This part of the interview discusses the transformative impact of AI on scientific research workflows.

Traditionally, research relied heavily on labor-intensive experimentation conducted by students, requiring extensive training to validate hypotheses and analyze data.

With the integration of AI, processes such as identifying failure mechanisms in materials have become significantly more efficient, enabling faster insights and streamlined data analysis.

AI has therefore evolved into an essential skill in materials science, particularly in image processing and interpretation.

The rapid emergence of new AI tools illustrates both the accelerating pace of technological advancement and the growing need for transparent, easily adoptable solutions that can be effectively implemented in educational and research environments.