Generative AI systems such as ChatGPT are increasingly used to assist with tasks or to receive information. Given that the systems are not perfectly reliable regarding the content they produce, human users need to carefully navigate to which degree they can place trust in the system (calibrated trust). However, based on media equation assumptions, it can be hypothesized that social cues displayed by the system might instill more trust than is warranted. Against this background, the present study investigates in a 2 × 2 between subject design ( N = 617) whether the social cues “typing behavior” and “personalized address” used by ChatGPT increase perceived trust (benevolence, ability, and integrity) in the system and whether this is mediated by perceived similarity and moderated by anthropomorphism inclination. The results show that the social cue “typing behavior” leads to a significant increase in trust in the dimension benevolence. Neither perceived similarity nor anthropomorphism inclination modulates this effect. However, as a side effect, perceived similarity was found to significantly predict trust in ChatGPT.

Textile surfaces, such as on sofas, cushions, and clothes, offer promising alternative locations to place controls for digital devices. Textiles are a natural, even abundant part of living spaces, and support unobtrusive input. While there is solid work on technical implementations of textile interfaces, there is little guidance regarding their design—especially their haptic cues, which are essential for eyes-free use. In particular, icons easily communicate information visually in a compact fashion, but it is unclear how to adapt them to the haptics-centric textile interface experience. Therefore, we investigated the recognizability of 84 haptic icons on fabrics. Each combines a shape, height profile (raised, recessed, or flat), and affected area (filled or outline). Our participants clearly preferred raised icons, and identified them with the highest accuracy and at competitive speeds. We also provide insights into icons that look very different, but are hard to distinguish via touch alone.