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AI and Transactive Memory Systems

Tepper School doctoral student Pim Assavabhokhin speaks with Professor Emily DeJeu about how AI can enhance short-term decision accuracy and transactive memory systems to improve team speed. Still, over-reliance on technology can create a dependency. Pim Assavabhokhin is a third-year Ph.D. student in Organizational Behavior and Theory at the Tepper School of Business at Carnegie Mellon University. Working with Professor Linda Argote and Professor Catherine Shea, her research examines human AI teaming, focusing on how AI systems help teams maintain and coordinate knowledge when transactive memory systems, or the shared understanding of who knows what, are strong, as well as when they break down. She conducts both laboratory and field experiments to understand how AI can support learning, performance, and knowledge continuity in teams. In a related stream of research, Pim investigates how AI reshapes advice seeking and impression management at work, drawing on network theory to examine whether seeking advice from colleagues undermines perceptions of competence and whether AI can serve as a private alternative that preserves access to information without incurring reputational costs. Working papers: Assavabhokhin, A., Shea, C, Argote, L. (2025). Artificial Intelligence and Transactive Memory Systems [Working paper] Assavabhokhin, A., Shea, C, Argote, L. (2025). Examining the Role of AI in Overcoming Workplace Advice-Seeking Barriers [Working paper] https://www.tepperspetives.cmu.edu https://www.cmu.edu/tepper