Students follow a pathway map on a phone, tablet or laptop. What they got wrong comes back in a daily review, and the motivation features follow published learning research.
A simulated walk through AdaptIQ. A new quiz appears on a student’s pathway; the student answers a question and marks the QRS complex on an ECG.
Students follow a pathway map step by step, on a phone, tablet or laptop. Underneath it is learning science that works: retrieval, spacing and honest feedback.
Exercises a student got wrong, and flashcards they rated, come back at growing intervals in a daily review queue.
Short exercises embedded in a lesson. A wrong answer returns in the review queue within a day.
Topic accuracy over the last 30 graded answers, shown only after 10. Until then the topic reads “Not enough data yet”.
Each practice set has a pass threshold, and a topic can require a minimum accuracy over a minimum number of answers before the next one opens.
The answer key never reaches the browser before the student has answered. Every exercise can carry an explanation the student sees afterwards.
On a phone: a bottom tab bar and reference tools that open as a bottom sheet. On a tablet or laptop the same tools open as a side panel.
Twelve built-in types, two from the 3D atlas module and one from the biomechanics module. The AI assistant can draft the eleven marked with a spark. AI can draft
Subject modules plug into AdaptIQ with their own exercise types, tools and pages. Here is what the ones we built can do.
A WebGL viewer with 2,234 meshes and 3,432 named structures, searchable by name. It powers two exercise types and opens from inside lessons and practice.
Built from BodyParts3D, © The Database Center for Life Science (DBCLS), licensed under CC BY 4.0.
Headings, tables, resizable images with captions, quotes — and formulas typeset with KaTeX, inline or as blocks, inserted through a formula dialog.
We turn a content package — Markdown lessons plus exercises — into a module with one command, and updating it keeps student progress. Deeper modules add their own exercise types and tools.
Each problem is generated from a seed, so every student and every attempt gets its own numbers. The server rebuilds the problem and recomputes the grade; answers count within ±1.5%.
Find the vertical support reaction at A.
Find the vertical support reaction at A.
Motivation in AdaptIQ follows published learning research and avoids dark patterns: no loss framing and no countdowns.
Students choose a daily goal and an if-then plan for when they will practise. Reaching the goal earns bonus XP.
XP measures activity, never changes a score and is never taken away. A daily cap keeps it fair, and every student sees how they earned theirs.
The streak counts practice days, and students have ways to protect it when something gets in the way.
About an hour before the student’s own plan time, quoting their plan. Quiet hours, one-click unsubscribe, and guilt or urgency phrases banned in code.
Each mechanic is a per-student switch with a percentage rollout and a measurement report. The protected streak and reminders ship switched off until an administrator turns them on.
Read the rules behind it →Book a demo and we’ll walk you through the AI workspace, the student player and the review flow — on a phone and on a desktop.