This past spring, as I closed out my 18th year of teaching, I felt anxiety that I'd never before felt at the end of a school year. By the time grades are submitted and signs of summer arrive, teachers are typically able to breathe for the first time in nine months. Instead of the relaxation, joy, and accomplishment that typically awaits the end of an academic year, I was consumed with worry that this might be the last time in a nearly two-decade career that I taught a class without having to worry about AI. I get it–AI has technically been around forever, and natural language processing tools such as OpenAI's ChatGPT are built on decades of research. Anyone who has used spellcheck or language translation apps or heard a spoken text message has used language processing tools driven by AI technology.

At a computational level, language processing tasks are traditionally processed in a language-only space/context, isolated from perception and action. However, at a cognitive level, language processing has been shown experimentally to be embodied, i.e. to inform and be informed by perception and action. In this paper, we argue that embodied cognition dictates the development of a new generation of language processing tools that bridge the gap between the symbolic and the sensorimotor representation spaces. We describe that tasks and challenges such tools need to address and provide an overview of the first such suite of processing tools developed in the framework of the POETICON project.