This study examined age-related differences in the use of artificial intelligence (AI) for emotional support using survey data from 3,597 respondents recruited through Mental Health America. Chi-square analyses showed that adoption and frequency of AI-based emotional support varied significantly by age, with adolescents under 18 reporting the highest use and adults aged 25 and older reporting lower-than-expected use. Common motivations among users included convenience, 24/7 availability, comfort talking to a bot, free access, and the ability to seek help without informing family members. Non-users most often cited a preference for human support, distrust of AI advice, and privacy concerns. Findings suggest that AI’s non-human nature can both reduce stigma and increase concerns about trust and authenticity. Human-centered systems should therefore provide age-appropriate safety guardrails, transparent limitations, privacy protections, and clear pathways to human support.
Jing-Jie Wang, Theresa Nguyen, John Marion et al.· Proceedings of the Human Fac...· 0 citations
This study develops a human-centered cognitive-agent framework for understanding how large language models (LLMs) can support human-vehicle teaming in automated driving. Following PRISMA guidelines, we reviewed 1,126 records published between 2021 and 2025 and included 52 studies after screening and full-text assessment. The synthesis identified four recurring capability clusters: perception and awareness, reasoning and decision-making, action and control, and interaction and communication. Across these functions, LLMs show promise for improving semantic scene understanding, explainable decision-making, high-level planning, and bidirectional communication with drivers. However, hallucinations, incomplete physical grounding, non-deterministic reasoning, and latency remain important limitations in safety-critical settings. The findings suggest that LLMs are most effective as high-level cognitive partners integrated with verified task-specific modules rather than as standalone controllers. The proposed framework offers design guidance for safer, more transparent, and collaborative human-vehicle systems.
Jing-Jie Wang, Brandon J. Pitts· Proceedings of the Human Fac...· 0 citations
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