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Émilie Fabre

Publications and source records attributed to Émilie Fabre.

3 recordsLinked to original sources

Juicy Text: Onomatopoeia and Semantic Text Effects for Juicy Player Experiences

Juiciness is visual pizzazz used to improve player experience and engagement in games. Most research has focused on juicy particle effects. However, text effects are also commonly used in games, albeit not always juiced up. One type is onomatopoeia, a well-defined element of human language that has been translated to visual media, such as comic books and games. Another is semantic text, often used to provide performance feedback in games. In this work, we explored the relationship between juiciness and text effects, aiming to replicate juicy user experiences with text-based juice and combining particle and text juice. We show in a multi-phase within-subjects experiment that users rate juicy text effects similarly to particles effects, with comparable performance, and more reliable feedback. We also hint at potential improvement in user experience when both are combined, and how text stimuli may be perceived differently than other visual ones. We contribute empirical findings on the juicy-text connection in the context of visual effects for interactive media.

cs.HC↗

Facilitating Longitudinal Interaction Studies of AI Systems

UIST researchers develop tools to address user challenges. However, user interactions with AI evolve over time through learning, adaptation, and repurposing, making one time evaluations insufficient. Capturing these dynamics requires longer-term studies, but challenges in deployment, evaluation design, and data collection have made such longitudinal research difficult to implement. Our workshop aims to tackle these challenges and prepare researchers with practical strategies for longitudinal studies. The workshop includes a keynote, panel discussions, and interactive breakout groups for discussion and hands-on protocol design and tool prototyping sessions. We seek to foster a community around longitudinal system research and promote it as a more embraced method for designing, building, and evaluating UIST tools.

cs.HC↗

More-than-Human Storytelling: Designing Longitudinal Narrative Engagements with Generative AI

Longitudinal engagement with generative AI (GenAI) storytelling agents is a timely but less charted domain. We explored multi-generational experiences with "Dreamsmithy," a daily dream-crafting app, where participants (N = 28) co-created stories with AI narrator "Makoto" every day. Reflections and interactions were captured through a two-week diary study. Reflexive thematic analysis revealed themes likes "oscillating ambivalence" and "socio-chronological bonding," highlighting the complex dynamics that emerged between individuals and the AI narrator over time. Findings suggest that while people appreciated the personal notes, opportunities for reflection, and AI creativity, limitations in narrative coherence and control occasionally caused frustration. The results underscore the potential of GenAI for longitudinal storytelling, but also raise critical questions about user agency and ethics. We contribute initial empirical insights and design considerations for developing adaptive, more-than-human storytelling systems.

cs.HC↗