Result: Enhancing pedagogical practices with Artificial Neural Networks in the age of AI to engage the next generation in Biomathematics.

In this work we present a C-MATH-NN framework that extends a C-MATH framework that was developed in recent years to include prediction using artificial neural networks (NN) in a way that is engaging, interdisciplinary and collaborative to help equip our next generation of students with advanced technological and critical thinking skills motivated by social good. Specifically, the C-MATH framework has successfully helped students understand a real-world Context through a mathematical Model which is then Analyzed mathematically and Tested through appropriate numerical methods with data, and finally this undergraduate research becomes a Habit for students. Furthermore, the explanation of the main components of a simple NN-model serves as an introduction to this popular artificial intelligence tool. This framework has contributed to the success of talented students in mathematical biology research and their academic goals. We present a visual introduction to the architecture of artificial neural networks and its application to disease dynamics for all interested learners. We introduce a simple feed forward physics-informed neural network (PINN) built in MS-Excel that works very well for an epidemiological model and an equivalent Python implementation that is robust and scalable. The products introduced in this work are shared in an online repository with curriculum material for students and instructors that includes MS-Excel workbooks and Python files to facilitate the acquisition of technology tools to explore and use in their own projects.
(© 2025. The Author(s).)

Declarations. Competing Interest: The authors have no competing interests to declare relevant to the content of this article.