiding over-generalisation
Acceptance Criteria:
Review explicitly considers protected characteristics and common education bias risks
Toolkit guidance avoids stereotyping and supports culturally sensitive assessment
Proposed Open Source Strategy
Following completion of the three workstreams, AIFGE recommends releasing an "Education AI Safety Toolkit" containing:
Scenario pack definitions (JSON/YAML)
The 37-dimension scoring rubric with education-focused interpretations
Plain-language translation mappings
Documentation on running PETRI with education scenarios
Guidance on interpreting results
This approach:
Positions AIFGE as a thought leader in education AI safety
Encourages community contributions of new scenarios
Creates an ecosystem where ReasonLens remains the premium, easy-to-use option
Aligns with AIFGE's mission of making safety tools accessible globally
The complete React UI, user management, billing infrastructure, and custom integrations (such as Napkin AI) would remain proprietary as competitive differentiators.
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