This paper examines how contrasting AI personas shape multi-human creative teamwork. Drawing on Computers are Social Actors and leadership research, we use two theory-grounded AI collaborator personas, a control-oriented persona and an autonomy-supportive persona, as contrasting probes to explore how contrasting AI social behavior would be interpreted in multi-human creative teamwork. We conducted a counterbalanced within-subject study with 16 human design dyads completing open-ended ideation tasks on a custom digital whiteboard, analyzing the results using a mixed-methods approach. We observed two emergent team orientations: persona-sensitive teams (7/16) treated the control-oriented persona as intrusive or adversarial, while persona-neutral teams (9/16) engaged both personas in a tool-like manner. Our preliminary findings indicate that teams do not respond to AI personas uniformly, motivating future works on understanding how existing collaboration norms shape those responses.

Storyboarding is an established method for designing user experiences. Generative AI can support this process by helping designers quickly create visual narratives. However, existing tools mainly focus on improving the accuracy of text-to-image generation. There is a lack of understanding on how to effectively support the entire creative process of storyboarding and how to develop AI-powered tools to be integrated into designers' diverse workflows. In this work, we designed and developed StoryDiffusion, a system that integrates text-to-text and text-to-image models, to support the generation of narratives and images in a single pipeline. In a user study, we observed 12 UX design students using the system for both concept ideation and illustration tasks. Our findings identified AI-directed vs. user-directed creative strategies in both tasks and revealed the importance of supporting the interchange between narrative iteration and image generation. We also found effects of the design tasks on their strategies and preferences, providing insights for future development. © 2025 Copyright held by the owner/author(s).

As interest grows in learning math concepts in fields like data science and machine learning, it is becoming more important to help broad audiences engage with math notation. In this paper, we explore how authoring tools can help authors better style and label formulas to support their readability. We introduce a markup language for augmenting formulas called FFL, or “Formula Formatting Language,” which aims to lower the threshold to stylize and diagram formulas. The language is designed to be concise, writable, readable, and integrable into web-based document authoring environments. It was developed with an accompanying runtime that supports live application of augmentations to formulas. Our lab study shows that FFL improves the speed and ease of editing augmentation markup, and the readability of augmentation markup compared to baseline LaTeX tools. These results clarify the role tooling can play in supporting the explanation of math notation.

Ancestry estimation from human skeletal remains is a significant component in forensic anthropology studies. Although some tools have been developed, their performances are not very good in practice. In this paper, we evaluated the utility of the deep learning method in cranial ancestry estimation based on Howells craniometric data. Specifically, 2,524 cranial individuals from the Howells main datasets and 468 from the Howells test datasets were analyzed in the paper. The individuals of 82 craniometric measurements in the Howells datasets were clustered into six ancestry groups: African, Austro-Melanesian, Polynesian-Micronesia, East Asian, Native American, and European. After the data engineering process, the Howells datasets with 30 craniometric measurements were fed into a feedforward neural network (FNN) model. The FNN model was trained on 80% of the Howells main datasets, validated on 10% of the Howells main datasets, and tested on another 10% of the Howells main datasets. The model's prediction accuracy for all six ancestry groups reached 80.6%. Compared with popular ancestry estimation programs AncesTrees and Fordisc 3.1 using the Howells test dataset, the performance of the FNN deep learning method is on par overall and better for some ancestry groups.

The emergence of low-power 32-bit Systems-on-Chip (SoCs), which integrate a 32-bit MCU, radio, and flash, presents an opportunity to re-examine design points and trade-offs at all levels of the system architecture of networked sensors. To this end, we develop a post-SoC/32-bit design point called Hamilton, showing that using integrated components enables a ~$7 core and shifts hardware modularity to design time. We study the interaction between hardware and embedded operating systems, identifying that (1) post-SoC motes provide lower idle current (5.9 μA) than traditional 16-bit motes, (2) 32-bit MCUs are a major energy consumer (e.g., tick increases idle current >50 times), comparable to radios, and (3) thread-based concurrency is viable, requiring only 8.3 μs of context switch time. We design a system architecture, based on a tickless multithreading operating system, with cooperative/adaptive clocking, advanced sensor abstraction, and preemptive packet processing. Its efficient MCU control improves concurrency with ~30% less energy consumption. Together, these developments set the system architecture for networked sensors in a new direction.