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

2026-02-17

AI Summary

This study addresses the widespread lack of deep understanding among high school computer science teachers regarding AI/ML systems and algorithmic fairness, which hinders effective AI literacy education. Through a year-long participatory design process with five experienced teachers, the research developed and implemented a novel curriculum centered on AI auditing—the systematic interrogation of AI/ML systems. This work represents the first integration of AI auditing into secondary education, enabling teachers to reconceptualize algorithmic justice through three interrelated dimensions: situatedness, criticality, and agency. Qualitative analysis reveals that participants developed more contextually grounded understandings of AI systems, significantly strengthened their critical awareness, and demonstrated a clear willingness to act as educators advocating for algorithmic justice within their school communities.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• AI/ML systems
• algorithmic justice
• AI literacy
• high school CS teachers
• AI auditing

Innovation

• AI auditing
• participatory design
• algorithmic justice
• AI literacy
• teacher agency