This document outlines potential future extensions of the AI Decision Support System. The roadmap is intentionally conservative and focused on preserving decision quality.
- Decision logic fully defined
- Diagnostic modules documented
- Example case study and report created
- Ethical and scope constraints established
This version focuses on conceptual clarity and system design, not implementation.
Goal:
Allow users to provide structured input without introducing full application complexity.
Possible approaches:
- Static form (e.g., Google Forms or Typeform)
- Structured JSON or YAML input
- Spreadsheet-based input (rows = users)
Key constraints:
- No real-time chat interface
- No adaptive questioning yet
- No user profiling or storage
Goal:
Automatically generate the decision report based on provided inputs.
Possible approaches:
- Template-based report generation
- Rule-based logic engine
- LLM-assisted explanation layer (non-authoritative)
Key constraints:
- Deterministic decision logic
- Explanatory text must remain interpretable
- No black-box scoring
Goal:
Provide a visual representation of the decision process.
Possible approaches:
- Static flow diagram
- Read-only decision tree visualization
- Interactive explanation (not decision override)
Key constraints:
- Visualization must reflect logic, not simplify it
- No gamification or ranking
The following are intentionally excluded:
- Machine learning model training
- Predictive career analytics
- User comparison or benchmarking
- Employment outcome optimization
If extended further, the system could serve as:
- a pre-diagnostic tool before education programs,
- a decision clarification step in career transitions,
- a teaching example of ethical AI decision support design.
Any future development should preserve:
- transparency,
- user agency,
- and decision integrity.