The advent of large language models (LLMs) such as ChatGPT and text-to-image generation systems like DALL-E developed by OpenAI has raised critical questions in education and art communities, urging us not only to reconceptualize our understanding of creativity, authorship, and human--machine relations, but also to examine their social and ethical implications for teaching and learning. Particularly because the data sets to train many generative artificial intelligence (AI) models are not made available to the public and their operating mechanisms are opaque to human reasoning, it is difficult to predict their potential harm to the field of art education. Algorithms are trained on data sets from past data, which are developed by inherently biased humans, who have oppressed certain groups of people throughout history. Scholars and artists have raised issues of authorship and copyright infringement (Aktay, 2022; Cetinic & She, 2022; Dehouche & Dehouche, 2023; Vartiainen & Tedre, 2023), as well as accusations of reinforcing biases against certain groups of people based on race, gender, class, and dis/abilities (Bianchi et al., 2022; Dehouche, 2021; Johnson, 2022). Therefore, it is important to develop effective art pedagogies that can empower students to engage critically with these emergent intelligences to understand how they shape the ways we see ourselves, others, and the world through imagery.