A special delivery by a fork: Where does artificial intelligence come from?

Abstract In this article, I discuss the use of artificial intelligence (AI) in evaluation and its relevance to the evolution of the field. I begin with a background on how AI models are developed, including how machine learning makes sense of data and how the algorithms it develops go on to power AI models. I go on to explain how this foundational understanding of machine learning and natural language processing informs where AI might and might not be effectively used. A critical concern is that AI models are only as strong as the data on which they are trained, and evaluators should consider important limitations when using AI, including its relevance to structural inequality. In considering the relationship between AI and evaluation, evaluators must consider both AI's use as an evaluative tool and its role as a new subject of evaluation. As AI becomes more and more relevant to a wider array of fields and disciplines, evaluators will need to develop strategies for how good the AI is (or is not), and what good the AI might (or might not) do.