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AI agent for computer science students

Algorithm Practice Coach Agent

Real understanding of algorithm patterns through graded practice and feedback

LearningIT and development
See how it worksGet access
Algorithm Practice Coach Agent: what goes in, what the agent does and what you get

What it does

Students who memorize specific solutions freeze when a problem is slightly different in an exam or interview. This agent gives practice problems on a chosen topic, from easier to harder. After each attempt it runs the student's solution against test cases, including edge cases, and checks efficiency. Instead of just marking it wrong, it asks the student to explain the approach and gives a hint on the specific weakness, such as a missed edge case or a slow loop. If the student still struggles after two hints, it steps back to a simpler version of the problem. It tracks which patterns the student has mastered and which need more work. The student writes all code. Edge case: a solution that passes tests but is inefficient counts as correct but is flagged for a complexity discussion.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueYes, continueApprovedNoNo 1 STARTS WHEN Student picks a topic to practice 2 DOES Pick a problem at the right level from the masteryrecord 3 USES A TOOL Run the student's solution against test cases andedge cases 4 CHECKS THE RESULT Is the solution correct on every test? If not: ask the student to explain the approach and givea targeted hint. Back to step 3. 5 CHECKS THE RESULT Is the solution efficient enough for the problem? If not: flag the complexity and hint at a fasterapproach. Back to step 3. 6 DOES Update the mastery record and pick the next problem 7 YOU APPROVE Student confirms understanding before advancing 8 RESULT Session summary with strengths and gaps
Read the steps as a list
  1. Student picks a topic to practice
  2. Pick a problem at the right level from the mastery record
  3. Run the student's solution against test cases and edge cases
  4. Is the solution correct on every test?If not: ask the student to explain the approach and give a targeted hint. Back to step 3.
  5. Is the solution efficient enough for the problem?If not: flag the complexity and hint at a faster approach. Back to step 3.
  6. Update the mastery record and pick the next problem
  7. Student confirms understanding before advancingThe agent waits here for your OK.
  8. Session summary with strengths and gaps

How it decides

It judges a solution on correctness and efficiency, and moves up in difficulty only after the student shows understanding, not just a passing run.

  • Judge on correctness and efficiency
  • Advance only after understanding is shown
  • Flag passing but inefficient solutions for discussion

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Topics and difficulty curve
  • Problem sources
  • How fast to escalate hints
  • Mastery criteria

What keeps you in control

It always asks you first

  • Student confirms understanding at each step

Hard limits

  • Never writes the student's solution
  • Does not help with work being graded live

It stops when

  • Done: topic practiced and gaps recorded
  • Stop: the student asks only for the answer

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensOn a Thursday evening, a student practicing binary search passed all tests with a linear scan. The efficiency check failed, so the agent asked how to halve the search space and gave one hint. The student's rewrite failed an empty-array test, so the agent pointed to that edge case. The third attempt passed both checks. The student confirmed understanding, and the agent marked the pattern as improving.

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