Enhancing behavioral nudges with large language model-based iterative personalization: A field experiment on electricity and hot-water conservation

1. 1Develop an LLM agent for iterative personalization
2. 2Test the LLM agent in a field experiment with multiple intervention rounds
3. 3Analyze the effectiveness of the LLM-based approach compared to traditional nudging methods
4. 4Refine the LLM agent based on the results and iterate on the personalization process

Data scientists and AI engineers on a team can benefit from this research as it demonstrates the potential of LLMs in behavioral change interventions, and product managers can apply these insights to design more effective personalized feedback systems

💡 LLMs can reduce cognitive work for recipients by generating tailored guidance and updating it across intervention rounds

💡 LLMs can enhance behavioral nudges with personalized guidance & iterative updates