Sunghee Yun (He/Him)

Co-Founder & CTO @ Erudio Bio, Inc., Co-Founder & Leader & Chair of Silicon Valley AI Nexus (K-PAI Nexus), Advisor @ Korean American Semiconductor Professional Alliance (KASPA), CGO / Global Managing Partner @ LULUMEDIC, KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, AI-Korean Medicine Integration Initiative Task Force Member @ The Association of Korean Medicine, Director of AI Semiconductor @ K-BioX, Visiting Professor @ Sogang University, Advisory Professor @ DGIST, Global Advisory Board Member @ Innovative Future Brain-Inspired Intelligence System Semiconductor of Sogang University, and Network Expert @ Gerson Lehrman Group

Abstract

As admission officers for Korea’s science high schools, you face a critical question: What qualities should we seek in students who will become scientists and engineers in an AI-augmented world? The transformation from the Turing Test to today’s large language models and AI agents has fundamentally altered scientific research, engineering practice, and problem-solving itself. When AI can assist with literature reviews, generate hypotheses, analyze complex datasets, and even suggest experimental designs, the criteria for identifying future scientific leaders must evolve. This talk examines how the new learning paradigm — domain mastery, AI fluency, and human values — should inform your selection decisions and shape your understanding of student potential.

In evaluating applicants, you will encounter two distinct but equally valuable pathways: students who will become AI researchers and engineers, and students who will become domain scientists (biologists, chemists, physicists, materials scientists) who leverage AI as a powerful tool. Both require deep problem-solving ability, but manifested differently. Look beyond traditional metrics of knowledge mastery to identify students who ask compelling questions, demonstrate genuine domain curiosity, show adaptability in learning new tools, and exhibit collaborative mindsets. The most promising students for the AI era are not necessarily those who memorize the most formulas, but those who can formulate meaningful problems, iterate creatively with available tools (including AI), and synthesize insights across domains. A student passionate about climate science who learns to use AI for satellite data analysis, or a biochemistry enthusiast who employs language models to explore protein folding literature — these represent the future of scientific excellence.

Yet as you select Korea’s future scientific leaders, consider also their character and values. When AI can automate significant portions of technical work, what distinguishes truly exceptional scientists is their ability to ask: What questions matter most? What ethical boundaries must we maintain? How do we ensure technology serves humanity? The students you admit will not only solve technical problems but will also grapple with existential questions about human agency, technological responsibility, and societal impact. Your role is to identify young people who combine scientific passion and AI adaptability with the moral integrity and empathy to guide innovation toward human flourishing. In the AI era, the best scientists will be those who know not just what can be built, but what should be built — and for whom.