These engagements are illustrative and anonymised. Institution names, logos and precise metrics are withheld or composite. Figures reflect past pilot results — not promises of future performance. Every project kept educators, academic integrity and student-data care in the loop.
Higher education · Ontario · Adaptive learning
A Canadian college's high-DFW first-year course
The problem: A gateway mathematics course enrolled roughly nine hundred first-year students each fall. Drop-fail-withdraw rates hovered near thirty percent, and faculty reported the same three misconceptions resurfacing every term — regardless of lecture format or textbook edition.
Our approach: A six-week discovery sprint mapped LMS log data against formative assessment results. We identified knowledge-tracing signals for the recurring misconceptions and designed adaptive remediation paths with spaced repetition intervals. An intelligent tutoring layer provided Socratic hints — never final answers — with every suggestion routed through an educator review queue before learner release.
Outcome (illustrative): The pilot cohort of one hundred and twenty students showed a measurable reduction in repeat attempts on targeted misconception items. DFW trended downward over two terms, though causation cannot be isolated to adaptive tooling alone. Faculty retained override authority on all path recommendations.
Training provider · Ontario · Intelligent tutoring
An Ontario training provider's tutoring backlog
The problem: A professional-development organization serving working adults faced a tutoring queue that stretched to ten business days during peak enrolment. Learners stalled on self-paced modules while waiting for human support, and completion rates suffered.
Our approach: We deployed an intelligent tutoring model scoped to the provider's existing courseware, configured with academic-integrity guardrails that blocked graded-assessment assistance. Tutors reviewed AI-drafted responses in a human-in-the-loop dashboard integrated with their LMS. Model bias checks were run against anonymized learner interaction logs.
Outcome (illustrative): Median tutor response time for routine queries dropped from ten days to under forty-eight hours during the pilot window, with complex cases still escalated to senior educators. Learner satisfaction scores improved modestly; the provider expanded the pilot to two additional certificate programmes.
University · Canada · Accessibility & analytics
A university's accessibility retrofit of a flagship course
The problem: A flagship online course looked polished in design reviews but failed a screen-reader audit. Video transcripts were incomplete, adaptive quiz logic had no keyboard navigation path, and learning analytics dashboards used colour-only indicators — all AODA concerns for a publicly funded institution.
Our approach: Instructional designers and senior learning engineers audited the course against UDL principles, rebuilt adaptive branching with accessible alternatives, and redesigned the learning analytics dashboard with text labels, contrast-compliant palettes and exportable reports for disability services offices. Student-data flows were documented for the privacy office under FIPPA.
Outcome (illustrative): The course passed a third-party accessibility review. Completion rates among learners using assistive technology improved, though sample sizes were small. The university adopted our dashboard template for three additional programmes.
Edtech SME · North America · LMS integration
Canvas integration for a learning-analytics scale-up
The problem: An edtech scale-up needed to embed personalized learning path widgets inside Canvas without forcing client institutions to leave their existing workflow. Data governance requirements varied by province.
Our approach: We built LTI-compliant integrations with human-in-the-loop review queues, configurable data-retention policies and PIPEDA-aligned consent flows. A retainer model provided ongoing model updates and educator training materials.
Outcome (illustrative): Three institutional pilots went live within one academic term. Integration maintenance continues under a monthly retainer.
All case studies above are illustrative. Metrics are drawn from past engagements and composite scenarios. LearnDrive AI does not guarantee grades, test scores, admissions, certifications or job placement. AI outputs require educator review; academic integrity is protected throughout.
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