cs.HC · 2026-07-27 · No. 66
Human-Computer Interaction, 2026-07-27.
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
Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education
Jennie Ren, Jordan H. McDowell, Kyrie Zhixuan Zhou
cs.HC · cs.AI
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 undergraduate computer science student to examine how these interpretations evolve through dialogue. Across three conversations, the educators reflected on students' AI use,...
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02
Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability
Ahmed M. Abuzuraiq, Philippe Pasquier
cs.HC · cs.AI · cs.LG · cs.MM
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 kind of material engagement We argue that even large models can function as creative materials when their internal structure is made visible and manipulable To support...
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03
Universal BCI Personalization: One API for Frozen EEG Trunks and Foundation Models
Sergey Musienko
cs.HC · cs.LG · q-bio.NC
Frozen EEG encoders proliferate; per-model fine-tune defaults do not scale. We present Nimbus Personalizer: one contract encode to Bayesian head to BrainState (optional affine mid-tier) that sits on heterogeneous frozen trunks without a new personalization stack per architecture. Thesis (systems): the contribution is the trunk-agnostic API - not LDA-on-embeddings as an ML novelty - so OEMs integrate once and swap trunks. Evidence: the same...
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04
AI-Integrated Scientific Inquiry: A Practice-Centered Vision for Science Education
Arne Bewersdorff, Matias Rojas, Xiaoming Zhai
cs.HC · cs.AI
Artificial intelligence (AI) has become part of scientific inquiry. Scientists use AI to observe and measure phenomena, to identify patterns in data, and to build models. As AI moves into scientific inquiry, it gains relevance for science education: students should learn how AI is changing scientific practices, ideally by engaging in AI-integrated scientific inquiry themselves. How to design such instruction, grounded in authentic scientific...
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