Valdemar Danry

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.

Given the growing prevalence of fake information, including increasingly realistic AI-generated news, there is an urgent need to train people to better evaluate and detect misinformation. While interactions with AI have been shown to durably reduce people’s beliefs in false information, it is unclear whether these interactions also teach people the skills to discern false information themselves. We conducted a month-long study where 67 participants classified news headline-image pairs as real or fake, discussed their assessments with an AI system, followed by an unassisted evaluation of unseen news items to measure accuracy before, during, and after AI assistance. While AI assistance produced immediate improvements during AI-assisted sessions (+21% average), participants’ unassisted performance on new items declined significantly by 15.3% in week 4 compared to week 0. These results indicate that while AI may help immediately, it may ultimately degrade long-term misinformation detection abilities.

Everyday conversations are more than exchanges of words—they reveal how people think, react, and adapt across situations. Through our speech we reveal the cognitive patterns that shape our behavior over time. Yet much of these patterns remains implicit: people operate through recurring heuristics and habits—some helpful, some limiting, many unnoticed. However, identifying such patterns requires self awareness that humans struggle with—and that current user modeling systems, often fragmented and narrowly tailored to specific tasks, fail to capture. Recognizing these patterns can enable user support systems to go beyond reactive assistance towards anticipatory support. We present Mind Mapper, an always-on wearable AI system that mines behavioral patterns from everyday real-life conversations. Mind Mapper employs a multi-stage LLM pipeline to generate, refine, and evaluate human-readable behavioral patterns. In a field study with 12 participants capturing over 700 hours of real-life conversational data, Mind Mapper generated behavioral patterns that participants consistently rated as accurate, unique, and helpful for reflection and behavior change. We further illustrate how such behavioral pattern modeling might enable new forms of human-AI interaction—anticipating user behavior through proactive interventions, adaptive content delivery, cognitive reframing, and behavioral simulation. Our results show the potential of always-on wearable systems with LLM-driven user models to support new forms of cognitively scaffolding, context-aware human-AI interactions.

With the rapid development of advanced machine learning methods for spatial reconstruction, it becomes important to understand the psychological and emotional impacts of such technologies on autobiographical memories. In a within-subjects study, we found that allowing users to walk through old spaces reconstructed from their videos significantly enhances their sense of traveling into past memories, increases the vividness of those memories, and boosts their emotional intensity compared to simply viewing videos of the same past events. These findings highlight that, regardless of the technological advancements, the immersive experience of VR can profoundly affect memory phenomenology and emotional engagement. As systems enabling immersive memory reconstruction become more ubiquitous, it is crucial to critically examine their effects on human cognition and perception of reality.