Multimodal AI systems promise more natural, expressive, and creatively rich interactions, yet they also complicate how users convey intent and maintain control. As these systems rapidly enter everyday practice, agency becomes shared between humans and machines, raising urgent questions about: fairness, trustworthiness, responsibility, and accountability; calibrated trust and appropriate reliance; creative authorship and co-creation; meaningful human–AI decision-making. The goal of this workshop is to clarify the concept of distributed agency and explore its implications for human and artificial creativity. Through presentations and hands-on activities, participants will collectively develop human-centered design pathways for multimodal AI interfaces.

Automatic systems are increasingly used to assess the originality of responses in creative tasks. They offer a potential solution to key limitations of human assessment (cost, fatigue, and subjectivity), but there is preliminary evidence of a self-preference bias. Accordingly, automatic systems tend to prefer outcomes that are more closely related to their style, rather than to the human one. In this paper, we investigated how Large Language Models (LLMs) align with human raters in assessing the originality of responses in a divergent thinking task. We analysed 4,813 responses to the Alternate Uses Task produced by higher and lower creative humans and ChatGPT-4o. Human raters were two university students who underwent intensive training. Machine raters were two specialised systems fine-tuned on AUT responses and corresponding human ratings (OCSAI and CLAUS) and ChatGPT-4o, which was prompted with the same instructions as human raters. Results confirmed the presence of a self-preference bias in LLMs. Automatic systems tended to privilege artificial responses. However, this self-preference bias disappeared when the analyses controlled for the idea elaboration. We discuss theoretical and methodological implications of these findings by highlighting future directions for research on creativity assessment.

The application of generative artificial intelligence in Creativity Support Tools (CSTs) presents the challenge of interfacing two black boxes: the user’s mind and the machine engine. According to Artificial Cognition, this challenge involves theories, methods, and constructs developed to study human creativity. Consistently, the paper investigated the relationship between semantic networks organisation and idea originality in Large Language Models. Data was collected by administering a set of standardised tests to ChatGPT-4o and 81 psychology students, divided into higher and lower creative individuals. The expected relationship was confirmed in the comparison between ChatGPT-4o and higher creative humans. However, despite having a more rigid network, ChatGPT-4o emerged as more original than lower creative humans. We attributed this difference to human motivational processes and model hyperparameters, advancing a research agenda for the study of artificial creativity. In conclusion, we illustrate the potential of this construct for designing and evaluating CSTs.

Imagining the future of HRI is crucial for anticipating ethical, social, and technological challenges. This paper proposes a Speculative News workshop procedure to engage high school students in discussion and reflection while creating the front page of a newspaper published in 2125. Results from 40 participants explored robotic imaginaries highlighting societal issues, human attitudes toward robots, and technological feasibility. The results open questions that expand HRI knowledge beyond artefacts and deep into societies. In conclusion, we reflect on how Speculative News can foster participatory and inclusive design.