Acting Humanly: Identification and Analysis of Logical Reasoning Biases Exhibited by ChatGPT versus Undergraduate Students

Abstract

Definitions of Artificial Intelligence (AI) include characterizing algorithms as those that: thinking humanly, thinking rationally, acting humanly and acting rationally. On the one hand, Logic, as a formal framework, allows for the creation of algorithms capable of thinking rationally by expressing real world situations in a language that enables valid and rigorous reasoning. On the other hand, Large Language Models, such as ChatGPT, represent algorithms that acting humanly, especially in tasks involving understanding and generating natural language text. However, these models can exhibit logical reasoning biases, which are tendencies that impair the ability to reason logically. This article aims to identify and analyze the logical reasoning biases exhibited by ChatGPT in comparison to those exhibited by Information Technology Undergraduate Students, beginners in the Logic course.

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