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EDUCATION · FOR TEACHERS

Teach the class where everyone ships.

Project-based learning is powerful but hard to run and harder to assess — especially now that AI can write the homework. Cartara gives you both: a project library ready to assign, and visibility into what each student genuinely understands.

PROJECT-BASED BY DEFAULT

Assign projects, not problem sets

Pick from the project library — each with difficulty, estimated time, prerequisites, and the skills it teaches — or sequence several into a course. The build is the curriculum.

REAL UNDERSTANDING, VISIBLE

See what students actually know

Cartara turns each student's work into a knowledge graph backed by comprehension checks on the code they wrote. You see understanding per concept, not just completed submissions.

AI IN THE CLASSROOM, HONESTLY

Teach with AI instead of against it

Students build with modern AI tools the way professionals do. Because Cartara measures comprehension against the real code, AI assistance becomes a teaching instrument — not an integrity problem.

MEET STUDENTS WHERE THEY ARE

One classroom, many levels

Beginner, intermediate, and advanced tracks of the same project library let a mixed-ability class work on different projects while you track everyone on one map.

THE TEACHER'S VIEW

Signals you can act on

Concept coverageWhich parts of the curriculum each student has genuinely exercised in real code.
Comprehension evidenceShort, evidence-linked checks tied to the student's own diffs — not generic quizzes.
Progress over timeHow each student's knowledge graph grows across projects and terms.
Where to interveneConcepts a student keeps leaning on AI for without understanding — surfaced early.

Frequently asked questions

How do you assess coding assignments when AI can write the code?
Cartara measures comprehension of the code a student actually shipped, not just whether it runs. Short, evidence-linked checks tie back to the student's own diffs, so you see which concepts each student genuinely understands — even when they used AI to help build. Output stops being the proxy for learning; understanding becomes the measure.
Can students use AI tools in my class without it becoming cheating?
Yes — that's the point. Students build with the same AI assistants professionals use, and because comprehension is measured against the real code rather than the finished artifact, AI assistance becomes a teaching instrument instead of an integrity problem. You can see where a student leaned on AI without understanding, and step in early.
What can I actually see about each student?
A per-student knowledge graph backed by comprehension checks: which concepts they've genuinely exercised in real code, how their understanding grows over time, and where they keep relying on AI without grasping the underlying concept. It's a map you can inspect concept by concept, not a single grade you have to trust.
What does a project-based class look like on Cartara?
You assign projects from the library — each with difficulty, estimated time, prerequisites, and the skills it teaches — or sequence several into a course. Students build the projects, and their work becomes the knowledge graph you track understanding against, so class time can go to mentorship and feedback instead of grading output.
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