Students already use AI
The real choice is not whether AI shows up in your class. It is whether students use it with your material and your teaching goals, or with whatever generic answer a chatbot gives them first.
Ellide prepares your slides, readings, handouts, and syllabi for the AI tools students already use, then adds optional teaching guidance so the conversation stays grounded in your materials instead of drifting into generic answer-dumping.
Log in to save generated documents and track credits. Your dashboard stores OCR outputs, study guides, and Tutor Packs.
Students already use AI
The real choice is not whether AI shows up in your class. It is whether students use it with your material and your teaching goals, or with whatever generic answer a chatbot gives them first.
Raw uploads flatten your course
A messy PDF upload turns your carefully structured syllabus, reading packet, or lecture deck into a vague prompt. The AI misses emphasis, context, and the way you want students to reason through the material.
Teaching posture matters
When the shortest path is "just tell me the answer," students take it. Ellide lets you push the AI toward guided questioning, source-based explanation, and better habits.
The signature difference
Ellide is easiest to understand when you see the output change. The student asks the same thing, but the prepared document nudges the AI toward the way you want it to teach.
Student prompt
"Can you explain the Week 4 reading so I know what to write about buyer power?"
Without Ellide
Document context seen by the AI
AI response
Porter's Five Forces is a business framework used to analyze competition in an industry.
The five forces are competitive rivalry, supplier power, buyer power, threat of new entrants, and threat of substitutes.
For your assignment, you can explain that buyer power affects pricing because customers can choose alternatives.
The answer is technically fine, but it behaves like a search result. It does not know what the instructor emphasized, and it gives the student a finished answer too quickly.
With Ellide
Document context seen by the AI
AI response
Before I explain it, which force did the Week 4 packet connect to switching costs?
Look at slide 17 first: the key idea is that low switching costs make buyers more powerful because they can leave easily.
Try applying that to the airline example on page 11. What would change if customers had fewer realistic alternatives?
The student still gets help, but the AI starts from the assigned material, asks for reasoning, and keeps the conversation inside the course.
How it works
01
Syllabi, lecture slides, handouts, scanned readings, old study guides, and course packets.
02
Ellide cleans the material, preserves the structure that matters, and can apply optional teaching instructions that shape how the AI behaves.
03
Students use the prepared output in the AI tools they already know, but the experience now reflects your material and your preferred tutoring style.
Invisible teaching instructions
Instead of exposing a prompt engineering workflow to students, Ellide lets teachers define the behavior they want and bakes that guidance into the AI-ready document.
Teacher controls
What that creates
Student experience
"Before we jump to the answer, what does your lecture slide say about demand uncertainty in the Zara example? Start there. If you want, I can help you compare it to the framework from the Tuesday handout."
Students get a tutor that sounds like the course, not the public internet.
You set the teaching posture once, then reuse it across syllabi, slides, readings, and study guides.
The visible document stays clean while the AI receives your guidance in the prepared output.
For students
Students do not need to think about hidden instructions or formatting choices. They just get cleaner, more grounded material to study from: lecture slides, syllabi, readings, scanned PDFs, and study guides that actually work in AI chats.
Student-friendly use cases
Trust and privacy
Ellide is designed so course material is processed for the task at hand, not used to train foundation models.
The product goal is to let teachers prepare the material once and let students use the result without turning them into prompt engineers.
The prepared output is meant for familiar AI interfaces, so adoption does not depend on a whole new classroom workflow on day one.
Ready to go deeper?