BAMoore

Gov Agentic EdTech

Praxis

28%

Decreased Training Time

84%

SUS Score

87%

Preferred UI Refactor

This is a portfolio-safe redesign of a real, deployed product built for government and federal security training environments.

Praxis is an adaptive training platform designed to prepare security personnel for high-stakes operational environments. Grounded in human factors research and cognitive load modeling, the platform replaced fragmented legacy systems with a modular, intelligent training experience that reflected the real pressures and pacing of security inspection work.

The Challenge

Security training at scale is a coordination problem as much as a learning one. Personnel need to know what to train on, when, and at what level of depth, while administrators need visibility into readiness across an entire workforce. Legacy tools handled neither well, creating gaps in compliance tracking, inconsistent skill development, and no meaningful feedback loop between training performance and operational readiness.

Session planning organized required training into a calmer queue so users could focus on the next task.

The Design

The platform was structured around five interconnected areas: a Planner that surfaced due and upcoming sessions with clear urgency signaling; an interactive Training environment built on branching decision trees that simulated real threat-assessment scenarios; a Learning hub that personalized module recommendations and visualized individual competency through a multi-dimensional radar model (ADARE); a Documentation center that gave users a searchable, well-organized knowledge base mirroring the platform's navigation; and a Settings panel that balanced user-level control with administrator-enforced boundaries.

The dark, low-distraction visual language was a deliberate choice for the operational context. High-contrast typography, restrained color use, and consistent spatial logic kept cognitive load manageable, even during high-stakes training sequences. Personalization options including display density, theme, and language ensured the platform could adapt to varied deployment environments without compromising usability.

Workflow mapping helped align adaptive instruction, system feedback, and administrator oversight around the same training model.
Personalization controls supported different training needs without adding unnecessary friction to the core workflow.

Performance and Skill Visibility

One of the core design challenges was making skill development legible without oversimplifying it. The Training module tracked five competency dimensions in real time: Accuracy, Detection, Analysis, Recall, and Engagement. The Learning hub extended this with the ADARE model, a radar visualization that gave learners and coordinators a shared vocabulary for readiness. Rather than a pass/fail system, the platform communicated growth as a continuous, multidimensional picture.

Documentation and supporting materials kept the system teachable for administrators, trainees, and future product teams.

Outcomes

The resulting design consolidated fragmented workflows into a single, coherent experience that was accessible to frontline personnel and actionable for administrators. The design system built for Praxis extended beyond the platform itself, providing a scalable foundation for other secure training tools and reducing future development overhead across the product suite.

Overview

Praxis turned fragmented legacy training tools into a modular learning system shaped around real operational pressure, pacing, and oversight.

Sector

Training

Timeline

2019 - 2020

Client

Federal Defense Contractor

Role

UX Design Engineer

Status

Live Product

Focus

Agentic WorkflowsField OperationsInstructional Design
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