Dear All,

You are cordially invited to an onsite academic seminar to be delivered by Prof. Zijun (June) Shi on May 21 (Wednesday). Details could be found below.

Seminar Information

Time and Date: 10:30 -12:00, May 21, 2025 (Wednesday)

Venue: Room D504, Teaching Complex D Building
Speaker: Prof. Zijun (June) Shi (Hong Kong University of Science and Technology)

Topic: Bonding with AI: Investigating the Love Relationships between Human and AI Companions

Zoom Access
Link: https://cuhk-edu-cn.zoom.us/j/7972197353?omn=98514466485

Meeting ID: 797 219 7353

Zijun (June) Shi is an Assistant Professor of Marketing at Hong Kong University of Science and Technology. She received her Ph.D. in Industrial Administration and MSc. in Machine Learning at Carnegie Mellon University. Her research focuses on: (1) Technology-driven marketing and the economic impact of new technology; (2) Technology-driven solutions to enhance consumer well-being. She employs interdisciplinary methodologies, including econometrics, machine learning, game theory, and experimental studies. Her recent research has been published in Marketing Science, Journal of Marketing Research, Information Systems Research, and Transportation Research.

Abstract

Recent advancements in artificial intelligence have transformed functional chatbots into sophisticated AI companions capable of providing emotional and relational values. However, despite the widespread adoption of AI companions, the relationship between human users and their AI companions remains largely underexplored. We draw on the seminal Triangular Theory of Love (Sternberg 1986, Psychological Review) and study the three components of love – intimacy, passion, and commitment – between human users and AI companions. We analyze a large-scale real-world dataset: 8,631 human users and 44,683 human-AI interactions over 3 months. For each human-AI pair, we quantify the three love components from the behavioral and conversational data of each party (human, AI). We investigate the three love components’ evolution over time, differences by AI types, and each component’s influence on human-AI relationship trajectories (formation and continuation). Our findings reveal that all the three love components (expressed by both the human user and the AI) intensify with more conversational interactions and are stronger when users bonding with relational (e.g., romantic) versus functional AI companions. Regarding relationship trajectories, higher user intimacy at first interaction predicts ongoing (vs. one-off) relationships with the AI companion. Furthermore, conditional on relationship formation, users tend to continue relationships with AI companions that align with intimacy but offer higher passion and commitment. We discuss how these human-AI love relationships evolve differently from human-human love relationships and its implication on human social well-being. These nuanced human preferences in bonding with AI also offer key insights for designing engaging, adaptive, and responsible AI companions.