Literacy and STEM Teachers Adapt AI Ethics Curriculum
Keywords:Artificial Intelligence, Ethics, Curriculum Co-design, Project-based Learning
AbstractThis article examines the ways secondary computer science and English Language Arts teachers in urban, suburban, and semi-rural schools adapted a project-based AI ethics curriculum to make it better fit their local contexts. AI ethics is an urgent topic with tangible consequences for youths’ current and future lives, but one that is rarely taught in schools. Few teachers have formal training in this area as it is an emerging field even at the university level. Exploring AI ethics involves examining biases related to race, gender, and social class, a challenging task for all teachers, and an unfamiliar one for most computer science teachers. It also requires teaching technical content which falls outside the comfort zone of most humanities teachers. Although none of our partner teachers had previously taught an AI ethics project, this study demonstrates that their expertise and experience in other domains played an essential role in providing high quality instruction. Teachers designed and redesigned tasks and incorporated texts and apps to ensure the AI ethics project would adhere to district and department level requirements; they led equity-focused inquiry in a way that both protected vulnerable students and accounted for local cultures and politics; and they adjusted technical content and developed hands-on computer science experiences to better challenge and engage their students. We use Mishra and Kohler’s TPACK framework to highlight the ways teachers leveraged their own expertise in some areas, while relying on materials and support from our research team in others, to create stronger learning experiences.
How to Cite
Walsh, B., Dalton, B., Forsyth, S., & Yeh, T. (2023). Literacy and STEM Teachers Adapt AI Ethics Curriculum. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16048-16055. https://doi.org/10.1609/aaai.v37i13.26906
EAAI Symposium: Resources for Teaching AI in K-12