Build capability. See the evidence. Know what comes next.
Reasonline is a higher-education learning and assessment platform designed for a world in which AI is changing both how students learn and what their finished work can tell us.
Create stronger evidence of what students can demonstrate. Help them learn purposefully with AI. Connect evidence from learning and assessment so students, instructors, and courses can make better next-step decisions.
An evidence-centered approach to learning and assessment for the AI era.
Reasonline Learn — Learning Experience
Capability
Evaluate a recommendation using relevant evidence.
Reasonline Assess — Evidence record
Adaptive follow-up
Recommendation
Try a reduced-support scenario next.
- Why
- Strong evidence during supported practice.
- Evidence
- 3 Learning Experiences, consistent critique.
- Possible action
- Assign one Assess follow-up before summative review.
AI changed how students learn — and what finished work can prove.
A polished submission may reflect meaningful student capability, effective use of AI, substantial outside support, or some combination of all three. That does not make the work meaningless — it means the artifact may no longer answer every question on its own.
The learning problem
Unstructured AI access
Designed learning interaction
How do we design better learning with AI?
Students need more than unrestricted chatbot access. They need purposeful opportunities to practice, apply, explain, critique, decide, and improve.
The evidence problem
Finished artifact
Another signal
How do we get stronger evidence of what students can demonstrate?
The finished work still matters. When it leaves an important question unanswered, another well-designed interaction can provide another signal.
Reasonline was built around the connection between those two problems.
Start where the artifact stops telling you enough.
Reasonline Assess — The artifact is one signal. Add another.
AVAILABLE NOWReasonline Assess creates a structured adaptive follow-up grounded in the task and, where appropriate, the student’s work. Students may be asked to explain a decision, apply an idea to a changed condition, or defend a recommendation.
The result is another source of evidence for academic judgment, available today. It makes no automated verdict about what the student “really knows.”
Reasonline Assess — George asks
Student’s submitted work
“We recommend entering the market in Q3 based on strong brand awareness.”
What evidence supports that awareness claim, and how confident are you?
Reasonline Learn — Learning Experience
Capability
Diagnose a leadership problem and defend a recommendation.
The other half of the problem is the learning itself.
Reasonline Learn — Don’t just give students AI. Design what they do with it.
EARLY ACCESS · FREE TRIALStart with what students should become capable of doing, then create experiences where they can practice, apply, simulate, critique, explain, revise, and respond to challenge.
Because those interactions connect to meaningful course capabilities, they can also create evidence of how students are developing — not merely whether they completed an activity.
Start from a goal and get a draft grounded in your own materials, or run a team challenge where each student decides privately, the team agrees a decision with George’s help, and every student defends it in their own words.
Three products. One evidence record.
Reasonline Learn supports students while they develop their work. Reasonline AgencyThread supplies evidence about how the work was made: the AI conversations, drafts, version history, and sources behind it. Reasonline Assess supplies evidence about what the student understands, through a bounded dialogue about the work. You interpret each kind of evidence in the context of the assignment.
Learn
Purposeful AI-supported learning around course capabilities.
AgencyThread
A report on where the student’s own judgment shows in the record of how the work was made. Pilots start January 2027.
Explore Reasonline AgencyThreadAssess
A structured adaptive follow-up grounded in the student’s work.
Start with what the student should become capable of doing — not a topic label, but a real capability statement.
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The question becomes less: Did the student submit something good? And more: What did the student demonstrate — under what conditions — and what should happen next?
Reasonline Complete — all three, connected.
EARLY ACCESSComplete brings Learn, AgencyThread, and Assess together around one evidence packet per assignment, so students assemble their evidence once. It keeps each kind of evidence connected without flattening them into one score or pretending they mean the same thing.
Learn
Formative evidence, AI-supported
Assess
Another signal, different condition
Shared capability evidence
Connected, context preserved
Learning Intelligence
Possible next action
Learn with support. Show how the work was made. Stand behind it.
Two capability layers make the evidence more useful.
Evidence Context
A strong performance during AI-supported practice and a strong performance under a separately configured assessment condition may both be meaningful — and answer different questions. Evidence Context preserves that difference — each piece of evidence keeps the conditions it was produced under: the AI-use rules, how George was helping, and whether it was team or individual work.
Explore Evidence ContextLearning Intelligence
Given the evidence available, what might be useful to do next — and why? Every recommendation opens onto the exact student work behind it, and patterns seen in too few students are watched rather than acted on.
No recommendation without the evidence behind it.
Explore Learning IntelligenceWhat this can look like in one course
An instructor wants students to evaluate a strategic recommendation using relevant evidence and identify the trade-offs that matter.
1. Capability
Evaluate a market-entry recommendation using relevant evidence and identify what trade-offs matter.
2. Learn
The student critiques an AI-generated recommendation containing incomplete data and an unstated assumption.
3. George challenges
George asks what cash-flow impact a sixty-day production delay would have. The student revises.
4. Evidence recorded
Assumption critique, evidence evaluation, and revision after challenge — recorded under an AI-supported condition.
5. Assess follow-up
On the capstone, Assess asks the student to defend one trade-off under a new regulatory constraint.
6. Recommendation
Learning Intelligence suggests a reduced-support scenario next, with the evidence and reason attached.
Keep the LMS you already have.
Your LMS can remain the academic home. Reasonline adds a layer focused on purposeful AI-supported learning, capability evidence, adaptive assessment, and useful next-step decisions.
Explore integrations →Built around human academic judgment.
Evidence should inform judgment — not replace it.
Trust & Security →Start with one thing that matters.
Try Assess free on one real assignment, or Learn free on one real activity. See whether the evidence is useful, and expand only when the value is clear.