Don’t just give students AI. Design what they do with it.
Reasonline Learn helps higher-education instructors turn the course they already teach into purposeful AI-supported learning experiences.
Start with what students should become capable of doing. Then design opportunities to practice, apply, simulate, critique, explain, and respond to challenge — with AI working inside goals, materials, and boundaries you define.
Course Studio — Learning Experience
Capability
Diagnose a leadership problem and defend a recommendation.
AI access is not an AI pedagogy.
A blank chatbot leaves most of the important decisions to the student. Reasonline Learn puts instructional design back around the AI.
Blank AI access
Reasonline-designed experience
- → Capability defined
- → Source material provided
- → Boundaries set by the instructor
- → Interaction pattern chosen on purpose
Start with what students should become capable of doing.
Bring the syllabus, assignment, or objective you already have. Reasonline helps make the capability embedded in it more explicit.
Pricing
Evaluate a pricing decision using customer, competitor, and financial evidence.
The capability is the connective tissue between what students learn, what they practice, and what evidence the experience creates.
What this can look like in a real course
The goal is not to keep the student chatting with AI. It’s to create the interaction the learning objective actually requires.
1. Capability defined
Evaluate a strategic recommendation using relevant evidence and identify the trade-offs that matter.
2. Student sees a weak recommendation
An AI-generated recommendation with incomplete evidence and an unstated assumption.
3. George challenges the assumption
4. Evidence captured
Evaluate a recommendation with evidence
Identified the missing evidence and revised the recommendation.
That is the difference between giving students AI and designing learning with AI.
Different learning goals call for different kinds of interaction.
Some objectives need only a focused exchange. Others benefit from sustained reasoning across several turns — interaction depth should follow the learning objective, not the novelty of the AI.
Learn
Work through an idea with explanation and checks for understanding.
“Walk through why this assumption matters.”
Practice
Another meaningful chance to apply a skill.
“Try that calculation again with new numbers.”
Apply
Move from knowing a concept to using it.
“Apply this framework to your own case.”
Simulate
Put the learner inside a scenario.
“You're in the client meeting. Respond.”
Critique
Evaluate evidence, arguments, or AI output.
“What's weak about this recommendation?”
Discuss
Explore a tension or trade-off.
“What would you give up either way?”
AI that works inside your course — not above it.
You establish the important capabilities, course materials, learning objectives, and instructional boundaries. Reasonline adapts inside that environment rather than silently deciding what the course should become.
The interaction can become part of the evidence.
Reasonline Learn is designed to capture what a student actually demonstrated while learning — not just whether they completed it.
Diagnose a leadership problem
Identified the root cause, compared two responses, and defended a recommendation after challenge.
Not all evidence means the same thing.
AI-supported practice
A student succeeds while AI support and feedback are available — meaningful, but a different question than a differently configured condition.
Reduced-support assessment condition
The same capability, examined later with less support — Evidence Context preserves both rather than treating them as interchangeable.
See what the evidence suggests next — and why.
Recommendation
Try another application with less support.
- Why
- Several supported learning experiences show strong application, but little evidence under a reduced-support condition yet.
- Evidence
- 3 supported Learning Experiences completed with strong critique and revision after challenge.
- Possible action
- Assign one more Learning Experience with less scaffolding before summative assessment.
When it matters, change the evidence condition.
Learning is supposed to involve support. Assessment can be configured differently — that is why Reasonline Learn works with Reasonline Assess.
Explore Reasonline CompleteWhat Reasonline Learn is built for
Learn is especially useful where students need to reason, explain, apply, critique, or make judgments — not simply produce a finished answer. It is not a generic chatbot beside the course; it is a learning environment organized around what students should become capable of doing.
Keep the LMS you already have.
Canvas, Blackboard, D2L, Moodle, or another LMS can continue handling the familiar academic infrastructure around the course.
Explore integrations →You do not have to redesign an entire course.
Start with one thing students need to learn better. Build one experience. Look at the evidence it creates. Then decide whether Reasonline belongs anywhere else.
Request Learn Early AccessDesigned for more than one discipline.
A business course might ask students to evaluate a recommendation. A health program might use simulation and decision-making. The same architecture applies across disciplines around different capabilities.
Early Access
Founding Instructor Early Access
$149/year for your first two years
Learn for your own courses, standard AI usage, Learning Experiences, Learning Evidence, Evidence Context, available Learning Intelligence capabilities, onboarding, and new Early Access capabilities.
Request Learn Early AccessPrograms & schools
From $3,500 per academic term
Coordinated Learn pilots, depending on scope.
Plan a Reasonline pilotFrequently asked questions
Is Learn an LMS?+
Not in the traditional sense. Learn includes course-building and learning-environment capabilities, but Reasonline is not primarily trying to replace enrollment, gradebook, deadlines, or campus administration. It is designed to work alongside that infrastructure.
Is this just an AI tutor?+
No. Tutoring may be appropriate in some experiences, but Reasonline Learn can also support practice, application, simulation, critique, discussion, explanation, and adaptive challenge. The interaction should follow the learning objective.
Can students just ask for the answer?+
Learning Experiences operate inside instructor-defined goals and boundaries. Depending on the experience, Reasonline can question, challenge, coach, provide bounded help, or request explanation rather than completing the student's intellectual work.
Does Learn automatically grade students?+
No. Learning Intelligence may surface evidence, recommendations, and patterns. Consequential academic judgments remain human decisions.
Do I have to move my whole course into Reasonline?+
No. Start with one capability and one Learning Experience.
Is Learn finished?+
No. Core workflows are operational, but Learn is in Early Access. That status is intentional: Reasonline is still learning from real instructors, courses, and students before treating the product as generally available.
Better AI use starts with better learning design.
Start with one thing you want students to learn better.
Request Learn Early Access