Designing for Public Enlightenment: Enhancing Generative AI Literacy on Socio-technical Aspects in Informal Learning Spaces

Overview

This research addresses the public literacy gap regarding generative AI's socio-technical dimensions by designing and evaluating interactive learning interventions in informal educational environments. The study recognizes a critical disconnect between widespread adoption of generative AI systems and insufficient public understanding of their mechanisms, risks, and benefits, including concerns around misinformation generation, algorithmic bias, and environmental resource consumption. The intervention targets informal learning spaces such as museums, libraries, and public parks to reach diverse demographic cohorts outside traditional educational channels.

Methods and approach

The research will employ a mixed-methods investigation combining qualitative and quantitative data collection. The approach comprises three principal components: first, empirical identification of prevalent public misconceptions regarding generative AI functionality and implications through direct public engagement; second, development of a structured pedagogical framework specifically tailored to AI literacy education in non-formal settings; and third, creation of tangible, embodied interactive exhibits designed to facilitate experiential learning and foster more nuanced public understanding. The study emphasizes embodied and interactive modalities as mechanisms for knowledge construction.

Key Findings

The anticipated research outcomes include identification of public misconceptions regarding generative AI socio-technical aspects, a pedagogical framework for informal AI literacy instruction, and tangible and embodied interactive exhibits for deployment in target informal learning spaces. The research will assess whether embodied and tangible interventions can shift public understanding from superficial engagement patterns toward more informed critical engagement. The exhibits will aim to facilitate user comprehension of underlying mechanisms, systemic biases, and environmental externalities associated with generative AI systems.

Implications

The research aims to establish a model for democratized AI literacy in informal educational contexts, extending scientific understanding beyond academic and professional constituencies to broader public populations. The pedagogical framework and exhibit designs will provide transferable methodologies for institutions seeking to address AI literacy gaps. The work will contribute to public discourse by enabling informed participation in policy discussions and responsible AI system use across diverse social groups.