An AI model must give different feedback messages based on a user's performance. The message should match the user's score. This helps the user feel understood and supported.
By Pawan Nayar · from the LLOS AI Learning record
An AI model must give different feedback messages based on a user's performance.
The message should match the user's score. This helps the user feel understood and supported.
An AI model should change its feedback message based on the user's score. A perfect score gets a congratulatory message. A good score gets a simple factual message. A low score gets a supportive message. This makes the feedback more personal.
A student uses an AI model for a quiz. If the student gets all answers correct, the AI model says, 'Quiz complete — perfect!' If the student gets some answers wrong, the AI model says, 'Quiz complete — 3 of 5.' If the student gets many answers wrong, the AI model says, 'Tough quiz. Take a breath.' The AI model adjusts its tone to the student's performance.
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An AI model should first acknowledge the user's reasoning. This shows the AI model understands the user's thought process.
An AI model should frame calls to action as challenges. This means encouraging the user to achieve a goal.
Making a feature optional lets users choose if they want to use it. This helps understand if the feature is useful.
Error messages must be clear and helpful. The message should tell the user what to do next.
An AI model must give different feedback messages based on a user's performance. The message should match the user's score. This helps the user feel understood and supported.