Start with one thing you want students to learn — or demonstrate — better.
Reasonline helps higher-education instructors design more purposeful AI-supported learning and gather stronger evidence of student capability without rebuilding an entire course.
Use Reasonline Learn when students need better opportunities to practice, apply, explain, critique, or make decisions. Use Reasonline Assess when the finished work leaves an important question about what the student can demonstrate. Use Reasonline Complete when development and demonstration need to connect.
My problem is…
Start with Reasonline Learn
EARLY ACCESSAI created two problems for instructors at once.
The first is a learning problem. Students have access to extraordinarily capable AI, but simply giving them access does not determine what they should practice, when AI should help, when it should challenge them, or what intellectual work should remain theirs.
The second is an evidence problem. A strong paper, project, recommendation, presentation, or case can still matter. But when AI or other support may have helped shape the final product, the artifact may no longer answer every question the instructor has about the capability behind it.
Reasonline is designed so you do not have to solve those problems with the same tool or on every assignment.
If the problem is learning, start with Learn.
Maybe students need more practice applying a framework. Maybe they need to work through a professional scenario. Maybe they need to critique an AI-generated recommendation instead of producing another generic response. Maybe they need to explain a difficult concept, defend a decision, or reconsider weak evidence after challenge.
Reasonline Learn helps you build those experiences around what students actually need to become capable of doing. You can begin with one capability and one Learning Experience. You do not have to redesign the course.
If the problem is evidence, start with Assess.
Maybe you still value the assignment but wish you could have a short follow-up conversation with every student. What evidence did they rely on? Why did they make that choice? What would happen if one condition changed? Can they explain the recommendation rather than simply submit it? Can they revise the reasoning when challenged?
Reasonline Assess creates that structured adaptive follow-up at a scale that would otherwise be difficult to provide. The result is another source of evidence — not an AI-authorship verdict and not an automated academic decision.
If the two problems are connected, use Complete.
Students may first develop a capability through AI-supported practice, feedback, simulation, critique, or adaptive challenge. Later, you may want another signal under a different assessment condition. Reasonline Complete lets those forms of evidence connect to the same capability while preserving the difference between them.
Explore Reasonline CompleteUse Reasonline where it actually adds value.
You probably do not need Reasonline on every assignment. And not every activity needs AI. The better question is: Where would another meaningful learning interaction or another source of evidence materially improve the course?
What this might look like in your course
One capability
Evaluate a recommendation using relevant evidence.
One experience
A Learning Experience or Assess follow-up.
Inspect evidence
Look at what the interaction actually produced.
Decide
Expand only when the value is clear.
The technology is not the organizing principle. The capability is.
You stay in control of the course.
Faculty determine what matters. That includes the capabilities, materials, objectives, learning boundaries, assessment criteria, number and depth of assessment rounds, response formats, and where Reasonline belongs at all. Reasonline can adapt inside those boundaries without silently deciding what the curriculum should become.
Better AI use does not require surrendering the thinking.
AI can be useful without doing the student’s intellectual work. A Reasonline interaction might challenge an assumption, change a scenario, ask for evidence, request an explanation, offer an appropriately bounded hint, or prompt a revision. The objective is not to keep the student talking to AI. The objective is to create the kind of thinking the learning goal requires.
Assessment without pretending AI can deliver certainty.
Reasonline Assess does not need to tell you whether AI “really wrote” the assignment. It gives the student another opportunity to demonstrate something. The question becomes less: Can software identify how the artifact was produced? and more: What can this student demonstrate now?
Learning Intelligence without another dashboard to manage.
Reasonline can help surface what the available evidence suggests might be worth your attention: a recurring conceptual mistake, a capability with very little evidence, success with substantial support but difficulty when support changes, or an assessment that asks students to do something they were given few opportunities to practice.
No recommendation without an explanation. No insight without an action.
Explore Learning IntelligenceKeep the LMS you already have.
Your LMS can continue handling content, deadlines, grades, enrollment, announcements, and course administration. Reasonline adds a different layer around purposeful AI-supported learning, capability evidence, adaptive assessment, and useful next-step decisions.
Explore integrations →Three ways to begin
If you want better AI-supported learning, request Learn Early Access. If you want stronger evidence around an existing assessment, try Assess free. If you want development and demonstration connected, explore Reasonline Complete.
You do not need more AI in your course. You need better reasons for using it.