Learning often involves interaction between multiple agents. Human teacher-student settings best illustrate how interactions result in efficient knowledge passing where the teacher constructs a curriculum based on their students' abilities. Prior work in machine teaching studies how the teacher should construct optimal teaching datasets assuming the teacher knows everything about the student.However, in the real world, the teacher doesn't have complete information and must probe before teaching.Our work proposes a simple probing algorithm which uses Gaussian processes for inferring student-related information, before constructing a teaching dataset.We study this in two knowledge settings where the student is a tabula rasa or has partial knowledge of the domain.We study this in the ridge regression, support vector machines and offline reinforcement learning domains.Our experiments highlight the importance of probing before teaching, demonstrate how students can learn much more efficiently with the help of an interactive teacher, and outline where probing combined with machine teaching would be more desirable than passive learning.