**ContextCam: Bridging Context Awareness with Creative Human-AI Image Co-Creation** **Abstract:** This paper introduces ContextCam, a novel human-AI image co-creation system that integrates context awareness with mainstream AI-generated content (AIGC) technologies like Stable Diffusion. ContextCam leverages Large Language Model (LLM)-based multi-agents to co-create images with users, providing inspiration by extracting relevant contextual data. A study with 16 participants and 136 scenarios revealed high user satisfaction, showcasing personalized and diverse outputs, as well as interesting user behavior patterns. Participants provided positive feedback on their engagement and enjoyment, highlighting the system's ability to inspire creativity. **Key Concepts:** - Human-centered computing → Ubiquitous and mobile computing systems and tools. **Keywords:** - Human-AI Co-Creation, Context-Aware Systems, Image Generation and Editing, LLM-Based Multi-Agent Systems **Introduction:** The rapid advancement of AIGC promises to transform various aspects of human life, particularly in image creation and self-expression. ContextCam aims to enhance this process by integrating context awareness, which can inspire more personalized and engaging images. The system's workflow includes two phases: "framing" and "focusing." In the "framing" phase, ContextCam extracts relevant contextual data and proposes three themes for the user to select. In the "focusing" phase, users collaborate with AI to create and refine images through natural language commands and straightforward selections. **Related Works:** The paper reviews existing work in human-AI co-creation, context-aware systems, image generation and editing, and LLM-based multi-agent systems. It highlights the potential of integrating contextual information into human-AI collaboration to enhance creativity and personalization. **Formative Study:** A formative study with 23 participants aimed to gather insights on user motivations, needs, and expectations for a context-aware human-AI co-creation system. The study identified six design guidelines for the system, emphasizing user-friendly interfaces, high-quality image generation, and personalized recommendations. **System Design and Implementation:** ContextCam is designed as a multi-agent system, integrating LLMs and vision models to create a personalized and engaging co-creation experience. The system includes agents for context selection, topic generation, tool management, artist assistance, and personalization. The implementation details and user study results are discussed, showing high user satisfaction and effective use of contextual data. **User Study:** A real-world user study with 16 participants evaluated ContextCam's performance and user behavior. The study found high user satisfaction, with 92.9% of conversations using system-generated topics. Users reported low interaction burdens and enjoyed the system's ability to inspire creativity and personalization. Contextual information significantly influenced image theme preferences and user experiences. **Conclusion:** ContextCam demonstrates the potential of integrating context awareness with human-AI co-creation, enhancing image creation processes and**ContextCam: Bridging Context Awareness with Creative Human-AI Image Co-Creation** **Abstract:** This paper introduces ContextCam, a novel human-AI image co-creation system that integrates context awareness with mainstream AI-generated content (AIGC) technologies like Stable Diffusion. ContextCam leverages Large Language Model (LLM)-based multi-agents to co-create images with users, providing inspiration by extracting relevant contextual data. A study with 16 participants and 136 scenarios revealed high user satisfaction, showcasing personalized and diverse outputs, as well as interesting user behavior patterns. Participants provided positive feedback on their engagement and enjoyment, highlighting the system's ability to inspire creativity. **Key Concepts:** - Human-centered computing → Ubiquitous and mobile computing systems and tools. **Keywords:** - Human-AI Co-Creation, Context-Aware Systems, Image Generation and Editing, LLM-Based Multi-Agent Systems **Introduction:** The rapid advancement of AIGC promises to transform various aspects of human life, particularly in image creation and self-expression. ContextCam aims to enhance this process by integrating context awareness, which can inspire more personalized and engaging images. The system's workflow includes two phases: "framing" and "focusing." In the "framing" phase, ContextCam extracts relevant contextual data and proposes three themes for the user to select. In the "focusing" phase, users collaborate with AI to create and refine images through natural language commands and straightforward selections. **Related Works:** The paper reviews existing work in human-AI co-creation, context-aware systems, image generation and editing, and LLM-based multi-agent systems. It highlights the potential of integrating contextual information into human-AI collaboration to enhance creativity and personalization. **Formative Study:** A formative study with 23 participants aimed to gather insights on user motivations, needs, and expectations for a context-aware human-AI co-creation system. The study identified six design guidelines for the system, emphasizing user-friendly interfaces, high-quality image generation, and personalized recommendations. **System Design and Implementation:** ContextCam is designed as a multi-agent system, integrating LLMs and vision models to create a personalized and engaging co-creation experience. The system includes agents for context selection, topic generation, tool management, artist assistance, and personalization. The implementation details and user study results are discussed, showing high user satisfaction and effective use of contextual data. **User Study:** A real-world user study with 16 participants evaluated ContextCam's performance and user behavior. The study found high user satisfaction, with 92.9% of conversations using system-generated topics. Users reported low interaction burdens and enjoyed the system's ability to inspire creativity and personalization. Contextual information significantly influenced image theme preferences and user experiences. **Conclusion:** ContextCam demonstrates the potential of integrating context awareness with human-AI co-creation, enhancing image creation processes and