Graduate Student Uses Claude to Build AI Image Detection Experiment

A graduate student at The New School in New York has built a research experiment called InPixelsWeTrust.org in collaboration with Claude to study how people judge visual evidence online. The experiment focuses on understanding how certainty and accuracy diverge when evaluating images, particularly with the growing prevalence of synthetic media.
Experiment Details
The experiment follows a simple structure:
- Users get 6 rounds of testing
- For each round, they have 10 seconds to decide whether an image is real or AI-generated
- After each decision, users rate how confident they felt
The entire process takes under 2 minutes and is completely anonymous with no personal data collected. The researcher is seeking participation from a wide range of respondents beyond academic circles.
Technical Implementation
According to the source, Claude was involved at every stage of development:
- Front-end code and responsive design
- Data pipeline that sends all results to a Google Sheet for analysis
The researcher noted that Claude was "awesome to work with" throughout the collaboration. The website serves as a practical example of how AI coding assistants can be used to build complete web applications for research purposes, from user interface to data collection infrastructure.
📖 Read the full source: r/ClaudeAI
👀 See Also

Qwen3-VL-32B-Instruct excels at multimodal flashcard grading
A developer tested Qwen3-VL-32B-Instruct for grading image-occluded Anki flashcards and found it outperformed models like Gemini 2.5 Flash, GPT 5 Nano/Mini, XAI 4.1 Fast, GLM, and Mistral models, with only ChatGPT 5.2 and Gemini 3/3.1/Claude 4+ coming close.

OpenClaw user reports improved utility after connecting to documentation via MCP
A user found their OpenClaw setup became significantly more useful after connecting it to their documentation using yavy.dev for indexing and MCP for integration, moving beyond generic question-answering to specific troubleshooting and configuration assistance.

How Fragile Test Scripts Caused Release Delays and What One Team Did About It
A team of about 15 engineers discovered their Appium test suite was consuming 50-60% of their QA engineer's time just for maintenance after a UI refresh broke locators, causing two releases to slip. They're now rebuilding tests using a tool that reads screens like a human and adapts to UI changes.

OpenClaw AI agent helps team salvage demo day with rapid prototype
A development team used OpenClaw's AI agent to build a working demo website with mock data in 10 minutes after their product pivot threatened their demo day participation at South Park Commons.