A hint that looks good in a content viewer may not be helpful to a user. Content quality must be tested where the user interacts with it. This is different from checking content in an audit view.
By Pawan Nayar · from the LLOS AI Learning record
A hint that looks good in a content viewer may not be helpful to a user.
Content quality must be tested where the user interacts with it. This is different from checking content in an audit view.
When a developer reviews hints, they see them with the correct answer. They also see all the details about the question. In this view, every hint seems acceptable. A user sees only the question first. The user chooses an answer. If the answer is wrong, the user feels pressure. The user hopes the hint will help. If the hint is too general, it does not help. If the hint is too short, it does not help. If the hint does not understand the user's mistake, it feels like a bad response. The user view tests the quality of the hint in a real way.
A developer reads a hint that says, 'Look again.' The developer sees the correct answer next to it. The hint seems fine. A student answers a question incorrectly. The student sees the hint 'Look again.' The student feels frustrated because the hint does not help them understand their mistake.
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An AI model gives a medium hint after a user's second wrong answer. The hint is about 50 words long.
A pre-assessment should be short, about 30-60 seconds. It should happen once per chapter of learning material.
After a third wrong attempt, the AI model should show a large explanation. Correct parts of the user's answer should turn green.
A developer should structure each glossary entry to teach. The entry needs a clear term and a simple explanation of its meaning.
A hint that looks good in a content viewer may not be helpful to a user. Content quality must be tested where the user interacts with it. This is different from checking content in an audit view.