Description

A practical guide for teachers and students navigating the complicated intersection of artificial intelligence, education, and justice

Artificial intelligence is rapidly integrating into today's classrooms, but unlike other new technologies, AI has the potential to harm, making it difficult to take advantage of its benefits. In Critical AI in K-12 Classrooms, Stephanie Smith Budhai and Marie K. Heath draw attention to the biases embedded within AI algorithms, such as those powering OpenAI's ChatGPT and DALL-E, to guide students and teachers in developing strategies to best incorporate AI--or not--into equitable learning.

AI's reliance on existing data and knowledge systems means Black, queer, those with disabilities, and other marginalized students are at greater risk of being harmed by built-in limitations and bias. Budhai and Heath show how to circumvent if not actively resist such harms as machine learning, NLPs, LLMS, and GenAI enter the classroom, with practical examples rooted in culturally sustaining, abolitionist, and fugitive pedagogies across disciplines. Their practical guide creatively answers the concerns of educators committed to forward-thinking yet fair instruction and the needs of students eager to use AI for just ends.

Critical AI in K-12 Classrooms meets the challenges of a key STEM technology with an eye toward cultivating a more just world. Balancing responsible learning with the joy of discovery, Budhai and Heath build a framework for AI instruction that all educators can confidently use.