The use of Artificial Intelligence (AI) in creative practice has expanded rapidly, among other fields, in User Experience (UX) and User Interface (UI) design where AI systems are increasingly explored as tools to support design activities. In this poster, we present an exploratory study on human-AI interaction in UI prototyping involving five experienced professionals and five novice UX designers, analyzing their interaction with AI throughout the prototyping process. Our preliminary results provide insights into how designers with varying level of expertise engage with AI systems in high-fidelity prototyping, and how such interaction shapes the iterative design process. Our initial findings further include suggested analytical perspectives and point to future research directions.

Large Language Models (LLMs) aim to mimic a natural form of human conversation, likely contributing to an anthropomorphic perception of AI in contrast to conventional human-computer interfaces. Our study explores human-AI conversations and humans’ perception of their counterpart in a collaborative mystery solving task with Anthropic’s Claude 3.5 Sonnet v2 model. We collected self-report data on participants’ perception of the interaction, measured task performance, and analyzed conversational dynamics using LLM-based emotion coding. We found that humans’ perception of AI, ranging from that of a teammate or colleague to a tool, did not necessarily impact performance in mystery solving, but correlated with aspects of the interaction itself. When participants perceived the AI as a teammate or colleague, they felt a stronger sense of team cohesion and their conversations were more collaborative, with more positive emotions. These findings may help practitioners design human-AI interfaces that foster positive interactions without endangering performance.

Recent years have brought immense progress in the development of AI technology. This broadened its application fields but also led to a surge of interest in many research domains and increasing significance of human-AI relations for the development of AI technology. This rapid growth and evolvement is reflected by the establishment of a great variety of terms, potentially leading to what is known as jingle and jangle fallacies. With our scoping review of the terminology used in scientific literature to describe human-AI relations and its evolvement over time (with 803 records screened, 658 finally included), we capture the variety and development of human-AI terminology in accordance with the shift from interaction to collaboration between humans and AI. We aim to raise awareness of these developments spanning over different research communities and provide a solid basis for future researchers and practitioners conducting complementary, cross-domain research. Our review comprises terminological, bibliometric and thematic analyses, e.g., reporting on the historical development of terms and term composition patterns, but also identifying key authors and publications, geographic distribution of relevant research, and elaborating on term conception and usage, and co-occurrences throughout the literature.

Recent years have brought immense advancements around development of Artificial Intelligence (AI) technology and its application. This has also led to a boost in interest on human-AI collaboration and co-creation. Specifically in the creative field, where it is usually not sufficient for an AI to solve given problems or produce deterministic output, this next-level interaction between humans and AI comes with huge potential but also major challenges, related to aspects such as role and power distribution, trust and reliance, or efficiency and effectiveness. In this paper, we present a pilot study on human-AI collaboration in three different creative fields (programming, marketing texting, and UI design), addressing User Experience, Technology Acceptance and, specifically, Perception of Collaboration. The study is based on a theoretical framework we derived from prior research through a focused, systematic literature review, and intended to raise research questions and identify related hypotheses informing future empirical work.