arXiv subjects

Richard Paluch

Publications and source records attributed to Richard Paluch.

3 recordsLinked to original sources

arXiv2026

Beyond Companionship: Robotic Pets as Embodied Communication Media for Older Adults

Robotic pets for older adults are typically studied as companions, with the older person positioned as the robot's primary interaction partner. This framing overlooks another role: a petlike robot can mediate relationships between people. We report a formative qualitative interview study with six adults aged 63-77 in Germany. Interviews examined participants' existing communication practices, experiences of being alone, and responses to scenarios in which a robotic pet could carry voice messages, support calls, move through the home, or convey a remote family member's actions. Thematic qualitative content analysis identified four findings. Participants maintained relationships through flexible combinations of telephone, messaging, and video calls; imagined petlike movement, proximity, voice, and touch-like gestures adding embodied presence to those channels; combined expectations of communication, companionship, and practical assistance; and made acceptance conditional on customization, hygiene, ease of use, and human control. Drawing together HCI research on mediated intimacy, CSCW research on awareness and care networks, and HRI research on social and telepresence robots, we contribute (1) an empirical account of how older adults imagine petlike embodiment carrying another person's presence, (2) the concept of robotic pets as embodied communication media, and (3) a four-part sensitizing framework organized around relational purpose, source of agency, mode of embodiment, and governance. The study identifies design hypotheses; it does not test a robot or establish effects on loneliness, depression, or relationship quality.

cs.HC↗

arXiv2026

Exploring patient trust in clinical advice from AI-driven LLMs like ChatGPT for self-diagnosis

Trustworthy clinical advice is crucial but can be burdensome to obtain. Limited access and financial costs may lead people to self-diagnose. However, self-diagnosis requires considerable learning and can create risks when people pursue treatment without professional guidance. Large language models (LLMs) such as GPT-4 may offer a convenient yet risky alternative because they can produce inaccurate but convincing information. We therefore ask whether patients can trust clinical advice from AI-driven LLMs. We examined this question through a think-aloud observation in which a patient used GPT-4 for self-diagnosis while a doctor assessed its responses using professional expertise. We then conducted a semi-structured interview with the patient about their trust in the system. Our results show that patients may struggle to identify errors because they lack professional medical knowledge, even when GPT-4 provides advice that a doctor can recognize as false. Patients may develop some trust because GPT-4 explains its responses and acknowledges its limitations, but this trust remains uncertain because its advice can be unreliable. The doctor also reported that checking GPT-4's responses required more effort than making a diagnosis without it. Patients tend to trust doctors because educated and authorized professionals can provide effective guidance. This trust also develops through social connection, certification, institutional accountability, and professional rules. Doctors can adapt their questions, observe patients, and use different methods when patients cannot clearly describe their symptoms. An LLM, however, depends primarily on the information provided in a prompt and may overlook details that a doctor could identify during a clinical consultation. These findings raise questions about competence, responsibility, autonomy, and safety when LLMs are used for clinical advice.

cs.HC↗

arXiv2023

Heteromated Decision-Making: Integrating Socially Assistive Robots in Care Relationships

Technological development continues to advance, with consequences for the use of robots in health care. For this reason, this workshop contribution aims at consideration of how socially assistive robots can be integrated into care and what tasks they can take on. This also touches on the degree of autonomy of these robots and the balance of decision support and decision making in different situations. We want to show that decision making by robots is mediated by the balance between autonomy and safety. Our results are based on Design Fiction and Zine-Making workshops we conducted with scientific experts. Ultimately, we show that robots' actions take place in social groups. A robot does not typically decide alone, but its decision-making is embedded in group processes. The concept of heteromation, which describes the interconnection of human and machine actions, offers fruitful possibilities for exploring how robots can be integrated into caring relationships.

cs.HC↗