Your course already exists. It just doesn’t answer back.
For program directors and deans who already run an online course. Alma's learning engineers add a way to stop a lecture and ask a question, spoken practice where there was only a quiz, and feedback that says why points were lost, inside the LMS you already use.
- Where it runs
- Any LTI-compliant LMS
- Start with
- One module
- Time to build
- Weeks
- Your team's time
- About a day of decisions
We add to the modules you have, under the instructor who teaches them.
Your course today is recorded lectures, a discussion board and quizzes, perhaps with a general chatbot switched on. Students in online programs describe courses like this as “teaching myself.” Everything we add works for the student and under the instructor. The AI teaching assistant answers from the course itself, and the instructor can see what it said. The instructor named on the course stays responsible for the course and the grade.
Six additions, each built from your own course.
Each goes into an existing module on its own. Take one, or all six.
Students can stop the lecture and ask
Your recorded lectures stay as they are. Each gets an AI teaching assistant that answers about the slide on screen.
Spoken practice where there was only a quiz
Each role-play with an AI character is built from one of your own cases. The student answers out loud, and the AI character pushes back on anything vague.
An AI tutor on each reading, there when the student asks
The student opens it as a one-to-one chat. It answers from the reading the instructor assigned.
Feedback that says why points were lost
Graded work comes back quoting the student's own words and naming what is missing. You choose how work is graded: AI-graded and human-confirmed, fully by your instructors, or fully by AI.
A considered reply to every discussion post
Replies come from the course's AI teaching assistant, labeled as AI in the thread, and from classmates.
Instructors see who is at risk while there is time to help
The course shows who has been active, who is flagged at risk, and where students stop. Faculty can open any student's progress during the term.
No migration. The course stays in your LMS, with your gradebook and your instructors.
Your platform
What we add opens inside the LMS you run, under your name and in your style. The student never sees Alma.
Your enrollments and gradebook
Nobody re-enrolls. Scores from the new activities go to the gradebook you already use.
Your course structure
Module order and prerequisites stay as they are. Every added activity carries an estimated time on task your team can check against your credit-hour policy.
The learning outcomes approved for the course
Every activity we add is traced to them.
Your instructors' role
You decide which roles stay human, including office hours. The AI teaching assistant supports the instructor's teaching and does not replace instructor interaction.
Your team can check everything we add against your own accessibility guidance before launch.
Start with one module. Then decide.
Before any of that, we build an example from your own material, at no cost. A pilot on one module then takes weeks to build, and the decisions take about a day of your team's time.
Step 1
You pick the module
A good choice is the module students ask the most questions about. Before we start, we agree what you will compare.
Step 2
We build the additions for that module
Alma's learning engineers build them in your LMS. Your faculty and learning designers approve each piece before students see it.
Step 3
You compare, then decide
Run it alongside the modules we did not touch and compare them on what we agreed. You pay a fee for the work, with no share of tuition, and you own what is built.
One module, before and after.
An illustration in words, with no client named.
Before
A recorded lecture and a 10-question multiple-choice quiz.
After: the lecture
The same recording, with an AI teaching assistant. A student stops at a slide and asks about it.
After: the practice
A spoken scenario with an AI character, built from a case in the module.
After: the feedback
Feedback quotes the student's answer and names what was missing. The instructor confirms the grade.
A chatbot in the corner is not teaching.
A general chatbot sits on every page of the LMS and takes any question. What we add is built for one course and placed inside its modules.
It knows where the student is in the course
The AI teaching assistant on a module 6 lecture knows which slide is on screen and what modules 1 to 5 covered.
It answers from the course itself
Answers come from your lectures, readings and cases. When something is not in the course, the assistant says so.
Practice is scored against a rubric your faculty approve
The rubric is yours, or one we draft for your faculty to approve. The score goes to your gradebook.
What a dean or program director will ask before a pilot.
What are students told?
The AI teaching assistant, the AI tutor and the AI characters are each labeled as AI wherever a student meets them. The instructor named on the course stays responsible for the grade.
Do faculty have to rebuild anything?
No. Alma's learning engineers do the production from the lectures, readings, rubrics and question banks the course already has. Your faculty and learning designers review and approve.
Can it go in mid-term?
Yes. Additions appear as new activities inside your existing modules. Completed work and recorded grades are left as they are. Whether a new activity counts toward the grade this term is the instructor's decision.
Who is this not for?
A team that wants a tool to make lessons itself this afternoon, or one chatbot for the whole LMS. A course with no lectures or readings yet should start as a new course.
Where to go if this is not quite your case.
Full course development
When the course does not exist online yet, or needs more than additions.
See new courses
Blended & faculty-led courses
When an instructor teaches the course live and you want practice around those sessions.
See faculty-led courses
Learning analytics and outcomes
When the first question is who is at risk and where students stop.
See learning analytics
How we work
Every step, with who does what and what your team approves.
See the full process
Bring one module of the course you already run.
Book a demo and we will walk through what would be added to that module and what the pilot costs.