When one teaches, two learn: The bidirectional learning process supervisor-PhD student

In contexts involving teachers and students, knowledge transfer is commonly assumed from the former to the latter. However, what if teachers learn from students? This paper investigates the bidirectional knowledge transfer between PhD students and their supervisors. We consider 51,826 PhD students who graduated in the STEM fields in France between 2010 and 2018. Focusing on Artificial Intelligence (AI) knowledge transfer, we find evidence that a student supervised by a supervisor with AI knowledge is 12 percentage points more likely to write a thesis in AI than a student with a supervisor with no AI knowledge, denoting an AI knowledge transfer from supervisors to students. We also find that a supervisor with no AI knowledge, if exposed to a student with AI knowledge, is 19 percentage points more likely to publish an article with AI content in the three years after the student's graduation, denoting an AI knowledge transfer from students to supervisors. Those results confirm the bidirectionality of the learning process.