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, then put five real cases on trial: you hear what happened, you give your verdict, and the reasoning behind the fairest verdict is revealed. After that you write the ground rules the class will work to when you start your own big project.
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:
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:
That pause is the human check. Suggest is fine; act without reading is how unread code causes real problems.
Five short cases, and in each one somebody used AI to help them code. Read the facts on screen, choose the verdict you think is fair, and commit to it before the reasoning is revealed, because guessing after the answer teaches you nothing.
Verdicts you can choose:
After each reveal, name who was responsible.
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