Behnam Mohammadi, a PhD student at the Tepper School of Business, focuses on human-AI interaction, large language models (LLMs), and AI regulation. His research includes developing LLM tools for businesses, like Pel, a programming language for coordinating AI agents, aimed at helping small businesses leverage AI. Mohammadi has published significant studies on AI transparency and creativity, demonstrating how certain regulations may affect consumer welfare and creativity in AI systems. He leads the BEACON project, which aims to democratize AI for small businesses, making advanced tools accessible to enhance their operations. Mohammadi will join the University of Texas at Dallas as a faculty member in Quantitative Marketing in fall 2025.
Exploring Human-AI Interaction: Behnam Mohammadi’s Research Journey
April 11, 2025
PhD student Behnam Mohammadi is making waves in the field of human-AI interaction, particularly with large language models (LLMs) and AI regulations. As a sixth-year doctoral student at the Tepper School of Business, Mohammadi is set to join the University of Texas at Dallas as a faculty member in the fall of 2025, focusing on Quantitative Marketing.
Mohammadi has a keen interest in understanding how LLMs function across various business scenarios. He believes these models are versatile tools that can enhance employee training and streamline business operations. His projects include developing LLM-powered resources for companies like PNC Bank, showcasing the practical impact of his work.
One of Mohammadi’s significant contributions is his paper on explainable AI (XAI), which challenges the common belief that transparency in AI systems always benefits consumers. His research suggests that strict requirements for explanation might hinder competition, ultimately harming consumer welfare. Instead, he advocates for more flexible regulations to preserve healthy Market dynamics.
In another groundbreaking study, Mohammadi uses Shapley values to analyze LLM behavior, revealing how seemingly minor parts of text can significantly influence AI decisions. This research sheds light on potential biases and emphasizes the importance of understanding AI decision-making processes.
On the creativity front, Mohammadi’s exploration into language model alignment reveals an unexpected trade-off: making AI safer and more reliable may limit its creative output. For businesses, this insight helps in choosing the right AI tools based on their needs, whether for predictable responses or creative tasks.
Beyond research, Mohammadi has developed Pel, an innovative programming language tailored for LLMs. Unlike traditional programming languages, Pel is designed specifically for AI agents, allowing them to collaborate effectively. Through the BEACON project, he aims to democratize AI access for small businesses. This initiative helps them compete in an increasingly competitive landscape by leveraging advanced AI capabilities.
As Mohammadi looks ahead, he’s excited about how agentic AI can take on mundane tasks, giving humans more time for meaningful work. His vision for the future includes a world where AI not only assists but empowers individuals and businesses alike.
In summary, Behnam Mohammadi’s research highlights the importance of understanding LLM behavior and striving for a balance between safety and creativity in AI applications. His work is paving the way for more intelligent business operations, making exciting advancements in human-AI interaction more accessible for everyone.
Primary keyword: human-AI interaction
Secondary keywords: large language models, AI regulations, explainable AI
What is Behnam Mohammadi’s research about?
Behnam Mohammadi focuses on how large language models (LLMs) can interact with humans. His work at the Tepper School of Business explores how people and AI can work together more effectively.
Why are large language models important?
Large language models are important because they can understand and generate text like humans. They help in various areas, such as customer service, education, and business decision-making.
How does human-AI interaction impact businesses?
Human-AI interaction can greatly improve business operations. With better communication between humans and AI, companies can enhance productivity, make smarter decisions, and improve customer experiences.
What methods does Behnam use in his research?
Behnam uses a combination of surveys, experiments, and data analysis. This helps him understand how people perceive and interact with AI systems in real-world scenarios.
How can I learn more about his research?
You can learn more about Behnam’s research by visiting the Tepper School of Business website or looking for his published papers. Following his work will keep you updated on the latest findings in LLMs and AI interaction.