Consider a society that faces uncertainty about a payoff-relevant state and wants to train students to make correct decisions. In educational institutions, students learn from their teachers, but they also get outside information, and later learn from peers. We show that privately Bayesian actions need not be optimal inputs into social learning: when students’ actions reflect teacher-side information that is correlated across peers, observing many such actions can give this information excessive social weight. A social planner may therefore optimally reduce the precision of instruction, inducing students to rely more on outside information before their actions become signals for others, and students can end up better informed despite learning less from their teachers. The case for a precision cap is stronger when peer interaction is homophilous, because same-teacher information is more likely to survive aggregation, and weaker when outside information is itself systematically distorted. This provides an informational rationale for viewpoint neutrality as an institutional policy: it limits the social overrepresentation of correlated teacher-side information when students mostly learn from peers exposed to similar sources.

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