Dunhuang murals, a significant world cultural heritage, present substantial comprehension barriers for general audiences due to their intricate compositions and culturally distant narratives. Existing digital systems limit users’ ability to establish coherent cognitive connections between original visual compositions and narrative progression, while reliance on manual content curation restricts both scalability and generalizability. We present Luminara, an AI-powered system that automatically analyzes Dunhuang murals and generates interactive narratives. Luminara integrates vision-language models (VLMs) and large language models (LLMs) to establish visual-textual correspondences, and employs a multi-agent framework (Storytelling, Knowledge, and Reflective agents) to generate interactive narratives. This design addresses key barriers identified through our formative study (N=12): visual-textual correspondence challenges, narrative structure comprehension difficulties, and cultural knowledge gaps. A user study (N=17) demonstrated the system’s effectiveness in helping users comprehend complex compositions and storylines, resulting in clear and immersive viewing experiences. This research contributes an automated, generalizable approach and practical design insights for interactive narrative systems in digital cultural heritage.

Advances in large language models (LLMs) are profoundly reshaping the field of human–robot interaction (HRI). While prior work has highlighted the technical potential of LLMs, few studies have systematically examined their human-centered impact (e.g., human-oriented understanding, user modeling, and levels of autonomy), making it difficult to consolidate emerging challenges in LLM-driven HRI systems. Therefore, we conducted a systematic literature search following the PRISMA guideline, identifying 86 articles that met our inclusion criteria. Our findings reveal that: (1) LLMs are transforming the fundamentals of HRI by reshaping how robots sense context, generate socially grounded interactions, and maintain continuous alignment with human needs in embodied settings; and (2) current research is largely exploratory, with different studies focusing on different facets of LLM-driven HRI, resulting in wide-ranging choices of experimental setups, study methods, and evaluation metrics. Finally, we identify key design considerations and challenges, offering a coherent overview and guidelines for future research at the intersection of LLMs and HRI.

Large Language Models are reshaping task automation, yet remain limited in complex, multi-step real-world tasks that require aligning with vague user intent and enabling dynamic user override. From a formative study with 12 participants, we found that end-users actively seek to shape task-oriented interfaces rather than relying on one-shot outputs. To address this, we introduce the human-agent co-generation paradigm, materialized in DuetUI. This LLM-empowered system unfolds alongside task progress through a bidirectional context loop—the agent scaffolds the interface by decomposing the task, while the user’s direct manipulations implicitly steer the agent’s next generation step. In a technical ablation study and a user study with 24 participants, DuetUI improved task efficiency and interface usability, supporting more seamless human-agent collaboration. Our contributions include the proposal of this novel paradigm, the design of a proof-of-concept DuetUI prototype embodying it, and empirical and technical insights from an initial evaluation of how this bidirectional loop may help align agents with human intent and inform future development.

A design method card deck helps designers understand and provoke thinking by presenting each method in a simple format and allow designers to switch between methods seamlessly by maintaining the same simple format across the deck. However, recent observations have shown designers hesitate to use a card deck due to the lack of support, while other tools have provided identified support with generative AI. Through a formative study, we identified the specific support designers need when applying the design method cards and intentions in integrating generative AI. Accordingly, we developed the intelligent design method card deck, I-Card, which integrates generative AI to provide applicable design methods, design knowledge and data support, and interactive and dynamic support. A user study demonstrates that I-Card improved the design efficiency and applicability by offering personalized guidance, enhanced decision-making with comprehensive data generation and provided more design inspiration via interactive support.