Introducing students to the AI toolbox in biological physics education

Artificial intelligence (AI) has emerged as a controversial tool in education. Institutions are grappling with how to regulate and provide guidance for use of this technology, specifically large language models (LLMs). There are, however, compelling reasons to both investigate and pursue avenues for incorporating it into biophysics education. AI is an umbrella term that refers to a series of algorithms designed to mimic human intelligence. Within that subset are tools such as machine learning, deep learning, neural networks, and large language models. For biophysics students, developing an understanding of these distinctions and a sense of how these tools work is critical. Biophysics is both highly interdisciplinary and involves many complex problems. Biophysics researchers study a variety of phenomena from molecular dynamics, to diffusion, to emergent behavior. Students interested in industry may find themselves building automated labs, developing medical diagnostic tools or working on image creation or analysis. In one way or another, AI use has found its way into many of these fields. This work presents both motivation and suggestions for how to incorporate AI into an upper division undergraduate semester course in biophysics. Outcomes from incorporating these suggestions include helping students understand how the technology works, how they can work with the technology themselves and how cutting edge research and industries are using these tools today.