Reasonline Complete — Learn, AgencyThread, and Assess connected.
One evidence record for coursework done in the age of AI.
Learn supports students while they work. AgencyThread supplies evidence about how the work was made. Assess supplies evidence about what the student understands. Complete connects all three around one evidence packet per assignment, with Evidence Context and Learning Intelligence.
Students assemble their evidence once. You interpret each kind of evidence in the context of the assignment, without Reasonline pretending they mean exactly the same thing.
Learn with support. Show how the work was made. Stand behind it.
Want to start with Learn and Assess? The free trial covers one Learn activity and one assessment — start with Learn or start with Assess.
Learn
Formative evidence, AI-supported
Assess
Another signal, different condition
Shared capability evidence
Connected, context preserved
Learning Intelligence
Possible next action
AI changed learning and assessment at the same time.
Higher education is often treating them as separate problems. One conversation asks: How should students learn with AI? Another asks: What does student work still tell us when AI may have helped produce it? Those questions are related.
If students increasingly learn with AI, then educators need better ways to design that learning. And if AI changes what the finished artifact can tell us, educators may sometimes need another source of evidence. Reasonline Complete was built around the connection.
Learn with support.
Reasonline Learn
Students should be able to ask questions, receive feedback, and revise their work. Reasonline Learn designs those experiences around meaningful course capabilities.
Explore Reasonline LearnReasonline Learn — formative evidence
Condition
AI-supported practice, feedback available
Show how the work was made.
Reasonline AgencyThread
Students submit finished work with the process behind it: AI conversations, drafts, version history, sources, and their Learn records. AgencyThread reports, dimension by dimension, where the student’s own judgment shows in that record, and says plainly what is missing.
Explore Reasonline AgencyThreadReasonline AgencyThread — process evidence
Evidence packet
Final artifact, two AI conversations, draft 1, revision history
Reasonline Assess — another signal
Condition
Reduced-support assessment, different constraint
Then add another signal when it matters.
Reasonline Assess
Reasonline Assess creates a structured adaptive follow-up grounded in the task. With AgencyThread, George can add clearly marked process questions, each tied to a specific piece of the student’s evidence and never to the report’s conclusions. The report and the dialogue stay separate records for you to compare.
Explore Reasonline AssessReasonline does not need to declare one form of evidence “real” and the other “invalid.” It needs to preserve the difference.
Evidence is more useful when its context stays attached.
Evidence Context
Reasonline Complete preserves information about the conditions surrounding the evidence. The same performance can support different interpretations depending on whether it occurred during AI-supported practice, as part of a team, with reduced support, or under another defined assessment condition.
What did the student demonstrate — and under what conditions?
Explore Evidence ContextAI-supported practice
Critiqued a weak AI-generated recommendation, received challenge, reconsidered an assumption, and revised.
Assessment — reduced-support condition
Completed a major recommendation and responded to Reasonline Assess questions about the evidence, trade-offs, and what would change under a new constraint.
Capability → Learning → Process → Assessment → Next step
Then ask what the evidence suggests next.
Learning Intelligence
Once learning and assessment evidence are connected, another question becomes possible: What might be useful to do next — and why? For the student, that may mean more practice, greater challenge, or another attempt with less support. For the instructor, it may mean a recurring misconception, missing evidence, or an inconsistency worth reviewing. For the course, it may mean that students are being assessed on something they had little opportunity to practice.
Evidence → Rationale → Possible action
Recommendation
Review the assessment's alignment with prior practice.
- Why
- Several students show strong critique capability during Learn but low scores on the related Assess follow-up.
- Evidence
- 4 of 6 students in this cohort had no Learning Experience covering the assessed sub-skill before the assessment.
- Possible action
- Add a short Learning Experience for the missing sub-skill before the next assessment window.
Complete does not turn every learning activity into an assessment.
Learning is for development. It should allow support, feedback, revision, experimentation, and productive mistakes. Assessment asks a different question: what can the student demonstrate under the conditions the instructor has chosen? Reasonline Complete connects those forms of evidence without confusing their purposes.
That is why the goal is not one continuous mastery score. The goal is a more useful evidence record.
A different response to AI
The answer to generative AI does not have to be banning it or assuming everything produced with AI tells us nothing. A more useful academic model can let AI support learning where it helps, preserve the context of that support, add another assessment signal when another condition would improve judgment, connect the evidence, and use it to make a better next decision.
Complete for instructors, programs, and institutions
For instructors, start with one Learning Experience, one important assessment, or one capability that appears in both. For programs, create shared principles around capabilities and evidence without forcing identical pedagogy. For institutions, explore a coherent academic model for AI-supported learning, evidence, and assessment alongside existing infrastructure.
Human judgment remains the center.
Reasonline can help generate learning interactions, ask adaptive questions, organize evidence, identify patterns, and suggest possible next actions. Those functions should support academic judgment — not quietly replace it.
Request Reasonline Complete Early Access
Complete now includes Learn, AgencyThread, and Assess. While we measure the cost of AgencyThread reports on realistic student evidence, Founding Early Access for Complete is offered as a written quote with a stated report allowance.
Requested Complete at $229/year before this change? You keep $229/year for your first two years.
Start with a defined pilot.
A typical early Complete pilot may involve approximately 5–10 faculty, selected courses or capabilities, up to roughly 500 students, and one academic term. The purpose is to answer meaningful questions about academic value, faculty experience, student experience, evidence quality, implementation, and whether expansion would be worthwhile.
$7,500–$10,000 per academic term
depending on scope. Each pilot is quoted against its workload; a shared evidence packet can cut onboarding effort even as AgencyThread adds processing.
Plan a Reasonline pilotReasonline gives instructors evidence of how the work was made and of what the student understands.
Reasonline Complete connects them with the learning behind the work.