In this episode, Jean Vassoyan explores a fundamental question in machine learning: how can we design “teacher” algorithms that effectively guide “student” agents?

Using a broad and abstract framework, he connects fields such as Curriculum Learning, Active Learning, and Reinforcement Learning, showing how they share a common mathematical foundation aimed at optimizing learning.

Applied to human education, this approach becomes Adaptive Learning, intelligent tutoring systems that personalize instruction for each learner. By viewing machine and human teaching through a unified lens, Jean invites us to rethink what it truly means to be a teacher and how this role can be defined and formalized in quantitative terms.