Students shape their own ideas, moving between the screen and a project in their hands. Teachers run the session, follow the thinking as it happens, and build activities around what their class actually cares about.
Practice concepts before code
Students describe how their project should work, building logic before they know the terms.
Explain AI-written code in their words
MicroChat builds the code, then students predict what it will do before they load their micro:bit.
Get hands-on with errors and successes
Students interact with their coded micro:bit project, working out how to fix it or make it better.
Build bigger than their skill level
Students collaborate with AI as a partner to build ideas that used to be out of reach.
How To Run Your First Class with MicroChat
No new hardware and no setup. Students open a browser, join your session, and describe what they want your micro:bit to do. Here's what that looks like.
Step 1 · They describe their idea
Student
Make my micro:bit count how many times I shake it.
Step 2 · MicroChat asks back
MicroChat
What do you think happens if you shake it gently?
Step 3 · They answer, then run it to find out
Student
I think it will only count hard shakes. Make it count gentle and hard shakes.
Step 4 · They load it on their micro:bit
Students see their idea working, or not, on the board in front of them. MicroChat helps them debug and expand their code.
Works with any micro:bit, V1 or V2
Nothing to install and no student logins
Block and JavaScript code, both editable
Edit the code by hand in MakeCode, then bring it back
Same conversation, different output. Students describe a game, get working code, and play it on a CodeCTRL to find out whether it does what they pictured.
Step 1 · They describe their idea
Student
Make a game where my character catches falling apples and loses a life if one hits the ground.
Step 2 · MicroChat asks back
MicroChat
How long should the gap be between apple drops?
Step 3 · They answer, then run it to find out
Student
Start at 1 second, then shorten after every 10 catches.
Step 4 · They play it on a CodeCTRL
Students plug the micro:bit into a CodeCTRL and play the game they built. First draft rarely survives, so they go back and tune it.
Plays on CodeCTRL or any micro:bit display shield
Nothing to install and no student logins
Block and JavaScript code, both editable
Edit the code by hand in MakeCode Arcade, then bring it back
Once the micro:bit alone stops being a challenge, plug in the hardware. MicroChat codes 16 smart sensors, modules, and motors across the Forward Education ecosystem, so students progress to interactive projects that can solve real-world problems.
Student
Use the soil moisture sensor and the water pump to water my plants when the soil dries out.
MicroChat
How dry should the soil get before running the pump, and how long should the pump run?
Every one starts from something a teacher or coordinator is already dealing with. Pick the situation that sounds like yours.
Starting from zero with a class set
Thirty micro:bits in a bin. Students describe what they want and get code that runs.
Teaching coding without a CS background
MicroChat explains the code. You facilitate the thinking.
No district AI policy yet
No student accounts, no student data, and a privacy agreement ready for IT.
Running the same projects every year
Students describe a project of their own instead, so everyone owns what they built.
Pushing a kit past the lesson plan
Students combine sensors the lesson plan never did, then find out whether it works.
Differentiating a mixed-readiness class
Fast finishers keep building while stalled students get scaffolded support.
Teaching prompting as an actual skill
A vague prompt gets code that misses, and the micro:bit shows it in three seconds.
Making AI concrete instead of abstract
Students see the decisions the system made, and where it went wrong.
Getting students to question the output
MicroChat challenges students to predict and explain what the code is doing.
Introducing a concept before the syntax
Students use computational thinking without realizing it.
Teaching debugging on purpose
Change one value, run it, and see what works and what breaks.
Moving students from blocks to text
Iterate in blocks, then see the same program in JavaScript.
Grading the thinking, not the artifact
The session keeps what students predicted, got wrong, and changed. That is the evidence.
Showing which standards a unit covered
Pull real student conversations as evidence, on hardware you already own.
Showing families what their kid actually did
Open the chat at conferences and let a parent watch their kid fix AI-written code.
Beta
Anyone can build a micro:bit tutorial aligned to their curriculum
Tutorial Builder turns any prompt into a step-by-step MakeCode tutorial your students follow at their own pace. No coding background needed, and it is built for the kit you already own.
Students reach MicroChat through a shared session link and an anonymous alias. There is no account to create, no email to collect, and no student record to protect.
No student accounts, everStudents join through a share link and work under an assigned alias. MicroChat never asks for a name, an email or a login.
Personal information is removedIf a student types personal information into a chat, it is proactively avoided and removed before anything is stored.
Session history stays with the teacherEvery conversation and program is kept against the alias, so you can review the work later without the record ever naming a student.
Closed to micro:bit codingMicroChat will not write an essay or answer an off-topic question, and safeguards block harmful content in both directions.
COPPA and FERPA compliant
SOC 2 in progress
Data privacy agreement on request
MicroChat is an AI coding assistant designed for micro:bit classrooms. It helps students turn plain-language ideas into real, downloadable micro:bit programs they can run on physical hardware.
How is MicroChat different from ChatGPT or general AI chatbots?
MicroChat is purpose-built for education and micro:bit coding. Instead of open-ended conversation, it focuses on generating real, runnable code for hands-on classroom projects. This keeps students on-task and aligned with learning goals.
Is MicroChat safe for classroom use?
Yes. MicroChat is designed as a classroom-safe AI tool with guardrails that keep interactions focused on coding and project-based learning. It avoids wide-open chat behavior and stays aligned with educational use.
Can beginners use MicroChat for coding?
Yes. MicroChat supports beginners with simple prompts while allowing students to grow into more advanced programming concepts. This creates a low floor to start and a high ceiling for deeper learning.
Does MicroChat work with real micro:bit hardware?
Yes. Students can download MicroChat-generated code directly to their micro:bit and see their programs run using LEDs, sensors, buttons, sound, and motion.
Does MicroChat teach AI literacy?
Yes. MicroChat helps students learn how AI generates code, how prompt wording affects outputs, and how to test, debug, and improve AI-generated programs. This builds practical AI literacy skills.
Do teachers need coding experience to use MicroChat?
No. MicroChat is designed to support teachers without requiring deep coding expertise. It helps educators focus on facilitation, problem-solving, and learning outcomes instead of troubleshooting code.
Why teach AI with physical computing instead of screen-only tools?
Teaching AI with physical computing helps students connect code to real-world outcomes. When students use sensors, lights, and motion, they can see how AI decisions affect real systems. This makes AI concepts more concrete, engaging, and easier to understand.
What are the benefits of hands-on AI learning for students?
Hands-on AI learning improves problem-solving, critical thinking, and engagement. Students learn to test, debug, and refine real systems instead of passively consuming AI tools. Physical projects also help students understand cause-and-effect relationships in AI systems.
What is the best way to teach AI literacy in K-12 classrooms?
The most effective way to teach AI literacy is through project-based learning. Students should create, test, and improve real code while exploring how AI generates outputs, responds to prompts, and interacts with real-world inputs like sensors and data.
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Back-to-Back Connector Building Blocks $8.00 USD /
Base Plate $8.00 USD /
Breakout Board $20.00 USD /
