Learning with agents requires feedback, not answers
Agents are most useful for learning when they expose feedback.
A finished answer can hide the reasoning path. A better learning loop asks questions, tests understanding, compares alternatives, surfaces mistakes, and forces the learner to revise their model.
Learning improves when the agent becomes a feedback instrument, not an answer vending machine.
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