"You Can Actually Do Something'': Shifts in High School Computer Science Teachers' Conceptions of AI/ML Systems and Algorithmic Justice

TL;DR

This study examines how high school CS teachers' understanding of AI/ML and algorithmic justice evolved after participatory lesson design, emphasizing situated, critical, and agentic perspectives within educational contexts.

Contribution

It reveals how participatory AI auditing lessons can shift teachers' views towards more grounded, critical, and action-oriented understandings of AI/ML systems.

Findings

Teachers' perspectives became more situated and context-aware.

Teachers developed a more critical view of AI harms.

Teachers emphasized their role in promoting algorithmic justice.

Abstract

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.