Text-based prompting remains the predominant interaction paradigm in generative AI, yet it often introduces friction for novice users such as small business owners (SBOs), who struggle to articulate creative goals in domain-specific contexts like advertising. Through a formative study with six SBOs in the United Kingdom, we identify three key challenges: difficulties in expressing brand intuition through prompts, limited opportunities for fine-grained adjustment and refinement during and after content generation, and the frequent production of generic content that lacks brand specificity. In response, we present ACAI (AI Co-Creation for Advertising and Inspiration), a multimodal generative AI tool designed to support novice designers by moving beyond traditional prompt interfaces. ACAI features a structured input system composed of three panels: Branding, Audience and Goals, and the Inspiration Board. These inputs allow users to convey brand-relevant context and visual preferences. This work contributes to HCI research on generative systems by showing how structured interfaces can foreground user-defined context, improve alignment, and enhance co-creative control in novice creative workflows.

Sign language machine translation (SLMT) -- the task of automatically translating between sign and spoken languages or between sign languages -- is a complex task within the field of NLP. Its multi-modal and non-linear nature require the joint efforts of sign language (SL) linguists, technical experts and SL users. Effective user involvement is a challenge that can be addressed through co-creation. Co-creation has been formally defined in many fields, e.g. business, marketing, educational and others, however in NLP and in particular in SLMT there is no formal, widely accepted definition. Starting from the inception and evolution of co-creation across various fields over time, we develop a relationship typology to address the collaboration between deaf, Hard of Hearing and hearing researchers and the co-creation with SL-users. We compare this new typology to the guiding principles of participatory design for NLP. We, then, assess 110 articles from the perspective of involvement of SL users and highlight the lack of involvement of the sign language community or users in decision-making processes required for effective co-creation. Finally, we derive formal guidelines for co-creation for SLMT which take the dynamic nature of co-creation throughout the life cycle of a research project into account.

This study explores a new method in food development by utilizing AI including generative AI, aiming to craft products that delight the senses and resonate with consumers' emotions. The food ingredient recommendation approach used in this study can be considered as a form of multimodal generation in a broad sense, as it takes text as input and outputs food ingredient candidates. This Study focused on producing "Romance Bread," a collection of breads infused with flavors that reflect the nuances of a romantic Japanese television program. We analyzed conversations from TV programs and lyrics from songs featuring fruits and sweets to recommend ingredients that express romantic feelings. Based on these recommendations, the bread developers then considered the flavoring of the bread and developed new bread varieties. The research included a tasting evaluation involving 31 participants and interviews with the product developers. Findings indicate a notable correlation between tastes generated by AI and human preferences. This study validates the concept of using AI in food innovation and highlights the broad potential for developing unique consumer experiences that focus on emotional engagement through AI and human collaboration.

This paper explores the potential for utilizing generative AI models in group-focused co-creative frameworks to enhance problem solving and ideation in business innovation and co-creation contexts, and proposes a novel prompting technique for conversational generative AI agents which employ methods inspired by traditional 'human-to-human' facilitation and instruction to enable active contribution to Design Thinking, a co-creative framework. Through experiments using this prompting technique, we gather evidence that conversational generative transformers (i.e. ChatGPT) have the capability to contribute context-specific, useful, and creative input into Design Thinking activities. We also discuss the potential benefits, limitations, and risks associated with using generative AI models in co-creative ideation and provide recommendations for future research.