CORTEXA
← Browse
arxivcs.HC2026-06-25

Floor Raiser or Ceiling Limiter? Differential Storytelling Outcomes with a Child-Centric GenAI System Across Individual Differences

Min Fan, Wanqing Ma, Xinyue Cui, Xiaolu Dai, Shengyu Huang

Generative AI (GenAI) holds promise for democratizing creative literacy, yet whether it benefits all children equally remains unclear. Using a child-centric GenAI storytelling system for children aged 7-12, we conducted a mixed-methods within-subjects experiment (N = 40, Grades 2-6) comparing GenAI-assisted and traditional storyboard conditions. Three findings emerged. First, the GenAI-assisted condition was associated with a floor-raising convergence pattern, with the quality gap narrowing by 83.5%, driven by lower-end support and upper-end constraint mechanisms. This convergence was dimension-selective, improving creativity and richness while leaving coherence and narrative structure tied to baseline performance. Second, younger children more often selected semantically distant keywords while older children preferred semantically closer ones, although engagement orientation varied across individuals regardless of age. Third, image regeneration was positively associated with structural quality dimensions, though this association was attenuated after baseline control. We propose mechanism-contingent scaffolding as a design principle for adaptive GenAI storytelling systems serving diverse children.

View free PDFSource page

Related papers

arxivcs.HC2026-07-24

The machine can say it but cannot hear it. Designed affective patterns and the expressive-sensing asymmetry in human-machine communication

Jan K. Argasinski

Affect-adaptive systems increasingly act as communicators that sense a user's emotion and respond with events meant to change it, closing an affective loop. This vision assumes both that a machine's affective messages are received and that the bodily channel it monitors carries a…

View free PDFSource page
arxivcs.HCcs.AI2026-07-24

Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education

Jennie Ren, Jordan H. McDowell, Kyrie Zhixuan Zhou

Generative AI is reshaping programming education, yet educators often infer students' AI-supported learning from classroom observations alone. This experience report presents a trio-ethnography involving two computing educators with different teaching philosophies and one undergr…

View free PDFSource page
arxivq-bio.QMcs.HC2026-07-24

Loom: Multi-Region Analysis of Spatial Transcriptomics with Local Neighborhoods and Global Trajectories

Siyuan Zhao, Nafiul Nipu, Hossein Fathollahian, Olga Karginova, Hao Chen, Ameen Salahudeen, et al.

We present Loom, a spatial transcriptomics (ST) visual computing system to support the analysis of pseudo-temporal trajectories, comparative investigation across samples and regions of interest, and the examination of spatially structured processes within local microenvironments.…

View free PDFSource page
arxivcs.HCcs.CL2026-07-24

Towards Reducing Foreign Language Anxiety Using Level-Appropriate Embodied Conversational Agents

Krishan Rajaratnam, Wenbin Gan, Yuan Sun

Foreign language anxiety (FLA) can be a major barrier to second language acquisition (SLA), especially in conversational contexts. With the proliferation of large language models (LLMs) throughout all areas of life, recent work suggests that interacting with LLM agents can be ins…

View free PDFSource page
arxivcs.HCcs.AIcs.LGcs.MM2026-07-24

Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability

Ahmed M. Abuzuraiq, Philippe Pasquier

Explainable AI (XAI) in creative practice can be less about technocentric explanation and more about enabling artists to inspect modify and debug models as part of making Yet largescale texttoimage diffusion systems are typically presented as opaque endtoend tools limiting this k…

View free PDFSource page