An AI model must explain why an answer is correct or incorrect. The explanation helps the user learn. A simple correct/incorrect signal is not enough for learning.
By Pawan Nayar · from the LLOS AI Learning record
An AI model must explain why an answer is correct or incorrect.
The explanation helps the user learn. A simple correct/incorrect signal is not enough for learning.
The AI model should show the correct answer clearly. The AI model should also explain why the answer is correct. This explanation helps the user understand. The explanation should be detailed. It should teach the user something new.
A student answers a question about parts of speech. The student identifies 'happy' as a noun. The AI model shows 'happy' is incorrect. The AI model then explains: "'Happy' describes a feeling. It does not name a person, place, or thing. Words that describe are adjectives. Nouns name things, like 'apple' or 'river.' Feelings and ideas can also be nouns, like 'hope,' because they are named concepts." The student learns the difference.
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A developer uses scripts to build different question types. Each script focuses on a specific kind of question.
An AI model must have a plan for error messages. The plan should define different types of errors.
The AI model should create practice exercises that reward exact word choice. This helps the learner use words more precisely.
An AI model should first acknowledge the user's reasoning. This shows the AI model understands the user's thought process.
An AI model must explain why an answer is correct or incorrect. The explanation helps the user learn. A simple correct/incorrect signal is not enough for learning.