Paradigms for teaching artificial intelligence in Ecuadorian education

Abstract

Having taught artificial intelligence in the educational field has been a unique and enriching experience, given its historical and continuous role. Introducing students to the history of artificial intelligence has played a critical role in its development. Having explored the key concepts of artificial intelligence such as the production system, symbolic reasoning and machine learning has allowed us to develop this type of paradigm as a new approach in the education of this generation. Teaching the fundamentals of symbolic programming such as list manipulation, definition of recursive functions, symbolic representation of knowledge and manipulation of symbols, how to design and develop expert systems using artificial intelligence. This involves teaching them to represent domain knowledge, implement inference rules, and create feedback systems to improve system performance. Having explored symbolic machine learning techniques, such as decision tree induction, Bayesian inference and fuzzy logic in collaboration on research projects. Having integrated these paradigms, students can gain a deep understanding of artificial intelligence while developing practical programming skills.