Adaptive Learning Assistant for a Digital Campus
At a glance
CLIENT
A regional online education provider
SERVICE
- Adaptive learning platform design, AI software development services, custom software development, data analytics consulting services, software development services for startups, CX
INDUSTRY
- Education
A regional online education provider was scaling fast across several subject areas, but student outcomes and teacher workload were diverging. In large gateway courses, instructors struggled to give individual attention, while some students were under-challenged and others quietly fell behind. Course content was delivered in the same way to everyone, regardless of prior knowledge, pace or study habits. End-of-term results showed high dropout rates in key subjects and significant performance gaps between campuses and demographic groups.
LeanCoded partnered with the provider to build an adaptive learning assistant for its digital campus: a platform that adjusts content and assignments to each student’s level, includes an AI tutor for step-by-step explanations, and provides real-time analytics for teachers and program leads. Over three academic terms, this solution transformed several core courses into data-driven, personalized learning experiences, improving pass rates, reducing failure rates and giving faculty clear visibility into how their groups were progressing week by week.
When “One Size Fits All” Stops Working
Before the program, most courses followed a conventional pattern: the same video lectures, the same problem sets and the same exam schedule for everyone. The provider’s platform could track basic activity, but it did not react to it. Students who arrived with gaps in foundational math or writing struggled from the first weeks, while more advanced learners breezed through early material and disengaged. Teachers received aggregate grade reports and could see who had already failed an assignment, but they saw it too late to change the trajectory for that term.
At the same time, the provider wanted to expand its online catalogue and reach new regions. That meant onboarding more adjunct faculty and subject-matter experts, many of whom were not used to teaching online at scale. Without better tools, the only realistic way to keep up with demand was to accept variable quality and high attrition in some courses. The leadership team needed a different model: a digital adoption platform for learning that could scale high-quality, adaptive instruction without overwhelming teachers with manual monitoring and 1:1 tutoring.
Turning Static Content into an Adaptive Learning Experience
LeanCoded proposed to treat each course as a living system that responds to student behavior in real time. Instead of only storing videos and PDFs, the new platform models each learner’s knowledge state and adjusts difficulty, practice volume and feedback accordingly. We combined AI software development services and custom software development to design a modular architecture: content stays under instructor control, but the path through it is governed by adaptive logic and machine learning models that use ongoing performance data.
Students interact with an AI tutor embedded directly in the course interface. The tutor answers questions about course concepts, walks through worked examples step by step and highlights where a student’s reasoning diverges from the expected solution. When a student struggles repeatedly with a concept, the system adjusts by offering alternative explanations, prerequisite review material or easier practice problems. On the other hand, students who quickly demonstrate mastery are guided towards more challenging tasks and enrichment activities instead of repeating what they already know.
From Course Content to an Adaptive Campus Assistant
To avoid treating the adaptive features as a standalone product, LeanCoded and the provider agreed to embed them into the existing LMS and course structure. Using data analytics consulting services, we first analysed three high-enrolment courses — an introductory quantitative course, a programming fundamentals course and a writing-intensive humanities course. For each, we identified key learning outcomes, historical pass and fail rates, common points of confusion and patterns of engagement over the term.
On top of this analysis, we defined a “knowledge map” per course: core concepts, prerequisite relationships and typical misconceptions. This map became the backbone for the adaptive engine. Our team then used AI software development services to design item difficulty models and mastery estimation algorithms that could infer where each student stood after only a few interactions. The AI tutor was trained on course-specific content and solution strategies, with guardrails to avoid providing full exam answers and to encourage genuine understanding instead of copying.
To support instructors at scale, we integrated a real-time analytics layer that aggregates progress data at student, group and course levels. Teachers and program coordinators can see where their cohort stands against the course milestones, which concepts create the most friction and which students may need intervention. This analytics layer, delivered through data analytics consulting services, was built to fit existing grading policies and program rules so that faculty could incorporate it into their daily teaching rather than treat it as an extra reporting burden.
Adaptive practice and assessment per student
AI tutor for step-by-step explanations
Early-warning analytics for instructors
Course-level insights for program leaders
Alignment with existing LMS and workflows
How LeanCoded Turned a Static Platform into an Adaptive Campus
The provider did not want to replace its entire LMS; it needed to make existing courses more responsive to how students actually learn. LeanCoded used AI and analytics to turn content and results into a feedback loop that benefits both learners and teachers.
- Learning paths that match each student
Assignments and practice sets adjust in real time to student performance, keeping struggling learners supported and advanced learners appropriately challenged. - A tutor that never gets tired of explaining
The AI tutor is available on demand, explains concepts step by step and points students back to relevant course materials instead of sending them to external sources. - Faculty see problems before exams, not after
Instructors receive clear, visual insights into who is falling behind and on which topics, so interventions can happen while there is still time to change the outcome. - Adoption built into the platform itself
Because the solution operates as a digital adoption platform for learning, students and teachers do not need to switch systems or change devices. The adaptive assistant is simply part of their normal course experience.
Impact on Pass Rates, Engagement and Teaching
After three terms of phased rollout across selected courses, the provider recorded tangible improvements. In gateway quantitative courses, overall pass rates increased by around 22 percentage points, driven by both fewer failing grades and a reduction in withdrawals. Across the pilot subjects, roughly 9 out of 10 students who remained active through the term achieved mastery on the defined core outcomes, compared to significantly lower mastery rates in previous cohorts.
Average assessment scores improved by an amount corresponding to a moderate effect size, and the distribution of grades shifted away from the lowest band. Student surveys reported higher clarity of explanations and greater confidence in handling course material, with many respondents citing the AI tutor as a key reason they felt able to keep up. Instructors, for their part, reported that they could identify struggling students earlier and focus scarce contact time on targeted feedback. By combining AI software development services, custom software development, data analytics consulting services, and software development services for startups into a cohesive digital adoption platform for learning, LeanCoded helped the regional provider turn its online campus into a more adaptive, data-informed environment for both students and teachers.