Module name :
Scenario-Based Module
Module Objective :
The Scenario-Based module evaluates an AI model’s ability to follow a multi-step scenario (successive questions and answers), simulating a real conversation.It calculates four key indicators — ICE, ICC, IFA, and IFS — to measure the model’s coherence, continuity, and adaptability.
How It Works :
- The user defines a scenario in several stages (e.g., employer vs. employee, GDPR case, customer situation).
- The AI generates a response at each stage (or they can be pasted manually in hybrid mode).
- The module automatically calculates coherence and continuity scores.
- The user can adjust the evaluation manually and export results (CSV).
Key Metrics :
- ICE – Step Coherence Index (alignment between expected and generated response)
- ICC – Contextual Continuity Index (logical consistency between stages)
- IFA – Adversarial Flexibility Index (handling of contradictions or challenges)
- IFS – Global Weighted Score (overall view of the scenario)
Who Is It For ?
- Legal and compliance experts
- Lawyers and AI auditors
- Universities, trainers, and students
- Customer support and user relationship teams
- Any organization seeking to test its AI in realistic dialogues
Practical Use Cases :
- Verify the consistency of a legal assistant in labor law cases
- Test an educational chatbot facing contradictory questions
- Simulate a client interaction for a financial service
- Train students through interactive narrative scenarios
Fields of Application :
- Law & compliance
- Education & research
- Finance & customer support
- AI governance
Why This Module Matters ?
- Because AI must be tested in realistic dialogue conditions — this module allows you to measure, document, and compare how a model handles authentic multi-step conversations.
Available On :
- BULORΛ.ai secure portal
- Access via secure token
- On-demand CSV / PDF export
