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System Architecture
TSB AI is built as a modular, scalable intelligence system.
It separates psychological modeling, adaptive logic, and content delivery into distinct layers to ensure flexibility and long-term extensibility.
Handles:
- Student queries
- Assessment responses
- Behavioral signals
- Performance data
This layer collects both explicit and implicit inputs.
Processes structured cognitive and behavioral indicators to generate:
- Stress profile
- Motivation model
- Decision pattern mapping
- Learning style indicators
Outputs a dynamic psychological state vector.
Aggregates assessment results into a persistent adaptive model.
Responsible for:
- Updating user profile
- Tracking longitudinal changes
- Identifying behavioral drift
- Detecting burnout risk
Core intelligence layer that determines:
- Tone of communication
- Level of conceptual depth
- Reinforcement strategy
- Study pacing
- Reflection prompts
This layer modifies how content is delivered.
Handles:
- Concept explanations
- Problem-solving guidance
- Structured learning plans
- Revision strategies
This layer interacts with large language models or internal knowledge systems.
Continuously monitors:
- Performance improvement
- Consistency patterns
- Emotional stability signals
- Engagement fluctuations
Feeds adjustments back into the Psychological Engine.
TSB AI is designed to support:
- Cloud-based intelligence
- Hybrid privacy-first systems
- Optional local deployment
- Modular hardware integration
The architecture is built to scale from chatbot prototype to full cognitive ecosystem.
Future extensions may include:
- Environmental data integration
- Focus-state detection
- Hardware-assisted feedback systems
- Multi-agent adaptive modeling