Teaching is an intuitive social activity that requires reason-ing about and influencing the mind of others. A good teacherforms a belief about the knowledge of their student, asks clar-ifying questions, and gives instructions or explanations to tryto induce a target concept in the student’s mind. We proposePartially Observable Markov Decision Processes (POMDPs)as a model of intuitive human teaching. According to this ac-count, teachers make pedagogical decisions with uncertaintyabout the knowledge state of their student. In two behavioralexperiments, human participants were tasked with balancingassessments (asking questions) and instructions to help teach astudent to build a tower of colored blocks. Human behavior inthe task was compared to the performance of a computerizedteaching algorithm optimized to solve the equivalent POMDP.Our results show that humans favor asking questions and estab-lishing common ground during teaching even at an economiccost and increase question asking as uncertainty grows.