2084 papers
The "Cs — Hc" category on Gist.Science explores the fascinating intersection where computer science meets human cognition, behavior, and society. Here, you will find research examining how algorithms shape our interactions, how artificial intelligence influences decision-making, and the complex ways technology integrates into daily life. These studies bridge the gap between technical systems and the people who use them, offering insights that are vital for developers, policymakers, and anyone curious about our digital future.
Every new preprint in this field is sourced directly from arXiv and processed by our team to ensure clarity for all readers. We provide both plain-language overviews for general understanding and detailed technical summaries for experts, making cutting-edge research accessible without losing scientific rigor. Below are the latest papers from this category, offering a fresh look at how code and culture intertwine.
This paper introduces ANSR-DT, a neuro-symbolic framework that integrates CNN-LSTM pattern recognition, Prolog-based reasoning, and PPO-driven adaptation to create a trustworthy, explainable, and adaptive digital twin capable of transparent anomaly detection and decision support in industrial systems.
This preliminary study proposes a research design and presents initial findings from a sample of 54 participants demonstrating that HEXACO personality traits, alongside demographic factors like gender proportion and age distribution, significantly influence teamwork quality in software development teams.
This study replicates and extends Zhang et al. (2021) on the cold-start problem in knowledge tracing models using the FoundationalASSIST dataset to demonstrate that model performance varies across student practice trajectories and problem types, while also showcasing the utility of the privacy-preserving SafeInsights infrastructure for facilitating reproducible educational research.
This paper presents the Frustrometer, a real-time system that fuses physiological and interaction data to predict user frustration in visualization tasks, finding that mouse movements and gaze patterns are more effective predictors than physiological signals like heart rate or skin response.
This paper introduces a novel benchmark and online evaluation framework for predicting next actions in spreadsheets, addressing the lack of edit history data by curating 52 reconstructed sequences and evaluating various models on complex spatial, temporal, and composite action prediction tasks.
This paper rethinks the conventional unstructured, depth-first, high-fidelity approach of GenUI tools by proposing and evaluating a contrastive design featuring structured input, breadth-first exploration, and low-fidelity generation, revealing distinct trade-offs in entry barriers, idea diversity, and fidelity expectations among UX professionals.
This paper introduces SpheriCity, a provenance-first conversational AI prototype designed to enhance trustworthy knowledge sensemaking and cross-document synthesis in sustainability decision-making, validated by expert feedback highlighting the critical role of transparent sourcing and workflow alignment in building user trust.
This longitudinal study of three HCI course cohorts reveals that while student disclosure of AI tool usage has increased significantly, the persistent gap between general disclosure and specific attribution of AI contributions exposes the inadequacy of current accountability frameworks for capturing continuous, ambient co-creation in the "vibe-coding" era.
This paper presents Fabula, an interactive AI-powered app for fiction writers that utilizes hierarchical narrative planning and participatory design with 42 experts to evaluate how generative models, transparent narrative structures, and iterative planning tools impact story quality and writer creativity across diverse storytelling traditions.
This paper presents a computational audit of ClinicalBERT revealing that representational bias in the model operates primarily through internal amplification of demographic associations rather than simple inheritance from training data, as evidenced by systematic deviations between model predictions and empirical corpus frequencies across race and gender categories.