Learning that adapts to every student.
Study smarter with personalised lessons, intelligent assessments and guidance based on your actual progress.
Your next chapter starts here
Learn at your pace, with guidance along the way.
Personalised learning paths
Lessons ordered around what each student has and hasn't mastered yet.
Instant assessment feedback
Automatic marking returns results the moment a submission is complete.
Progress tracking
Concept-level mastery, not just a course-wide percentage.
Lecturer-approved content
Every lesson and AI tutor answer is grounded in material a lecturer wrote or approved.
How the system works
The same short cycle repeats through every module, so students always know where they stand and what to do next.
- 1
Learn a topic
Read a focused lesson built around one or two knowledge concepts, with resources and worked examples.
- 2
Complete an assessment
Answer practice questions or a graded assessment. Results are available as soon as automatic marking finishes.
- 3
Receive personalised guidance
The platform identifies weak concepts and recommends a specific lesson, explanation or practice set, and says why.
Guidance built from each student’s own results
Every question a student answers updates their mastery of the concepts it covers. When a concept falls behind, the platform explains what happened and offers a direct action. Recommendations are produced by transparent rules a lecturer can read and override, not a hidden model.
- Mastery tracked per knowledge concept, not just per lesson.
- Prerequisite gaps surfaced before they block new material.
- Every recommendation states the result that triggered it.
Recommendation
Review “Recursion” before starting “Trees”
- Why
- Trees build on recursion, which is currently at 34% mastery.
- Triggered by
- Practice set — 3 consecutive incorrect recursion questions.
- Expected benefit
- Tree traversal becomes noticeably easier to follow.
A clear picture of progress
Students and lecturers see completed lessons, assessment scores, strong areas and areas that need work in one place.
Lessons completed
5 / 12
Average score
79%
Strong areas
Complexity, arrays
Needs improvement
Recursion, linked lists
Concepts several students find difficult
- Recursion18 of 29 students below mastery
- Linked structures12 of 29 students below mastery
- Hashing7 of 29 students below mastery
Recommended action
Schedule a short recursion review before the class begins tree traversal.
Built for the people who teach
Lecturers arrange courses into modules and lessons, write lesson content, upload materials and create questions, quizzes and assessments. Class and individual performance views show which concepts several students are finding difficult, so teaching time can be spent where it matters.
Lecturers also review the recommendations the platform gives their students and can override any mastery decision.
See the lecturer workflowAn AI tutor grounded in your course
Students can ask questions and get explanations drawn from the material their lecturer has approved for the course. Answers include references to the lesson or document they came from. When the approved material does not cover a question, the tutor says so rather than guessing. It is a course-focused learning assistant, not a general chatbot, and it never sets or changes official grades.
When keys are inserted in sorted order, every new node attaches on the same side, so the tree has no branching and search must walk one long path — the same cost as scanning a list. Balanced variants avoid this by keeping the height logarithmic.
Source: Module 3 · Lesson 2 — Binary search trees