How does academic education background affect top researchers' performance? Evidence from the field of artificial intelligence

The early academic beginning is critical in the development of a researcher's academic career because it helps determine one's further success. We aim to shed light on the path that drives the success of talents in the field of artificial intelligence (AI) by investigating the academic education background of distinguished AI researchers and analyzing the contribution of different educational factors to their research performance. In this study, we collected and coded the curriculum vitae of 1832 AI researchers. Results show that most AI researchers were educated in the United States and obtained their highest degrees from top universities. As for their educational background, approximately 18.27% of AI researchers chose non-AI majors, such as mathematics, physics, and chemistry, instead of AI-related majors, such as computer science. Furthermore, negative binomial regression analysis demonstrates that individuals who publish more during study period will have better research output, whether they are currently in academia or industry. Researchers in academia with overseas degrees published more articles than those without overseas degrees. In terms of interdisciplinary education, a mathematics background leads to increased research visibility of AI researchers in the industry but depresses the scholarly productivity of AI researchers in academia. Academic qualification is the main factor determining the scientific performance of AI researchers in industry, which is not the case in academia. The analysis also showed that individuals who graduated from more prestigious universities tended to receive more citations than those graduating from less famous universities. Moreover, AI researchers in academia who have graduated from prestigious universities seem to pay more attention to the quality of the papers rather than the quantity.