EET Personalized Learning AI · Pilot
Personalized Engineering Learning, Built Around You
AI-native personalized learning infrastructure that transforms engineering course libraries into adaptive learner-specific learning paths and guided technical journeys.
EET Personalized Learning AI reads what you already know, what you want to achieve, how much time you have and what you want to build, then gives you an ordered learning path made only of real Educational Engineering Team courses, with a clear reason and outcome for every step.
How it works
- Tell us where you are. A short assessment on EET Academy asks for your goal, current level, what you already know, the result you want, your time window and daily study time, and any project you have in mind.
- We build your learner profile. The planner interprets your answers: target skill, real starting level, intent (learn a skill, build a project or get a job) and the prerequisites you can safely skip.
- You get an ordered path. A sequence of stages chosen only from the approved EET course catalog. Each stage explains why it is there for you and what you will be able to do after it, sized to fit your time window.
- You start learning. Every stage links straight to its course on EET Academy.
Personalized paths, not generic playlists
Two learners who both ask for “Arduino” can get completely different paths. In our pilot tests, a complete beginner who wants a first hardware project in four weeks gets fundamentals, tool setup, sensors and two guided projects. An intermediate learner who already knows GPIO, sensors and serial communication and wants a connected build skips every beginner course and goes straight to communication protocols, interrupts and IoT projects. The two paths share no courses.
What shapes your path
- Skills: what you can already do, so you don’t repeat basics.
- Goals: a job, a project or a new skill each produce a different path shape.
- Interests: connected/IoT builds, sensors, hardware or professional practice.
- Prerequisites: foundations first, so nothing later feels like a jump.
- Time: stages are sized to your weeks and daily study time.
Example path
Complete beginner · wants a first hardware project · 4 weeks at 30 minutes a day
- Fundamentals: Introduction to Arduino
- Tool setup: Design and Simulate Arduino Boards and Test Your Code
- Core concept: Arduino Sensor Mastery: Unleashing Digital Brilliance
- Guided project: Arduino Car Parking Assistant
- Guided project: Arduino Weather Station Step By Step Guide
Engineering disciplines
Live in the pilot: Arduino and embedded systems. Arduino has the deepest coverage today. When someone asks for a topic the catalog does not yet cover well, the request is flagged for human review instead of getting an unrelated path.
Expanding to: ESP32, IoT, electronics, PCB design, industrial automation and AI for engineers, as those courses are added to the approved catalog.
Built on the existing EET ecosystem
Educational Engineering Team has taught engineering online since 2007. The new product plugs into that library and audience, so learners get paths made from courses that already exist and have already been taught at scale.
| Existing EET channel | Figure |
|---|---|
| Udemy (Educational Engineering Team instructor profile) | 463,486 learners |
| YouTube (@EducationalEngineeringTeam) | 41.4K subscribers · 31M+ lifetime views |
| EET Academy (academy.eduengteam.com) | 42 courses in the current Personalized Learning AI pilot catalog |
AI-powered curriculum planning with Claude
The planner’s reasoning layer is built on Anthropic’s Claude (integrated in the product code; it is switched on in production once API access is configured). Claude reads the learner’s assessment and the approved catalog, including each course’s public facts (summary, key topics, projects), and returns a structured learner profile and an ordered path where every step has a learner-specific “why” and “outcome”.
- Catalog-only recommendations: Claude may only return course IDs that exist in the catalog. Anything else is dropped and the plan is flagged for review.
- Structured, validated output: responses are schema-constrained JSON, validated again in our code before a learner sees them.
- Grounded explanations: course facts in each explanation must come from that course’s catalog entry.
- Always a plan: if the AI layer is unavailable, a deterministic rules engine builds the path, so no learner is left without one.
Technical learning assistance (next)
The next stage is help inside each step: answering questions about the code, circuits and debugging in the course a learner is taking, grounded in that course’s content, and re-planning the path as the learner progresses. This is in development and not live yet.
Current pilot status
- Early pilot, started in 2026 and connected to EET Academy.
- The assessment, learner profiling and ordered paths work end to end in production today.
- Current catalog: 42 courses, Arduino-first, plus some ESP32 and embedded courses.
- The Claude reasoning layer is integrated in the product; paths are generated by the rules engine until Claude API access is switched on.
Who’s behind it
EET Personalized Learning AI is a new venture started in 2026 by Ashraf AlMadhoun, engineer, educator and founder of Educational Engineering Team (EET). EET has taught Arduino, embedded systems, electronics, IoT and related engineering topics online since 2007. The new product turns that library into a personal path for each learner.
Try it
Take the assessment to get your path, or see the live product page.