What can the model see?
Compare labelled examples and identify the features the model can use before it makes a prediction.
Built for schools · designed for doing
Don't just teach the future.
Let students
build it.
WieLearn brings curriculum, browser-based labs, teacher workflows, and visible student outcomes into one school-ready learning system for ages 8–18.
NOTEBOOK LAB● RUNNING
accuracy = model.score(test_data)
print(f"Trust score: {accuracy:.0%}")OUTPUTTrust score: 86%
OUTCOMEMODEL BUILT✓
A computing class shouldn't end at“Understood.”
Schools already have the timetable, the teachers and the intent. What is usually missing is the bridge between explaining a concept and letting every student use it for real.
The shiftSame school period.A completely different outcome.
What often happensWhat students should do
- 01
Watch AI on a slide
Train and test a model in Notebook Lab
- 02
Copy code from the board
Run it, break it, debug it—and explain why
- 03
Submit one more worksheet
Ship a project with evidence and teacher feedback
The bell rings.The learning keeps moving.
THE PROMISE
WieLearn fits the school day schools already have. What changes is what students do inside that period—and what teachers know by the end of it.
- 01BEFORE THE BELL
TEACHER · PREPARE
The teacher opens a ready lesson.
A mapped objective, classroom prompts and the linked lab are already together—ready to adapt, not rebuild. - 02FIRST 8 MIN
CLASS · UNDERSTAND
Start with one sharp idea.
The class gets a short concept brief, examples and a question worth investigating—not a forty-slide lecture. - 03NEXT 25 MIN
STUDENT · BUILD
Every student builds in the browser.
They run the experiment, change inputs, make mistakes and test again while the teacher can see where support is needed. - 04FINAL 12 MIN
SCHOOL · PROVE
Finish with an explanation—not a tick mark.
Students submit the project and explain the result. The teacher leaves with attempts, reflections and visible evidence.
18 LABS · ONE BROWSER
Not software students watch.Tools they operate.
Each lab opens from the lesson, works without a complicated local setup and returns the student's process to the teacher.
NO INSTALL QUEUETEACHER-LINKEDWORK AUTO-SAVED
wielearn.school / labs / notebook● RUNNING
STUDENT WORKSPACEAUTO-SAVED
01import pandas as pd02from sklearn.ensemble import RandomForestClassifier03from sklearn.model_selection import train_test_split04data = pd.read_csv('data/student_outcomes.csv')05X = data[['study_hours', 'quiz_score', 'attendance']]06y = data['completed_project']07X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42)08model = RandomForestClassifier(n_estimators=100, random_state=42)09model.fit(X_train, y_train)10confidence = model.predict_proba(X_test.iloc[[3]]).max()
✓ Cell completed in 1.2sValidation accuracy 86%
TRUST SCORE86%✓ Model + reflection saved
The video starts the idea.The school journey continues it.
AIforKids gives students clear, free explanations they can revisit. WieLearn connects those lessons to classroom labs, teacher guidance and evidence of what each student actually understood.
One learning pathway—not two disconnected products.01 · WATCHAIforKids lessonA clear concept explanation students can replay.
→02 · BUILDWieLearn labThe idea becomes an experiment inside the browser.
→03 · PROVETeacher-visible outcomeThe project and explanation return to the classroom.
CLASS 10 · CBSE AI 417
Six connected course pathways
Public explanations outside class. Structured progression inside school.
- 01CONCEPT + REFLECTION
Introduction to AI
Recognise where AI appears, what it can learn and what it cannot decide alone.
- 02NOTEBOOK LAB
AI Project Cycle
Move from problem scoping and data collection to modelling, testing and explanation.
- 03DATA VIZ LAB
Data Sciences
Read datasets, find patterns and ask whether the available data is good enough.
- 04VISION EXPERIMENT
Computer Vision
Explore how machines represent images and test a small visual classifier.
- 05NLP NOTEBOOK
Natural Language Processing
See how text becomes machine-readable and build a simple language workflow.
- 06MODEL REVIEW
Evaluation
Use accuracy and a confusion matrix to judge results, limitations and fairness.
One school day.Three views that make sense.
Students should know what to do next. Teachers should see where help is needed. School leaders should know whether the programme is actually working.
01STUDENT VIEW
TODAYClear next steps—not another dashboard.
Every lesson opens with one focused path from explanation to evidence.
AI PROJECT CYCLE12 MIN LEFT
- 1
Watch the concept
DONE - 2
Run the lab
OPEN - 3
Explain the result
NEXT
02TEACHER VIEW
LIVE CLASSSee the room while learning is happening.
Assign the lesson, spot blockers and respond before the period is over.
CLASS 10A24 students active
LESSON LIVEAS
Aarav S.
BuildingMR
Meher R.
Needs a nudgeZK
Zoya K.
Submitted03SCHOOL LEADER VIEW
THIS TERMFrom “we introduced AI” to evidence you can show.
See curriculum movement, classroom participation and completed student work without asking teachers to prepare another report.
CURRICULUM4 of 6pathways in motion
CLASSROOM8active sections
EVIDENCE↗projects ready to review
ONE CONNECTED RECORDLESSON → LAB → FEEDBACK → OUTCOME
Bring us one teaching period.See what the whole system changes.
We’ll use your context—not a generic sales script—to show how WieLearn connects curriculum, browser labs, teacher guidance and visible student outcomes.
NO PREPARED PITCH REQUIRED
Just bring the grade, subject and classroom constraint you want to solve.