You have trained a model, coded against it, and watched an agent plan and act. Today the question flips: when is AI help a good idea, and when does it go wrong? You will practise one human check on a short AI suggestion, try five real cases in the AI courtroom, then write the class ground rules you will work to when you build your own projects.

AI tools sit on a spectrum. Some only suggest: they explain a loop, draft a line of code, or rank search results, and a person decides what to do next. Others can act: an agent given a goal can plan steps, use tools, and change the world (send a message, edit a file, run a program) without asking every time.

That power is useful, and it is also where the risk lives. The person who sets the goal, reads the output, and accepts the result stays responsible. Keep three questions in mind for every case today:

• Did a human stay in the loop before anything irreversible happened?
• Who is responsible if it goes wrong: the tool, the person, or both?
• Was the AI used to learn, or used instead of thinking?

Practise staying in the loop before the courtroom.

Your teacher will show a short AI-suggested snippet on the board (a tiny score or print line that may hide a mistake). Before anyone runs it:

1. Read every line.
2. Say what you think it will do, and whether anything looks wrong.
3. Fix anything that is wrong (or agree the safe line with your teacher).
4. Only then type that checked version into your editor and run it.

That pause is the human check. Suggest is fine; act without reading is how unread code causes real problems.

Five short cases. For each one: read the facts on screen, choose a verdict, and commit before the reasoning is revealed.

Verdicts you can choose:

• Fine: good use of AI
• Fine, with a check: useful, but only if a human verifies
• Not OK: crossed a line

After each reveal, name who was responsible.