They acknowledge that “while the rhythm in most line encountered in a work of poetry appears mundanely repetitive on the surface, poetry, while mostly a constrained literary form, is prone to unexpected deviations of such standard patterns.” It is this continual setup and subversion of literary expectations that makes meter an ideal playspace for machine learning and provides an opportunity to teach AI fundamentals in the English classroom. In their 2016 paper for the International Conference on Computational Linguistics, Manex Agirrezabala, Iñaki Alegria, and Mans Hulden* apply Natural Language Processing (NLP) techniques to a selection of poetry in an attempt to identify its meter-the underlying rhythm expressed through stressed and unstressed syllables. Because public domain poetry texts are widely available and far shorter than novels, they make great candidates for introducing machine learning techniques in the ELA curriculum. Our Narrative Modeling with StoryQ project aims to integrate AI into existing disciplinary studies such as English Language Arts (ELA) in order to prepare youth for the future.Īmong many literary genres that students encounter in high school, poetry presents a unique opportunity for integrating AI education.
MAKING SENSE OF POETIC SCANSION HOW TO
This means that many students simply write off a future in AI because they aren’t “math people” or don’t think they can learn how to code. However, opportunities to study AI at the pre-college level, if available at all, are limited to computer science classes. From increasingly autonomous self-driving cars to climate change models, Artificial Intelligence (AI) has become a ubiquitous medium for understanding, explaining, and interacting with the world around us.
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