Category
Three-Minute Thesis
Description
Artificial intelligence (AI) chatbots are rapidly becoming integrated into everyday life, with recent data indicating that approximately 29% of U.S. adults have used AI in the past year (Google & Ipsos, 2025). While AI tools are often utilized for productivity purposes (e.g., drafting emails, taking meeting minutes, etc.), younger populations are beginning to use them for personal conversations, companionship, and emotional/mental support. Recent research suggests that OpenAI’s ChatGPT platform might be the largest provider of mental health services in the United States of America (Rousmaniere et al., 2025). This development presents a unique opportunity to examine how use of AI chatbots affects one’s social health—particularly among university students, a population already at elevated risk for loneliness (Active Minds & TimelyCare, 2024; Lasgaard et al., 2016). The overall objective of this study is to explore the relationship between AI chatbot use and social connectedness among university students. To do this, the study will employ a cross-sectional mixed-methods design. Quantitative measures will include survey questions to assess chatbot usage frequency, motivations for use, emotional reliance, anthropomorphism of chatbots, and social substitution, as well as the Social Connectedness Instrument (SCI) developed by Kelley et al. (2025). Qualitative data will be collected through open-ended prompts and analyzed using thematic coding procedures to identify major patterns in participant experiences. Given the novel technology that spurred this research, this study will serve as a proof of concept upon which future studies can build loneliness interventions, AI addiction measures and interventions, and theories of human-computer interaction dynamics.
Social Connectedness in the Age of AI: Chatbot Use and Loneliness Among University Students
Three-Minute Thesis
Artificial intelligence (AI) chatbots are rapidly becoming integrated into everyday life, with recent data indicating that approximately 29% of U.S. adults have used AI in the past year (Google & Ipsos, 2025). While AI tools are often utilized for productivity purposes (e.g., drafting emails, taking meeting minutes, etc.), younger populations are beginning to use them for personal conversations, companionship, and emotional/mental support. Recent research suggests that OpenAI’s ChatGPT platform might be the largest provider of mental health services in the United States of America (Rousmaniere et al., 2025). This development presents a unique opportunity to examine how use of AI chatbots affects one’s social health—particularly among university students, a population already at elevated risk for loneliness (Active Minds & TimelyCare, 2024; Lasgaard et al., 2016). The overall objective of this study is to explore the relationship between AI chatbot use and social connectedness among university students. To do this, the study will employ a cross-sectional mixed-methods design. Quantitative measures will include survey questions to assess chatbot usage frequency, motivations for use, emotional reliance, anthropomorphism of chatbots, and social substitution, as well as the Social Connectedness Instrument (SCI) developed by Kelley et al. (2025). Qualitative data will be collected through open-ended prompts and analyzed using thematic coding procedures to identify major patterns in participant experiences. Given the novel technology that spurred this research, this study will serve as a proof of concept upon which future studies can build loneliness interventions, AI addiction measures and interventions, and theories of human-computer interaction dynamics.
