Creativity tools are a cornerstone of HCI, with systems for video, music, writing, and design deeply embedded in modern creative practice. Yet one key element of these systems remains undertheorized: the role of activity traces. Activity traces are the records of creator data, including artifact iterations, annotations, or reference materials, produced over the course of a creative process. To examine how activity traces are leveraged, we reviewed 133 creativity systems from major HCI venues. We structure our findings through a Living Framework for Trace Awareness, which captures both the characteristics of trace data and how systems engage with their temporal features. This framework offers the first systematic account of activity trace usage in creativity tools. We highlight overlooked assumptions about creator data in feature design and position activity traces as a core design material for shaping the next generation of creativity support systems.

Background music plays a crucial role in social media videos, yet finding the right music remains a challenge for video creators.These creators, often not music experts, struggle to describe their musical goals and compare options.AI text-to-music generation presents an opportunity to address these challenges by allowing users to generate music through text prompts; however, these models often require musical expertise and are difficult to control.In this paper, we explore how to incorporate music generation into video editing workflows.A formative study with video creators revealed challenges in articulating and iterating on musical preferences, as creators described music as "vibes" rather than with explicit musical vocabulary.Guided by these insights, we developed a creative assistant for music generation using editable vibe-based recommendations and structured refinement of music output.A user study showed that the assistant supports exploration, while direct prompting is more effective for precise goals.Our findings offer design recommendations for AI music tools for video creators.

Human–Agent Teaming (HAT) research spans machine learning, HCI, robotics, and cognitive science, but suffers from fragmented testbeds that limit generalization and comparability. We introduce Vertical Farm, a cooperative farming simulator explicitly designed to tackle three key challenges in HAT testbed design: expressiveness (supporting diverse research agendas), unification (enabling standardized benchmarks), and rich team dynamics (capturing the complexity of real-world teaming). Vertical Farm embodies three design principles (composability, human-agent complementarity, and flexible team & role structure) that allow it to support a wide range of experimental configurations, from role negotiation to mixed-initiative control. Together, these contributions provide a blueprint for more unified, expressive, and realistic HAT research.

This paper explores new ways to support blind and low vision (BLV) game stream participants. Prior work on game-aware streaming systems has focused on the potential for viewer interaction and personalization for sighted viewers, but how such systems impact interaction and personalization for BLV viewers remains largely unexplored. Most streaming experiences have significant visual information but no non-visual or sensemaking alternatives, which can exclude BLV viewers from understanding and interacting with the streaming experience. Building on the pre-existing system MARS, we developed a design probe that makes game data available to stream viewers in personalizable visual and non-visual formats. We use this probe to investigate the needs of BLV game stream viewers through qualitative interviews and live prototype testing sessions on Twitch. In addition to the technical contributions of our probe, our work addresses how game-aware streaming technologies can align with the needs and motivations of BLV viewers, and paves the way for novel designs in future iterations of game-aware streaming technologies.

Research on live streaming systems that incorporate real-time data, such as game or viewer data, have been a topic of HCI research for some time. Despite the potential of data-driven game streaming interfaces, translating this research into practice faces two key challenges. First, the design space afforded by data-driven game streaming systems is not yet well understood, making it difficult to identify how designs might meet users' existing and potential needs. Second, adoption of these systems relies on engagement with the entire streaming ecosystem, which includes developers, streamers, moderators, and viewers, rather than with just one group. Through a two-phase design study, we investigate the expectations, desires, and experiences of streaming stakeholders, shedding light on how data-driven game streaming systems can meet their needs. Building upon these insights and drawing upon previous research, we propose a design framework aimed at analyzing and generating data-driven game streaming designs, thereby moving toward formalizing the design and development of such systems.

We explore the future of synchronous, multiplayer mobile AR gaming through our game V-Light, which extends current mobile AR game capacities using edge computing. Mobile AR games are currently limited by on-board processing power, while offloading operations to the cloud introduces high latency costs. This is a critical issue for games needing real-time response to player input. V-Light demonstrates how mobile AR games can leverage the power of edge computing, bringing computational resources closer to the user, keeping latency low and bandwidth high. We share our development toolkit, analyze the design and development of V-Light through the lens of an existing model for shared-world mobile AR, and demonstrate that edge computing can provide a “time machine” that lets game designers prototype mobile AR games for devices that do not yet exist.

We present MARS (Metadata Augmented Real-time Streaming), a system that enables game-aware streaming interfaces for Twitch. Current streaming interfaces provide a video stream of gameplay and a chat channel for conversation, but do not allow viewers to interact with game content independently from the steamer or other viewers. With MARS, a Unity game’s metadata is rendered in real-time onto a Twitch viewer’s interface. The metadata can then power viewer-side interfaces that are aware of the streamer’s game activity and provide new capacities for viewers. Use cases include providing contextual information (e.g. clicking on a unit to learn more), improving accessibility (e.g. slowing down text presentation speed), and supporting novel stream-based game designs (e.g. asymmetric designs where the viewers know more than the streamer). We share the details of MARS’ architecture and capabilities in this paper, and showcase a working prototype for each of our three proposed use cases.

We present an autobiographical design journey exploring the experience of returning to long-term single player games. Continuing progress from a previously saved game, particularly when substantial time has passed, is an understudied area in games research. To begin our exploration in this domain, we investigated what the return experience is like first-hand. By returning to four long-term single player games played extensively in the past, we revealed a phenomenon we call The Pivot Point, a ‘eureka’ moment in return gameplay. The pivot point anchors our design explorations, where we created prototypes to leverage the pivot point in reconnecting with the experience. These return experiences and subsequent prototyping iterations inform our understanding of how to design better returns to gameplay, which can benefit both producers and consumers of long-term single player games.

We present Mutation: performing arts based approach that can help decrease the cognitive load associated with cyborg transitioning. Cyborgs are human-machine hybrids with organic and mechatronic body parts that can be implanted or worn. The transition into and out of experiencing additional body parts is not fully understood. Our goal is to draw from performing arts techniques in order to help decrease the cognitive load associated with becoming and unbecoming a cyborg. Actors constantly shift between states, whether from one character to another, or from pre- to post- performance. We contribute a straightforward adaptation of classic performing art practices to cyborg transitioning, and a study where actors used these protocols in order to enter a cyborg state, perform as a cyborg, and then exit the cyborg state. Our work on Mutation suggests that classic performing art practices can be useful in cyborg transitioning, as well as in other technology augmented experiences.

Requirements Engineering (RE) is closely tied to other development activities and is at the heart and foundation of every software development process. This makes RE the most data and communication intensive activity compared to other development tasks. The highly demanding communication makes task switching and interruptions inevitable in RE activities. While task switching often allows us to perform tasks effectively, it imposes a cognitive load and can be detrimental to the primary task, particularly in complex tasks as the ones typical for RE activities. Visualization mechanisms enhanced with analytical methods and interaction techniques help software developers obtain a better cognitive understanding of the complexity of RE decisions, leading to timelier and higher quality decisions. In this paper, we propose to apply interactive visual analytics techniques for managing requirements decisions from various perspectives, including stakeholders communication, RE task switching, and interruptions. We propose a new layered visualization framework that supports the analytical reasoning process of task switching. This framework consists of both data analysis and visualization layers. The visual layers offer interactive knowledge visualization components for managing task interruption decisions at different stages of an interruption (i.e. before, during, and after). The analytical layers provide narrative knowledge about the consequences of task switching decisions and help requirements engineers to recall their reasoning process and decisions upon resuming a task. Moreover, we surveyed 53 software developers to test our visual prototype and to explore more required features for the visual and analytical layers of our framework.