This web-based microlearning module, developed using Lovable AI and hosted on Amazon S3, builds cybersecurity awareness for remote workers. Inspired by the KnowBe4 cybersecurity training I completed with my employer, it uses branching scenarios to immerse learners in realistic situations, from phishing attempts and suspicious emails to unauthorized data requests and unexpected 2FA alerts, with personalized feedback guiding every decision.

The instructional flow applies Adult Learning Theory to ensure relevance and immediate applicability, Scenario‑Based Learning to foster authentic decision‑making, and Mayer’s Personalization and Multimedia Principles to keep content engaging yet cognitively manageable. Accessibility was integrated from the start, meeting WCAG 2.2 standards for keyboard navigation, high‑contrast visuals, and inclusive interaction design.

Developed through the ADDIE framework and leveraging the ARCS model to sustain attention and motivation, the course reinforces both awareness and practical defense skills, aligning with industry demand for remote workforce security training.

Tools & Frameworks Used

• Lovable AI, Perplexity AI

• WCAG 2.2 accessibility standards

• Adult learning principles: autonomy, practical relevance, prior experience and immediacy

• Instructional design framework and models: Gagné, Merrill, Mayer

Project Documents

• Needs Analysis

• WCAG 2.2 Analysis