Lessons From Two Roundtables on Artificial Intelligence and Signal Processing Education: Addressing the emergence of a new era and a new discipline [Special Issue on Artificial Intelligence for Education: A Signal Processing Perspective]

Recently, the Signal Processing Society Education Board launched an initiative to host roundtables discussing the impact of machine learning and artificial intelligence advancements in signal processing education. The first of these events took place in October 2023. The panelists were Profs. Alan Bovik (The University of Texas at Austin), Edward J. Delp (Purdue University), Aggelos K. Katsaggelos (Northwestern University), Anna Scaglione (Cornell University and Cornell Tech.), Sharon Gannot (Bar-Ilan University), and Andreas Spanias (Arizona State University). The panel was moderated by Profs. Marios Pattichis (University of New Mexico) and Andres Kwasinski (Rochester Institute of Technology). The second panel, which was organized during ICASSP 2024, had as panelists Profs. Gene Cheung (York University), Danilo Mandic (Imperial College London), Martin Haardt (Ilmenau University of Technology), and José M. F. Moura (Carnegie Mellon University). This panel was moderated by Prof. Andres Kwasinski. This article summarizes the roundtable discussions, distills key lessons, and offers additional insights. A key consensus among the panels was that we are at a pivotal moment when we are witnessing the emergence of a new discipline that combines the model-based approach from traditional signal processing with the data-driven approach from ML and Data Science. The emergence of this new discipline calls for new pedagogical methods and brings new tools that will reshape how we do research.