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AI-Powered Learning Systems and Solutions

I solve learning and performance problems by combining learning science, systems thinking, and technology.

I design and build AI-enabled tools, workflows, and learning experiences that help learning teams work better and make better decisions.

See the WorkHow I Work
Brent Michael Espineda

Course Quality

100 courses, 50 min

Audited against ASU design standards on a hybrid deterministic and AI pass, about 30 seconds per course.

Course Development

150 hrs to 25 hrs

Canvas curriculum and course build time with an automated planning and authoring pipeline.

Decision Support

44% to 69%

Survey participation at Amazon, giving leaders current data to update SOPs and request material fixes before problems surfaced.

What I Build

Learning Systems

Quality assurance, authoring workflows, accessibility analysis, and the LMS-adjacent tooling that learning teams run on.

AI and Automation

LLM-assisted workflows, evaluation systems, knowledge tools, and operational automation with human review built in.

Learning Experiences

Simulations, branching scenarios, and microlearning that prove the learning craft behind the systems.

Selected Work

The Trainer Experience Series simulation, showing a live conversation with an AI employeeOpen Demo

The Trainer Experience Series

An AI-supported de-escalation simulation where trainers practice a difficult conversation instead of reading about one.

  • Storyline 360
  • JavaScript
  • Cloudflare
  • Gemini
The Trainer Experience Series: Open the Simulation
See All Work

About

I started in Education building and executing behavioral and academic intervention curriculums as a School Counselor. I then transitioned into corporate L&D facilitating live trainings and onboardings for business operations. In this role, I discovered an interest in learning systems thinking about why a workflow exists, where the friction comes from, and whether the problem is really a training problem at all.

Read the Full Story

How I Work

  1. 01

    Understand

    Interview stakeholders, observe the workflow, review the evidence.

  2. 02

    Define

    Decide whether the problem is learning, performance, process, or technology.

  3. 03

    Architect

    Choose the smallest system that can materially change the problem.

  4. 04

    Prototype

    Build a working model to test feasibility before committing to scale.

  5. 05

    Validate

    Test with users, deterministic rules, and adoption signals.

  6. 06

    Scale

    Document, improve, integrate, and support adoption.

Have a Learning or Performance Problem Worth Solving?

I can help diagnose the problem, design the solution, and build what the workflow is missing.

Email Me

or write directly to brent.michael670@gmail.com