Adaptive & Personalized Learning
Project: Adaptive by Design: From Concept to Practice
A training module on adaptive learning concepts that demonstrates how pre-assessment and branching can guide learners toward personalized learning paths.
This interactive module is designed for instructional designers and learning professionals exploring how adaptive learning can be used in training environments. The project focuses on a shift I think matters: from ‘What is adaptive learning?’ to ‘When should it be used, and what problem is it solving?’ My roles include concept development, storyboarding, interaction design, and prototype development.
Adaptive learning has extraordinary potential, but it can quickly become abstract. Personalization, learner data, branching, AI-supported pathways - all useful ideas, but not always easy to picture in practice. The real challenge is helping learning professionals move from understanding adaptive learning as an idea to seeing how it can guide decisions during training.
I chose a scenario-based prototype because adaptive learning is easier to understand when learners can experience the logic for themselves. The module uses progressive learning paths, branching scenarios, and decision-based activities to show how learner choices and pre-assessment data can shape the path forward. Using Articulate Storyline, variables, and conditional logic made the adaptive structure visible and testable.
Because this audience already understands training design, the pre-assessment shows adaptive learning in action: choices guide the path, creating targeted responses to knowledge gaps. I organized the content into experience-level sections, paired key ideas with practice and feedback, and used pre-assessment logic to recommend learner tracks. Reflection prompts, glossary support, and learner-controlled navigation help keep the experience guided without closing off learner input.
This project strengthened my instructional strategy and development skills by pushing me to think carefully about adaptive pathways, branching clarity, and learner flow. Through feedback, I refined the navigation, improved the branching logic, and found more efficient ways to build the interactions. Next, I would add advanced tracks, case-based scenarios, and analytics tracking so learner behavior can shape future iterations.
