From Reddit to Policy Insights: Using AI to Summarize Public Opinion on Robotics Courses

• In Event: . From Text to Evidence: AI-Assisted Analysis and Measurement: Using AI to turn documents and online discussions into research findings

Session Submission Type: AI Peer Learning Lab

Abstract

As schools expand robotics and engineering courses, teachers, parents, and students are discussing them candidly in online communities such as Reddit, often more honestly than in surveys or public hearings. This tutorial demonstrates a practical, low-cost workflow for collecting these discussions and using AI to answer two questions policy researchers often ask: What are people talking about, and how do they feel about it? The 20-minute session walks through a real project pipeline using K-12 robotics and engineering courses as the running example, in three stages: (1) collecting posts and comments from education-related subreddits (e.g., r/Teachers, r/AskEngineers) using Reddit's API with a short Python script in Google Colab; (2) using large language models and topic modeling to cluster discussions and label the main themes, such as student engagement, teacher preparedness, curriculum resources, and funding constraints; and (3) applying AI-assisted sentiment analysis to classify opinions as positive, negative, or mixed. For example, this step can distinguish enthusiasm about student engagement from frustration over being assigned to teach robotics without training or equipment. Results are then aggregated into a readable summary of public sentiment. Interactive elements: participants will suggest a policy-relevant subreddit at the start for a live demonstration, and a midway discussion invites participants to share how they gauge public opinion in their own work. Learning outcomes: participants will leave able to (a) set up Reddit data collection with minimal coding, (b) apply an AI-assisted workflow to summarize public opinions and sentiment on any policy topic, and (c) recognize key pitfalls, including sampling bias in online communities, terms-of-service and ethics considerations, and the need to validate AI-generated labels against raw data. A shared Google Colab notebook with annotated code, prompts, and a sample dataset will be provided.

Policy Area

• AI Peer Learning Lab

Facilitator

• Jialing Wu, The Ohio State University