Tracing Everyday AI Literacy Discussions at Scale: How Online Creative Communities Make Sense of Generative AI

TL;DR

A large-scale analysis of Reddit conversations from 80 creative-oriented subreddits over a three-year period suggests that AI literacy is dynamic, practice-driven, and event-responsive rather than static or purely conceptual.

Abstract

Developing AI literacy is increasingly urgent as generative AI reshapes creative practice. Yet most AI literacy frameworks are top-down and expert-driven, overlooking how literacy emerges organically in creative communities. To address this gap, we performed a large-scale analysis of 122k Reddit conversations from 80 creative-oriented subreddits over a three-year period. Our analysis identified four consistent themes in AI literacy-related discussions, and we further traced how discourse shifted alongside major AI events. Surprisingly, creators primarily frame AI literacy around how to use tools effectively, foregrounding practice and task skills, while discussions of AI capabilities and ethics surge only around high-profile events. Our findings suggest that AI literacy is dynamic, practice-driven, and event-responsive rather than static or purely conceptual. This study provides insights for researchers, designers, and policymakers to develop learning resources, community support, and policies that better promote AI literacy in creative communities.

Figures (9)

• Figure 1: Overview of our mixed-methods pipeline. We collected 122k Reddit posts and 1.5m comments, identified 6 initial themes after merging topic modeling results on posts, and then conducted qualitative analysis on 900 conversations. Through this analysis, we refined the themes and arrived at 8 final themes (see Figure \ref{['fig:process']}), including 7 AI-related themes and 1 unrelated. We then classified all conversations, conducted a temporal analysis of 7 AI-related theme dynamics over time, and focused on reporting 4 more literacy-related themes among them in the results section.
• Figure 2: Overview of how the topic-modeled theme Prompting Practices & Refinement was reorganized during qualitative analysis. The theme was divided into prompt sharing---when users post full prompts as examples of prompt crafting and prompt feedback---when users share prompts to seek suggestions or improve results. These subcategories were then folded into the broader Tool Literacy theme, with prompt feedback ultimately placed under the help-seeking subcategory.
• Figure 3: We identified 8 themes through qualitative analysis. During content classification, each conversation was labeled with one of these 8 themes. Among them, T1–T7 represent AI-related content, while T8 (Not-related Content) was excluded from the temporal analysis. In the results section, we focus on reporting themes T1–T4, as these themes are more directly connected to AI literacy.
• Figure 4: The top panel (a) shows the raw count of AI literacy conversations over time, with major AI tool releases, controversies, and platform events annotated. Key spikes align with high-impact moments such as the launch of ChatGPT. The bottom panel (b) isolates trends in Capacity Awareness, Ethics and Responsible Use, and Community Engagement, as these themes are often overshadowed by the dominant focus on Tool Literacy. Please note that subcharts (a) and (b) are using different y-axis scales.
• Figure 5: Trends in AI literacy discourse over time. Panel (a) shows the relative distribution of AI literacy conversations, annotated with major AI tool releases, controversies, and platform events. Tool Literacy remained the dominant theme throughout the observation period, accounting for approximately 55–60% of discussions. A smaller share of conversations (around 4–7%) is about capacity awareness and ethical considerations. Panel (b) isolates the relative shares of Capacity Awareness, Ethics and Responsible Use, and Community Engagement, highlighting their fluctuations over the same period. Note: Panels (a) and (b) use different y-axis scales.