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EdTech and learning platforms

AI integration for EdTech and learning platforms

A learning platform owns an asset almost no competitor can copy: years of proprietary content, already-graded submissions, and a trace of what each student struggled with. The sector's most expensive mistake is bolting a generic model on top of that and ending up with a chat that answers well about the world and badly about the course.

The second mistake is unit economics. In EdTech usage is high and revenue per student is low, so an AI feature without a cost-per-active-student figure calculated up front eats the margin of the plan that sells it. The two decisions that shape the project — what content it retrieves and what each answer costs — are made before choosing a model.

Short answer

In EdTech the first thing to integrate isn't a tutor: it's search over your own content, with a citation to the source. Then come the course-scoped tutor, assisted grading and item generation. The grade on the record and any academic-integrity case are always signed by a teacher.

What we automate first

01

Cited search over your own content

Retrieval over your courses, transcripts and materials, citing the module and the timestamp in the video. It's the foundation for everything else: without it, any tutor answers from the model's memory.

02

Course-scoped tutor

Answers from the unit's material, says "that isn't in this course" instead of filling in the blank, and won't do the graded assignment: it walks the student to the step before and stops there.

03

Rubric-based assisted grading

Returns a draft grade and criterion-by-criterion feedback, quoting evidence from the student's own text. The teacher adjusts and signs. This is where the lost hours actually are.

04

Item generation and question banks

Builds new questions from the material, aligned to the stated learning objective and with plausible distractors. They go through review before publishing.

Use cases in this industry

Each one explains a concrete implementation: how it works end to end, which number moves, and what has to exist on your side before starting.

Rubric-based AI assisted grading

How to implement AI assisted grading in a learning platform: a draft grade per criterion, evidence quoted from the student, and the teacher's signature.

Semantic search over learning content

How to implement semantic search in a learning platform: over transcribed video and your own material, citing the timestamp and respecting the student's scope.

Where to start

01

Inventory the content: what's text, what's untranscribed video, what has no metadata. That inventory defines the project's real scope.

02

Start with cited search — it's measurable without touching assessment and already changes the student experience.

03

Calculate cost per active student against real traffic before opening the feature to the whole base.

04

Add assisted grading in a single subject, with the teacher signing, and extend only once suggested and final grades converge.

What it integrates with

  • LMS: Moodle, Canvas, Google Classroom
  • LTI 1.3, xAPI and SCORM
  • Student information system (SIS)
  • Your own application and its API
  • Content storage and video transcription
  • Vector store and model providers

What stays under human approval

  • The grade that goes on the record, always.
  • Any plagiarism or academic-integrity case.
  • Content published to underage students.
  • Progression, certification or withdrawal decisions.

When it isn't worth it

  • If the product doesn't retain yet, an AI tutor won't fix it. AI amplifies content that works; it doesn't replace content that doesn't.
  • If the material lives in scanned PDFs and untranscribed video, the first project is content normalization, not AI. A model doesn't do that work.
  • If the plan selling the feature costs less per month than the estimated model cost per active student, there's no product — there's a loss-making promotion. That number gets calculated before building.
  • If your audience is minors, the legal framework — COPPA, FERPA, GDPR — defines the architecture before the model does. Adding it afterwards means rebuilding.

Related questions

How do you keep the tutor from making things up?
By scoping retrieval to the course material and requiring a citation: if no passage supports the answer, the tutor says so instead of filling in. We test that with a set of questions whose correct answer is "it isn't in the material" — precisely the case almost nobody evaluates.
What does the feature cost per student?
It depends on message volume and context length, but it's estimated before building, against the platform's real traffic. In production the cost is controlled with caching, tight retrieval and a small model for most queries, reserving the large one for what genuinely needs it.

Notes on this industry

Reliability4 min read

Why RAG fails on educational content

The five reasons retrieval over course material fails more than over technical documentation, and what has to change in how the content is prepared.

Next step

What education product should exist next?

Tell us what you are building, what is not working yet or which AI opportunity you want to evaluate. In the first conversation, we will tell you where we would start, what it requires and what we would not build.

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