cs.HC · 2026-06-24 · No. 33
Human-Computer Interaction, 2026-06-24.
4 new papers in cs.HC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
4 entries-
01
It's Complicated: On the Design and Evaluation of AI-Powered AAC Interfaces
Blade Frisch, Will Wade, Dylan Gaines, Michelle Kinsella, Betts Peters, Tamara Broderick, Keith Vertanen
cs.HC · cs.AI
Artificial intelligence (AI) can enhance what people who use augmentative and alternative communication (AAC) are able to do with their systems. However, evaluating AI-powered AAC interfaces can be difficult. People are intersectional beings and current evaluation metrics can struggle to capture the multifaceted and nuanced desires people may have for their AAC. We explore the complicated nature of six AAC problem spaces, explore how AI might...
-
02
Visualizing "We the People": Bridging the Perception Gap through Pluralistic Data Storytelling
Lisa Schirch, Beth Goldberg
cs.HC · cs.AI · cs.CY · cs.ET · cs.GR
Traditional visual data storytelling relies on binary graphics that depict two simplified groups in conflict. This can increase political polarization by oversimplifying intra-group disagreements and erasing ambiguity and shared ideas or values. This can inadvertently foster "us versus them" thinking. Intentional, pluralistic design choices for AI-enabled digital platforms can produce visualizations that emphasize nuance, opinion...
-
03
Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders
Xavier Vasques, Paul Barbaste, Olivier Oullier
cs.HC · cs.AI · cs.RO · q-bio.NC
Electroencephalography (EEG) is the dominant non-invasive modality for brain-computer interfaces (BCIs), yet reliable decoding of motor imagery is hampered by inter- and intra-individual variability. A recurring claim is that one decoding pipeline, most often a spatial or Riemannian method, is broadly preferable. We test the weakest version of that claim under the most favourable conditions. Using the Mother of All BCI Benchmarks (MOABB)...
-
04
The impact of generative artificial intelligence on academic development of Chinese students in humanities and social sciences
Lei Fan, Fangxue Liu
cs.HC · cs.AI
Generative artificial intelligence(GenAI) is reshaping learning in higher education, with particularly pronounced implications for the humanities and social sciences(HSS), where learning outcomes are commonly expressed through written and interpretive forms that align closely with GenAI's capabilities. Yet, systematic evidence on the educational impacts of GenAI on HSS students remains limited. Addressing this gap, this study draws on a...
This edition is part of The Daily Abstract — cs.HC archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.
#D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.