Francis David
Learning Design Architect
AI & ML Curriculum Architecture
Project Overview
Designing a two-track curriculum architecture to support progressive skill development in Artificial Intelligence and Machine Learning.
This work focuses on creating a clear learning pathway that helps learners understand progression across levels while enabling instructional teams to design and scale learning consistently.
Designing the Curriculum Architecture
The curriculum is designed as a two-track learning pathway, separating Artificial Intelligence and Machine Learning while sharing a common foundational base.
Each track is organised into Beginner, Intermediate, and Advanced levels, with modules and topics sequenced to reflect increasing conceptual depth, tooling exposure, and application complexity.
Client Context
The client needs a clear and structured AI and ML learning pathway that supports learners at different stages of experience. Existing content is fragmented, progression across levels is unclear, and expectations vary between courses.
There is a need to define how individual modules connect, how skills build over time, and how learning translates into real-world capability across both tracks.
What this curriculum is (and isn’t)
It is a curriculum architecture and learning pathway design, not a content library.
It defines progression, sequencing, and instructional intent across levels and tracks.
It supports multiple learner entry points while maintaining coherence.
It is not a certification map or assessment blueprint.
Design Approach
Curriculum design is treated as a system-level problem, rather than a collection of individual courses. Progression is intentionally defined so each level builds on prior knowledge and capability.
Instructional intent is embedded at the module and topic level, ensuring alignment between concepts, tools, and expected learner outcomes across both AI and ML tracks.
Key Components
- Two-track learning pathway (AI and ML)
- Level-based progression: Beginner, Intermediate, Advanced
- Module- and topic-level structuring
- Tooling and technology mapping per level
- Instructional intent defined for each learning segment
Role & Contribution
I designed the complete curriculum architecture, including track structure, level progression, module sequencing, tooling alignment, and instructional intent mapping.
This included defining how skills evolve across levels and ensuring coherence across both learning tracks.
Outcome
The curriculum architecture provides a clear, scalable learning path that supports structured skill development across AI and ML domains.
It enables instructional teams to design consistent learning experiences while helping learners understand how individual modules contribute to long-term capability building.
Reflection
This work reinforced the importance of treating curriculum design as an architectural exercise. Making progression and intent explicit improves clarity, scalability, and adaptability as technologies and role expectations evolve.
Description
Personal Portfolio
Curriculum architecture and learning pathway design for Artificial Intelligence and Machine Learning, structured across progressive skill levels.