AI-Personalised Learning Pathways
Deploy adaptive AI that personalises learning content, pace, and assessments for each student — improving completion rates by 40% and learning outcomes by 25%.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
All students receive the same content in the same order at the same pace. Fast learners are bored while struggling students fall behind. Completion rates for online courses average 15-20%. Instructors cannot identify at-risk students until it is too late. One-size-fits-all assessment fails to measure true competency.
After
AI adapts content difficulty, sequence, and pace to each student's demonstrated mastery. Struggling students receive additional support materials while advanced students are challenged. At-risk students are flagged early for intervention. Completion rates reach 55-65% with measurably better learning outcomes.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Build Learner Knowledge Model
3 weeksDefine the knowledge graph for your curriculum — concepts, prerequisites, and mastery criteria. Instrument your LMS to capture granular learning signals: time-on-task, quiz performance, content interactions, help-seeking behaviour. This data feeds the personalisation engine.
Develop Adaptive Content
6 weeksCreate multiple versions of key learning materials at different difficulty levels. Build a content library with: core explanations, simplified versions, advanced extensions, practice exercises at varying difficulty, and alternative formats (video, text, interactive). Tag all content with knowledge graph concepts.
Build Personalisation Engine
6 weeksImplement adaptive algorithms: Bayesian Knowledge Tracing for mastery estimation, multi-armed bandits for content selection, and spaced repetition for review scheduling. Build real-time learner dashboards showing progress, predicted completion, and recommended next actions.
Pilot With Cohort
4 weeksRun A/B test: adaptive pathway vs. standard sequential pathway with 100+ students. Measure: completion rate, assessment scores, time-to-mastery, student satisfaction, and engagement metrics. Collect qualitative feedback on the learning experience.
Scale & Iterate
OngoingRoll out adaptive pathways across all courses. Continuously improve personalisation models with new student data. Add AI tutoring for real-time Q&A support. Build instructor dashboards showing class-level insights and individual student alerts.
Tools Required
Expected Outcomes
Increase course completion rates from 15-20% to 55-65%
Improve post-course assessment scores by 20-30%
Reduce time-to-mastery by 25-35% for motivated learners
Identify at-risk students 2-3 weeks earlier for timely intervention
Improve student satisfaction with personalised learning experience
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Frequently Asked Questions
Yes, most adaptive learning tools integrate with major LMS platforms (Moodle, Canvas, Blackboard) via LTI or API connections. The adaptive engine runs alongside your existing LMS rather than replacing it. Content is served through the LMS while the AI handles the personalisation logic behind the scenes.
Adaptive algorithms can begin personalising with as few as 50-100 students, using initial assessments to gauge starting level. Performance improves significantly with 500+ students as the system learns patterns across different learner types. The beauty of adaptive learning is that it provides value from day one through diagnostic assessment.
Ready to Implement This Workflow?
Our team can help you go from guide to production — with hands-on implementation support.