adaptive learning paths. a layer on top of your existing lms that re-orders lessons for each student based on the questions they got wrong. same course, same instructors. the sequence changes per learner so weak spots get taught before the cohort moves on.
here is the problem this solves. a 4-month cohort starts with 120 learners and ends with 74. the ones who drop rarely fail on day one. they fall behind on module 3, never recover, and quietly stop showing up by module 6.
you can fix that with more instructor hours, or you can fix the sequence. adaptive paths pick the sequence. when a learner misses two questions on sql joins, the next thing they see is the joins remediation, not the next scheduled lecture.
#can ai raise cohort completion without hiring more instructors?
does ai for personalized learning paths need new teaching staff?
no. adaptive learning paths re-sequence your existing lessons around each student's wrong answers, so learners hit remediation before they fall behind. the instructors, the content, and the lms stay the same. you are changing the order lessons are served, not adding headcount or rebuilding the course.
the lever is order, not volume. most curriculum directors already have enough good content. what they lack is a way to serve it in the right sequence for a learner who is struggling in week 5 versus one who is coasting.
we have shipped this shape before. stennir built an lms extension for a 4-month-cohort online education company where rubric-driven automation cut grading time per cohort by 70% while instructor-quality nps held steady. same buyer, same lms-extension pattern that adaptive sequencing plugs into. more on how we run these engagements is on the consultancy page.
#do we have to rebuild our lms to do this?
no. this is built on top of the lms you already run. thinkific, teachable, canvas, moodle. we connect through lti integrations, scorm packages, or the direct api, whichever your platform supports.
your lms stays the system of record. enrolment, payment, grades, and certificates do not move. the adaptive layer reads which questions a learner missed and re-orders the lessons it serves. it does not touch your course structure or your billing.
we are
stennir builds an adaptive layer that re-sequences your own lessons around each student's wrong answers, keeping your instructors and rubrics in charge of what mastery means.
we aren't
stennir does not sell a generic chatbot tutor that answers homework for learners and helps them skip the work you are trying to teach.
#what changes for a curriculum director day to day?
- learners who miss a concept get the remediation lesson next, not three modules later when they have already checked out.
- you see which lessons cause the most wrong answers across the cohort, so you fix the weakest lesson first instead of guessing.
- instructors keep grading against the same rubric. the sequencing reads those results, it does not overrule them.
- pass rates and completion get measured per cohort against your baseline, so you know if the re-sequencing earned its keep.
the point of measuring per cohort is honesty. if adaptive paths do not move completion for your specific course, you should be able to see that in one cohort and turn it off. we would rather show you the number than sell you the idea.
the students who drop are not the ones who fail the first quiz. they are the ones who never got the second explanation of the thing they missed.
if your team also needs help getting instructors comfortable reading these signals, our training division runs the enablement side, and more use-case write-ups like this one live in the journal.
we are new, and the way we work is plain. we scope one course, connect to your existing lms, run adaptive sequencing on the next cohort, and compare completion and pass rates against your baseline. if you run an online course company, a bootcamp, or an in-house l&d team and want to see whether re-sequencing lifts your numbers, book a 30-min discovery call and bring one cohort's data.