Students write real code in a real editor. An AI-powered guide tracks their progress, breaks hard problems into subproblems, and offers help the moment they need it — not before.
Every coding problem becomes manageable — even the hard ones.
Multi-part coding problems are decomposed into subproblems — "implement the helper function," then "write the main loop." You focus on one piece at a time. Research on subgoal learning shows this helps students recognize structural patterns and transfer them to new problems.
When you're stuck, you decide what kind of support to use. Request a hint, reveal a subgoal label, apply a code scaffold, or try the problem as a Parsons puzzle. Research on learner agency shows students learn more when they have a say in their scaffolding.
Support starts light and deepens only when needed — matching how expert tutors escalate.
See the subproblems and where you stand. Clarifies the goal without giving anything away.
Graduated from conceptual nudges to procedural guidance. Hints you've already passed are skipped automatically.
Structural comments, function signatures, or partial code injected into your editor. You fill in the logic.
Still stuck? Solve the subproblem as a drag-and-drop puzzle, then type the solution yourself.
After every code run, the system evaluates your progress against checkpoints — does the function exist? Do the tests pass? Is the right pattern present? Hints you've already surpassed are skipped. Help targets the subproblem where you're actually stuck.
Every hint viewed, every scaffold applied, every Parsons detour taken — teachers see the complete chronological record of each student's help interactions. No black box.
Decomposing problems into labeled subgoals helps learners recognize structural components and transfer solutions to new problems. Students given subgoal labels outperform those without.
Effective tutoring reduces "degrees of freedom" — the number of simultaneous decisions. Graduated hints and scaffolds narrow the task to what the student can manage right now.
Students who choose their own scaffold type show stronger metacognitive engagement. Offering choices — hint, scaffold, puzzle — keeps students in control of their learning.
Hint sequences that start weak and escalate match how expert tutors behave. Skipping hints the student has already surpassed avoids redundancy — a key finding from Cognitive Load Theory.
| Feature | Alps Adaptive Coding | Standard Code Editors | AI Chat Assistants |
|---|---|---|---|
| Problem decomposition | ✓ Automatic subproblem breakdown | ✗ Not supported | ~ On request only |
| Progress tracking | ✓ Checkpoint-based, per subproblem | ~ Pass/fail only | ✗ None |
| Hint quality | ✓ Graduated, context-aware, auto-skipping | ✗ None | ✗ Unpredictable, may give answer |
| Student agency | ✓ Student chooses scaffold type | ✗ N/A | ~ Student must formulate questions |
| Parsons fallback | ✓ Built-in per subproblem | ✗ Not available | ✗ Not available |
| Teacher visibility | ✓ Full interaction timeline | ~ Final submission only | ✗ No visibility |
| Subgoal labels | ✓ Built in, revealable | ✗ Not supported | ✗ Not supported |
Start with the free Educator Basic tier. Every student gets a guide that meets them where they are.