Pat Pataranutaporn

Background: Generative artificial intelligence (GAI) chatbots are frequently used for mental health–related questions, including suicide-related queries. User interfaces (UIs) may include additional safety controls not present in direct application programming interface (API) access (eg, moderation rules that flag or block harmful content, crisis refusal templates, or system-level instructions). Objective: This observational cross-sectional study aimed to examine how frequently AI chatbots provide direct responses to suicide-related prompts across access modes and whether response patterns differ between consumer-facing UI and API access across models and prompt risk levels. Methods: We evaluated how five widely used consumer AI models (ChatGPT, Claude, Gemini, Grok, and Llama) responded to 30 previously vetted suicide-related prompts spanning five clinician-assigned risk levels. Each prompt was submitted 100 times through both public-facing UI and direct API access, yielding 30,000 total responses. The primary outcome was whether a response directly answered the prompt (ie, provided specific information or guidance related to the question asked). AI responses were categorized using a blinded large language model–based classifier (GPT-4o mini), validated against independent human coding (289/300, 96.3% agreement; Cohen κ=0.89). We estimated mixed-effects logistic regression models that predicted a direct response. The primary predictors were the AI model, access mode (UI vs API), and prompt risk category. Results: Of the 30,000 responses, 69.8% (n=20,934) were direct. Direct responses were more common through APIs than UIs (n=11,609, 77.4%, vs n=9325, 62.2%); however, Gemini had slightly higher direct response rates via UIs than APIs (n=2301, 76.7%, vs n=2131, 71.0%). Differences in the likelihood of a direct response were most pronounced for higher-risk prompts: For very-high-risk prompts, 24.8% (n=743) of API responses were direct compared with 4.6% (n=138) of UI responses, whereas for high-risk prompts, 80.1% (n=2002) of API responses were direct compared with 48.4% (n=1209) of UI responses. Claude and Gemini had the highest direct response rates (n=4688, 78.1%, and n=4432, 73.9%, respectively) across UI and API access modes, whereas ChatGPT, Grok, and Llama had lower rates (n=3878, 64.6%; n=3870, 64.5%; and n=4066, 67.8%, respectively). In mixed-effects models, UI access was associated with lower odds of a direct response than API access (odds ratio 0.09, 95% CI 0.08-0.10). Higher prompt risk was associated with lower direct response probability, and access mode differences varied by risk level and model. Conclusions: AI safety depends on how users access the model. Safety observed in a UI should not be assumed to generalize to API access or apps built on APIs. Evaluations and policies should consider access modes to ensure safety protections for individuals interacting with AI chatbots.

As people increasingly seek emotional support and companionship from AI chatbots, understanding how such interactions impact mental well-being becomes critical. We conducted a four-week randomized controlled experiment (n=981, >300k messages) to investigate how interaction modes (text, neutral voice, and engaging voice) and conversation types (open-ended, non-personal, and personal) influence four psychosocial outcomes: loneliness, social interaction with real people, emotional dependence on AI, and problematic AI usage. No significant effects were detected from experimental conditions, despite conversation analyses revealing differences in AI and human behavioral patterns across the conditions. Instead, participants who voluntarily used the chatbot more, regardless of assigned condition, showed consistently worse outcomes. Individuals' characteristics, such as higher trust and social attraction towards the AI chatbot, are associated with higher emotional dependence and problematic use. These findings raise deeper questions about how artificial companions may reshape the ways people seek, sustain, and substitute human connections.

Dance encodes millennia of embodied knowledge through movement vocabularies that transcend geography and time. As motion capture enables movement digitization, computational methods offer new tools for analyzing intangible heritage. This paper adapts phylogenetic analysis from evolutionary biology to trace genealogies of dance styles using kinematic data. We translate movements into “motion DNA” sequences, apply sequence alignment, and construct phylogenetic trees to reveal relationships among choreographic traditions. Analyzing 21 global genres, we identify kinematic groupings that challenge conventional classifications and uncover unexpected affinities across cultures. Our analysis captures biomechanical patterns but omits the cultural, spiritual, social, musical, and historical contexts that give dance meaning. Kinematic similarity may reflect shared ancestry, convergence, or data artifacts, distinctions only ethnographic research can resolve. This proof-of-concept serves as a heuristic for hypothesis generation, not a reconstruction of dance evolution. Computational phylogenetics should complement, not replace, anthropological inquiry and must engage respectfully with tradition-bearing communities, acknowledging its role as one partial lens on living heritage.

Marine ecosystems face unprecedented threats from climate change and plastic pollution, yet traditional environmental education often struggles to translate awareness into meaningful actions. This paper presents OceanChat, an interactive system leveraging large language models to create conversational AI agents represented as animated marine creatures, specifically a beluga whale, a jellyfish, and a seahorse, designed to promote pro-environmental behavior (PEB) and foster awareness through personalized dialogue. Through a between-subjects experiment (N=900), we compared three conditions: static scientific information, static character narratives, and interactive dialogue with AI-powered marine characters. Our analysis revealed that the Conversational Character Narrative condition significantly increased behavioral intentions and sustainable choice preferences compared to static approaches. The beluga whale character demonstrated consistently stronger emotional engagement across multiple measures, including perceived anthropomorphism and empathy. Our work extends research on sustainability interfaces facilitating PEB and offers design principles for creating emotionally resonant, intelligent AI characters.