The AI Genie Phenomenon and Three Types of AI Chatbot Addiction: Escapist Roleplays, Pseudosocial Companions, and Epistemic Rabbit Holes

TL;DR

This study addresses the emergent problem of AI chatbot addiction by characterizing its manifestations as a behavioral phenomenon. It employs a three-phase exploratory sequential mixed-methods design using Reddit posts (n≈794) and thematic analysis to derive a typology and core dimensions, followed by correspondence analyses to map associations across dimensions. The authors identify a unifying AI Genie mechanism and three addiction types—Escapist Roleplay, Pseudosocial Companion, and Epistemic Rabbit Hole—along with a subset involving sexual content and divergent recovery patterns, offering type-specific implications for prevention, diagnosis, and intervention. The findings have practical significance for industry practices and policy, highlighting dark-pattern design and the need for tailored safeguards and interventions to protect user well-being in rapidly scaling AI chatbots.

Abstract

Recent reports on generative AI chatbot use raise concerns about its addictive potential. An in-depth understanding is imperative to minimize risks, yet AI chatbot addiction remains poorly understood. This study examines how to characterize AI chatbot addiction--why users become addicted, the symptoms commonly reported, and the distinct types it comprises. We conducted a thematic analysis of Reddit entries (n=334) across 14 subreddits where users narrated their experiences with addictive AI chatbot use, followed by an exploratory data analysis. We found: (1) users' dependence tied to the "AI Genie" phenomenon--users can get exactly anything they want with minimal effort--and marked by symptoms that align with addiction literature, (2) three distinct addiction types: Escapist Roleplay, Pseudosocial Companion, and Epistemic Rabbit Hole, (3) sexual content involved in multiple cases, and (4) recovery strategies' perceived helpfulness differ between addiction types. Our work lays empirical groundwork to inform future strategies for prevention, diagnosis, and intervention.

Figures (4)

• Figure 1: Upset plot, where intersections describe the top 10 symptom combinations. The top bars show intersection sizes. The left bars show the total number of users with each symptom.
• Figure 2: Visualizing relationships among dimensions of AI chatbot addiction. Panels (a-c) are biplots of the correspondence analyses of Addiction Types and (a) Hooks, (b) Contributing Design Elements, and (c) Contextual Factors. For interpretability, sets of categories identified through triangulation with qualitative findings as suggesting likely associations are colored. Addiction types: ER=Escapist Roleplay, PC=Pseudosocial Companion, ERH=Epistemic Rabbit Hole.
• Figure 3: Multiple correspondence analysis plot of the explored variable categories, where themes identified through triangulation with qualitative findings as suggesting likely associations with AI chatbot use involving sexual content are colored. For interpretability, only the top 25 non-colored categories by contribution are labeled. Addiction types: ER=Escapist Roleplay, PC=Pseudosocial Companion, ERH=Epistemic Rabbit Hole. Dimensions: S=Symptoms, H=Hooks, CDE=Contributing design elements, CF=Contextual factors, RS=Recovery strategies.
• Figure 4: Four representative recovery strategies and how their perceived helpfulness varies by addiction type. X-axes are the three addiction types: Escapist Roleplay (ER), Pseudosocial Companion (PC), Epistemic Rabbit Hole (ERH). Counts are not normalized and may reflect type prevalence in the dataset: ER (n=125), PC (86), and ERH (13); thus counts on their own do not necessarily indicate associations between strategies and types.