Funhouse Mirror or Echo Chamber? A Methodological Approach to Teaching Critical AI Literacy Through Metaphors

TL;DR

This paper introduces a methodological approach combining Conceptual Metaphor Theory with UNESCO's AI framework to develop effective metaphors for teaching Critical AI Literacy, enhancing understanding of AI's societal impacts.

Contribution

It presents a systematic method for selecting pedagogically effective metaphors aligned with AI literacy standards, including four key metaphors for teaching critical understanding of AI.

Findings

Identified four key metaphors for teaching AI literacy

Developed criteria for selecting pedagogically appropriate metaphors

Created interactive activities to engage students in AI concepts

Abstract

As educational institutions grapple with teaching students about increasingly complex Artificial Intelligence (AI) systems, finding effective methods for explaining these technologies and their societal implications remains a major challenge. This study proposes a methodological approach combining Conceptual Metaphor Theory (CMT) with UNESCO's AI competency framework to develop Critical AI Literacy (CAIL). Through a systematic analysis of metaphors commonly used to describe AI systems, we develop criteria for selecting pedagogically appropriate metaphors and demonstrate their alignment with established AI literacy competencies, as well as UNESCO's AI competency framework. Our method identifies and suggests four key metaphors for teaching CAIL. This includes GenAI as an echo chamber, GenAI as a funhouse mirror, GenAI as a black box magician, and GenAI as a map. Each of these seeks to address specific aspects of understanding characteristics of AI, from filter bubbles to algorithmic opacity. We present these metaphors alongside interactive activities designed to engage students in experiential learning of AI concepts. In doing so, we offer educators a structured approach to teaching CAIL that bridges technical understanding with societal implications. This work contributes to the growing field of AI education by demonstrating how carefully selected metaphors can make complex technological concepts more accessible while promoting critical engagement with AI systems.