"You Can Actually Do Something'': Shifts in High School Computer Science Teachers' Conceptions of AI/ML Systems and Algorithmic Justice
This research addresses the need for AI literacy among teachers and students, focusing on algorithmic justice in high school education, but it is incremental as it builds on existing participatory design methods with a small sample.
The study examined how five experienced high school computer science teachers changed their understanding of AI/ML systems after a year of participatory design, where they co-developed lessons on AI auditing, leading to shifts toward more situated, critical, and agentic perspectives grounded in educational roles.
The recent proliferation of artificial intelligence and machine learning (AI/ML) systems highlights the need for all people to develop effective competencies to interact with and examine AI/ML systems. We study shifts in five experienced high school CS teachers' understanding of AI/ML systems after one year of participatory design, where they co-developed lessons on AI auditing, a systematic method to query AI/ML systems. Drawing on individual and group interviews, we found that teachers' perspectives became more situated, grounding their understanding in everyday contexts; more critical, reflecting growing awareness of harms; and more agentic, highlighting possibilities for action. Further, across all three perspectives, teachers consistently framed algorithmic justice through their role as educators, situating their concerns within their school communities. In the discussion, we consider the ways teachers' perspectives shifted, how AI auditing can shape these shifts, and the implications of these findings on AI literacy for both teachers and students.