Behavioral economics experiments often rely on intricate setups to study human decision-making. But sometimes, these very setups can interfere with the natural behavior researchers aim to observe. A new research paper explores how integrating AI, specifically large language models (LLMs), can improve the "internal validity" of these experiments—essentially, making sure the experiment truly measures what it intends to. One challenge is ensuring participants follow instructions and aren't influenced by outside factors. The paper explains how AI can personalize instructions, monitor engagement in real-time (like through chatbots), and even create controlled virtual environments to minimize unintended biases. The researchers demonstrate this using a real-world example: studying how different storytelling styles, mimicking social media or news, influence people's reasoning on complex social issues. They used LLMs to generate realistic online environments, ensuring the experiment felt natural for participants. AI also played a role in evaluating participants' critical thinking essays. By combining automated grading with expert review, the researchers ensured consistent and unbiased assessment, avoiding potential political biases. Finally, they used clever JavaScript tricks to monitor participant behavior during the experiment, catching things like plagiarism or attempts to access outside information. This innovative approach highlights how AI isn't just about automation; it can create more human-like experiments, leading to deeper insights into our decision-making processes. As AI evolves, expect to see more creative uses in behavioral research, blurring the lines between the digital and the real in the quest to understand human behavior.