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AI agent for computer science students

Lecture to Practice Problem Set Agent

A weekly practice plan that shifts toward the student's weakest topics, with mastery tracked until exam time

LearningIT and development
See how it worksGet access
Lecture to Practice Problem Set Agent: what goes in, what the agent does and what you get

What it does

Students take notes all term and then find during exams that they cannot apply them. After each lecture this agent reads the notes and builds a graded set of practice problems, from warm-up to exam level. When the student submits answers or code, it checks them against worked solutions, runs the code on test cases, and records what went wrong. Topics the student missed are added to the next set, and the agent keeps a mastery score per topic across weeks so older topics return before they fade. It never just shows the answer first; it gives a hint, then the solution after a second miss. The student reviews and approves the plan for the following week. Edge case: when the notes are thin on a topic, the agent asks for the slide or textbook section instead of making up material.

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 uploads notes after a lecture 2 USES A TOOL Read the notes and map them to syllabus topics 3 DOES Build a graded problem set from warm-up to examlevel 4 CHECKS THE RESULT Does each topic have enough source material to set aproblem? If not: ask the student for the slides or textbooksection and re-read. Back to step 2. 5 USES A TOOL Check the student's answers and run submitted codeon test cases 6 DOES Update the mastery score for each topic 7 CHECKS THE RESULT Is any topic below the 70 percent mastery line? If not: keep the topic in the next set with newproblems, then recheck after it. Back to step 3. 8 DOES Draft next week's set mixing weak, new and reviewtopics 9 YOU APPROVE Student approves the plan for next week 10 RESULT Next week's problem set and mastery table
Read the steps as a list
  1. Student uploads notes after a lecture
  2. Read the notes and map them to syllabus topics
  3. Build a graded problem set from warm-up to exam level
  4. Does each topic have enough source material to set a problem?If not: ask the student for the slides or textbook section and re-read. Back to step 2.
  5. Check the student's answers and run submitted code on test cases
  6. Update the mastery score for each topic
  7. Is any topic below the 70 percent mastery line?If not: keep the topic in the next set with new problems, then recheck after it. Back to step 3.
  8. Draft next week's set mixing weak, new and review topics
  9. Student approves the plan for next weekThe agent waits here for your OK.
  10. Next week's problem set and mastery table

How it decides

Weak topics are those with mastery under 70 percent. Each set mixes new material, weak topics and one older topic due for review.

  • Add a topic to the next set when mastery is below 70 percent
  • Give a hint on the first miss and the full solution on the second
  • Bring back any topic not practiced for 3 weeks
  • Raise difficulty only after 2 correct answers in a row

Make it yours

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

  • Mastery line that counts as learned (default 70 percent)
  • Problems per set (default 8)
  • Exam date to plan backward from
  • Topics to prioritize

What keeps you in control

It always asks you first

  • The weekly practice plan before it starts

Hard limits

  • Never reveals a solution before the student attempts the problem
  • Never invents course content that is not in the sources

It stops when

  • Done: all topics reach the mastery line before the exam
  • Stop: notes are missing for the lecture

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 happensAfter week 5 on graph algorithms, the agent built 8 problems. The student scored 3 of 4 on traversal but 1 of 4 on shortest paths, so mastery on shortest paths stood at 25 percent. The agent found the notes covered only the idea, so it asked for the textbook section. With that added it set 5 new shortest path problems in week 6, and the student reached 70 percent by Friday.

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