Evaluating personalized, sequential treatment strategies for Alzheimer's disease (AD) using clinical trials is often impractical due to long disease horizons and substantial inter-patient heterogeneity. To address these constraints, we present the Alzheimer's Learning Platform for Adaptive Care Agents (ALPACA), an open-source, Gym-compatible reinforcement learning (RL) environment for systematically exploring personalized treatment strategies using existing therapies. ALPACA is powered by the Continuous Action-conditioned State Transitions (CAST) model trained on longitudinal trajectories from the Alzheimer's Disease Neuroimaging Initiative (ADNI), enabling medication-conditioned simulation of disease progression under alternative treatment decisions. We show that CAST autoregressively generates realistic medication-conditioned trajectories and that RL policies trained in ALPACA outperform no-treatment and behavior-cloned clinician baselines on memory-related outcomes. Interpretability analyses further indicated that the learned policies relied on clinically meaningful patient features when selecting actions. Overall, ALPACA provides a reusable in silico testbed for studying individualized sequential treatment decision-making for AD.

We present Feel My Focus, a gaze-triggered mixed reality (MR) demonstration that enables rapid creation and sharing of lightweight 3D digital twins during remote collaboration. Unlike systems that use gaze primarily as a referential pointer, Feel My Focus treats sustained visual attention as a generative action: a fixation on a physical object initiates single-image capture, attention-guided segmentation, and one-shot 3D reconstruction, allowing a remote partner to inspect and discuss the object in situ. The system integrates gaze-tracking glasses, modular segmentation and reconstruction services, and an MR client for runtime streaming and interaction. This design prioritizes responsiveness and experiential continuity over photometric fidelity. We report observations from a public demonstration, highlighting design trade-offs, failure modes, and user control considerations for deploying gaze-triggered generative systems in real-world collaborative settings.

Artificial Intelligence (AI)-powered conversational agents are entering young children's learning and play, yet little is known about how children construe these agents during interaction. We examined anthropomorphism toward an AI-powered conversational agent and asked how children's attributions related to behavioral and neural engagement. Children aged 5-6 (N = 23) completed three sessions: storytelling with an AI alone, with a parent alone, and with the AI and parent together, while prefrontal brain activity was recorded. Children anthropomorphized parents more than the AI overall but still attributed strong perceptive and epistemic abilities to the AI. Higher perceptive attributions were associated with greater right dorsomedial prefrontal activation during AI-only interaction, lower activation when a parent was present, and more scared mood. Findings highlight how children's AI construals and parent co-presence shape child-AI interaction and point to design considerations for age-appropriate AI-powered conversational agent systems for young children.

As the construction industry accelerates its digital transformation, it is essential to thoroughly investigate the value proposition of emerging technologies. This study investigated the complex interplay between the perceived origin of a design (AI vs. Human), the information format (AR vs. Paper), user trust, and work performance. The objective was to understand how user trust is affected by the perceived origin of a design and whether it influences user work performance in an AR-assisted environment. To explore this, we conducted a controlled experiment with one hundred practicing industry craft workers performing a full-scale Mechanical, Electrical, and Plumbing (MEP) assembly task. With the use of deception, the perceived origin of the design was manipulated as either AI or human-generated. Participants used traditional paper drawings or one of two AR models to complete the task. We recorded self-reported trust, both before and after the task, as well as work performance factors such as task time, rework, and errors. The findings revealed that user trust is not uniform: participants’ perceptions of design accuracy remained stable regardless of origin, even when they noticed errors, whereas trust in the design’s safety decreased significantly when it was attributed to AI. Regarding performance, the AR interface notably enhanced accuracy by reducing rework, regardless of the design’s perceived origin. Task speed, however, was mainly influenced by the user’s innate spatial cognitive ability. These outcomes suggest that successful integration of AI into construction will not depend just on the capabilities of the algorithms but on the entire sociotechnical system, with the user interface playing a crucial mediating role. Therefore, to unlock the full potential of these technologies, experts must address underlying human factors such as trust and user perception.

This paper reports findings from four AI literacy workshops conducted with secondary school teachers in Ibadan and Abuja, Nigeria. The workshops introduced foundational AI concepts through interactive lectures, hands-on activities, and group discussions designed for settings with limited digital infrastructure. Teachers expressed interest in integrating AI concepts into their classrooms but identified persistent constraints, including unreliable electricity and internet access, limited device availability, and the absence of locally relevant AI materials. We found that low-tech, analog activities, such as AI-by-hand exercises, supported conceptual understanding without requiring computational tools. Drawing on these findings, we propose a framework for designing AI literacy programs in resource-constrained educational contexts, with attention to infrastructural limitations and sustained teacher support through communities of practice.

As generative AI (genAI) rapidly enters classrooms, accompanied by district-level policy rollouts and industry-led teacher trainings, it is important to rethink the canonical “adopt and train” playbook. Decades of educational technology research show that tools promising personalization and access often deepen inequities due to uneven resources, training, and institutional support. Against this backdrop, we conducted semi-structured interviews with 22 teachers from a large U.S. school district that was an early adopter of genAI. Our findings reveal the motivations driving adoption, the factors underlying resistance, and the boundaries teachers negotiate to align genAI use with their values. We further contribute by unpacking the sociotechnical dynamics—including district policies, professional norms, and relational commitments—that shape how teachers navigate the promises and risks of these tools.

With artificial intelligence (AI) becoming more present in ed- ucation globally, it is essential to consider how cultural con- texts shape teachers’ perspectives, an understanding that sup- ports more inclusive and sustainable learning systems. This study draws on the African philosophy of Ubuntu to frame our cross-cultural investigation of how children conceptu- alize AI through the lens of their teachers. We conducted semi-structured interviews with twelve middle school teach- ers in Nigeria and the United States, asking them to interpret AI-themed essays written by students. These teacher reflec- tions revealed differing educational priorities, cultural val- ues, and infrastructural realities: U.S. educators’ interpreta- tions centered on personal development and future careers, while Nigerian teachers highlighted students’ focus on fam- ily, community well-being, and practical societal challenges. Nigerian participants also pointed to the need for improved infrastructure (e.g., electricity, internet), broader AI literacy, and education policies that reflect local needs. Our findings il- lustrate how culturally grounded worldviews, such as Ubuntu, shape interpretations of AI and its role in society, and sug- gest that AI education is never culturally neutral. We argue that AI literacy initiatives must be designed not only to teach technical skills but also to support educational sustainability, defined here as inclusive, resilient, and culturally responsive learning systems capable of evolving within diverse contexts. We offer actionable recommendations for the HCI commu- nity to co-design AI education tools that foreground collec- tive well-being, foster global digital citizenship, and reduce epistemic exclusion in the development of future technolo- gies.

We present The Architect, a system that turns Microsoft Excel into an interactive view of deep learning mathematics. A user describes a neural network in a compact table. The system then generates a workbook that shows the full forward pass and, when requested, the backward pass and parameter updates. Computed values appear as live spreadsheet formulas, while user-controlled values such as inputs, weights, labels, and hyperparameters remain editable. Excel reactively updates the dependent computations through its recalculation engine. Most deep learning tools hide the numerical details behind library calls. Many visualization tools show architecture diagrams or training summaries, but they do not expose the full arithmetic of the model. The Architect focuses on that missing middle layer. It makes matrices, activations, losses, gradients, and updates visible as inspectable spreadsheet regions, with editable controls for values users naturally manipulate. The system also produces aligned PyTorch snippets, which helps users connect formulas to implementation. This report describes the motivation, design, implementation, and use cases of The Architect. We show how the system supports introductory arithmetic tracing, learning-rate exploration, diagnosis of dying ReLU, and inspection of vanishing gradients. The main idea is simple: spreadsheets already support formulas, direct editing, reactive recomputation, and tabular layout. These properties make them a useful medium for understanding how small educational and diagnostic neural networks compute.

Individuals who perceive the caregiving they received from their parents as more caring tend to bond better with their infants and show more sensitive parenting behaviors. Early caregiving experiences are also related to differences in the functions of hormonal systems, including the oxytocinergic system. The current study examined how perceptions of childhood maternal care relate to parenting behaviors, oxytocin levels, and neural responses to infant stimuli. Perceived childhood maternal care was measured using the Parental Bonding Instrument (PBI) for 54 first-time birthing parents. Salivary oxytocin and observations of parenting behaviors were assessed during parent-infant play at 3.5 months postpartum. Neural activation while listening to infant cry was measured with fMRI. More positive perceptions of childhood maternal care and higher oxytocin were interactively related to greater anterior cingulate activation to own infant's cry. Higher oxytocin levels were associated with reduced left cuneus activation in response to own infant's cry when compared with control cry and matched noise. Findings suggested that positive memories of childhood caregiving may have protective functions for birthing parents with high oxytocin levels during the early postpartum period, a time when parents need to manage increased stress and form an exclusive bond with their baby.

Artificial Intelligence (AI) chatbots powered by a large language model (LLM) are entering young children's learning and play, yet little is known about how young children construe these agents or how such construals relate to engagement. We examined anthropomorphism of a social AI chatbot during collaborative storytelling and asked how children's attributions related to their behavior and prefrontal activation. Children at ages 5-6 (N = 23) completed three storytelling sessions: interacting with (1) an AI chatbot only, (2) a parent only, and (3) the AI and a parent together. After the sessions, children completed an interview assessing anthropomorphism toward both the AI chatbot and the parent. Behavioral engagement was indexed by the conversational turn count (CTC) ratio, and concurrent fNIRS measured oxygenated hemoglobin in bilateral vmPFC and dmPFC regions. Children reported higher anthropomorphism for parents than for the AI chatbot overall, although AI ratings were relatively high for perceptive abilities and epistemic states. Anthropomorphism was not associated with CTC. In the right dmPFC, higher perceptive scores were associated with greater activation during the AI-only condition and with lower activation during the AI+Parent condition. Exploratory analyses indicated that higher dmPFC activation during the AI-only condition correlated with higher end-of-session "scared" mood ratings. Findings suggest that stronger perceptive anthropomorphism can be associated with greater brain activation related to interpreting the AI's mental states, whereas parent co-presence may help some children interpret and regulate novel AI interactions. These results may have design implications for encouraging parent-AI co-use in early childhood.

Building information modeling (BIM) has revolutionized the construction industry; however, field personnel still rely on traditional methods for design interpretation. Augmented reality (AR) head-mounted display devices (HMDDs) offer a promising alternative for delivering three-dimensional design information, yet their effectiveness must be validated across diverse populations with a significant sample size. This study examines the impact of AR HMDDs on mechanical, electrical, and plumbing (MEP) assembly tasks, evaluating key performance metrics: task completion time, rework rate, and error rate. Both novice and experienced participants completed assembly tasks in industrial and laboratory settings, marking the first comparative analysis of AR models with different levels of detail (LOD 300 and LOD 400) against traditional isometric paper plans. Findings indicate that the AR LOD 400 model significantly improved all performance metrics across both populations, except for the error rate among industry professionals. The AR LOD 300 model notably reduced rework rates, while traditional paper plans were the least effective. Age did not significantly impact performance, whereas higher spatial cognition enhanced novices' efficiency. Participants acknowledged AR HMDDs, particularly the LOD 400 model, as beneficial for design comprehension; however, some also reported distractions. These insights highlight the need for user-centered AR interface design and tailored training strategies to enhance usability and efficiency. As the first study to investigate the influence of AR model LODs on construction task performance with a robust sample size, the results provide valuable guidance for optimizing AR HMD technology, training protocols, and design information delivery in the construction industry.

Augmented Reality (AR) Head Mounted Displays Devices (HMDDs) have the potential to revolutionize information delivery during the construction phase. However, concerns remain about whether AR HMDDs impact workers' ability to detect changes in their surroundings, which could pose safety risks. In this controlled experiment, one hundred industry craft workers participated in an assembly task on a full-scale Mechanical, Electrical, and Plumbing (MEP) model using three information formats: traditional isometric paper drawings and two AR models at levels of detail (LOD) 300 and 400 that vary based on the density of information provided. A safety hazard scenario was introduced, and the response time to detect the change was recorded. Findings revealed a significant difference in response times, with non-AR HMDD users detecting changes more quickly than AR HMDD users. Further investigation examined the correlation between workers' age, spatial cognition, and response time to detect changes. This study is one of the first in the construction domain to introduce hazards (referred to as change) and examine AR HMDDs’ impact on individuals' ability to detect them.

Portrayals of intelligent technologies and smart devices are becoming increasingly common in narrative media, such as novels, video games, and movies, targeted at children and teenagers. This media can shape children's understanding of technology and potentially inform how interact in a society increasingly influenced by artificial intelligence (AI) and machine learning (ML). In this work we seek to understand the potential benefits and limitations of these depictions by examining narrative media that portray intelligent technologies through an educational lens. We crafted systematic summaries of 64 examples of such media and used them as the basis for an interdisciplinary workshop involving nine subject matter experts from varying fields of education and technology. The workshop yielded insights regarding the qualities of these depictions; in particular our findings allowed us to draw important implications about how narrative depictions of intelligent technologies could be applied in K-12 education in ML and AI.

Creative storytelling with parents plays an important role in child development including language skills, social competence, and emotional understanding. Recognizing the challenges parents face in finding time for storytelling due to work and home responsibilities, we explore the feasibility of ChatGPT for engaging children in creative storytelling. This study investigates the use of ChatGPT, a conversational agent powered by GPT-4, in creative storytelling with children aged 5-6, comparing its interaction styles with those of parents. The current study included eight child-parent dyads. We found that children were engaged in shorter and more frequent interactions with parents compared to ChatGPT. ChatGPT and parents asked different types of questions, and ChatGPT more frequently provided positive feedback compared to parents. More children selected the interactions with ChatGPT as their favorite interactions. The study provides preliminary evidence on ChatGPT's interaction styles and insights into its potential role in supporting families in creative storytelling activities.

Previous research indicates that maternal cortisol function and maternal brain response to infant are each in turn related to variations in parenting behavior. However, little is known about how maternal cortisol and maternal brain function are associated, thus studying these two mechanisms together may improve our understanding of how maternal cortisol assessed during interactions with own infant is associated with brain response to infant cry. First-time mothers (N = 59) of infants aged 3-4 months old were recruited to participate. Mothers' cortisol concentration was measured during a naturalistic interaction with their infant and their behavior was coded for two parenting behaviors-- maternal sensitivity and non-intrusiveness. In an fMRI session, mothers listened to their own infant and a control infant crying. Higher cortisol concentration was associated with more intrusive behavior. We found greater cortisol concentration was further associated with decreased activation in the brain to infant cry in the right precentral gyrus, the left culmen extending into the left inferior temporal gyrus and fusiform, two clusters in the superior temporal gyrus, and in the medial frontal gyrus. We also found that lower activation in these regions was associated with more intrusive maternal behavior. These data demonstrate the associations between maternal cortisol concentration and reduced brain activation to infant cry in both motor planning and auditory processing regions in predicting intrusive parenting behavior.

This article examines the ways secondary computer science and English Language Arts teachers in urban, suburban, and semi-rural schools adapted a project-based AI ethics curriculum to make it better fit their local contexts. AI ethics is an urgent topic with tangible consequences for youths' current and future lives, but one that is rarely taught in schools. Few teachers have formal training in this area as it is an emerging field even at the university level. Exploring AI ethics involves examining biases related to race, gender, and social class, a challenging task for all teachers, and an unfamiliar one for most computer science teachers. It also requires teaching technical content which falls outside the comfort zone of most humanities teachers. Although none of our partner teachers had previously taught an AI ethics project, this study demonstrates that their expertise and experience in other domains played an essential role in providing high quality instruction. Teachers designed and redesigned tasks and incorporated texts and apps to ensure the AI ethics project would adhere to district and department level requirements; they led equity-focused inquiry in a way that both protected vulnerable students and accounted for local cultures and politics; and they adjusted technical content and developed hands-on computer science experiences to better challenge and engage their students. We use Mishra and Kohler's TPACK framework to highlight the ways teachers leveraged their own expertise in some areas, while relying on materials and support from our research team in others, to create stronger learning experiences.

Today's cybersecurity and AI technologies are often fraught with ethical challenges. One promising direction is to teach cybersecurity and AI ethics to today's youth. However, we know little about how these subjects are taught before college. Drawing from interviews of US high school teachers (n=16) and students (n=11), we find that cybersecurity and AI ethics are often taught in non-technical classes such as social studies and language arts. We also identify relevant topics, of which epistemic norms, privacy, and digital citizenship appeared most often. While teachers leverage traditional and novel teaching strategies including discussions (treating current events as case studies), gamified activities, and content creation, many challenges remain. For example, teachers hesitate to discuss current events out of concern for appearing partisan and angering parents; cyber hygiene instruction appears very ineffective at educating youth and promoting safer online behavior; and generational differences make it difficult for teachers to connect with students. Based on the study results, we offer practical suggestions for educators, school administrators, and cybersecurity practitioners to improve youth education on cybersecurity and AI ethics.

The field of Artificial Intelligence (AI) is leading transformative impacts across different sectors. However, these advancements are often developed with a Western-centric focus, neglecting the cultural diversity in regions such as the Global South. In this paper, we synthesize twelve research papers focusing on cultural considerations in designing AI systems for the Global South. Our findings revealed a significant focus on domains like healthcare and intelligent assistants and challenges, including usability, AI transparency, and data availability, that can hinder the design and deployment of culturally sensitive AI systems. Moreover, the results demonstrated the need to integrate cultural values to increase the acceptance and usefulness of AI systems for regions in the Global South. This paper guides future research towards developing culturally and contextually relevant AI systems for Africa, highlighting the need for increased visibility and representation of African researchers in HCI and AI domains.

Reflection is a metacognitive skill that’s essential to creative discovery. As we design interactive technologies for reflection, how might we measure the impact of our designs? In this paper, we develop a coding scheme to explore reflective moments in the speech and language of young children during child-computer interaction. Using cross-disciplinary theories — from the learning sciences to cognitive neuroscience — we define and describe 13 reflective processes occurring within Baumer’s 3 conceptual dimensions of reflection. We then use this framework to measure the impact of a child-robot storytelling interaction with twelve children ages 4–5, and offer developmentally-appropriate transcript examples for each of the 13 reflective processes. This coding scheme provides a practical tool for exploring the impact of our designs on reflection, and can be used to guide design iteration.

Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements in hardware, software, and research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience research community. We spotlight fNIRS through the lens of different end-application users, including the unique perspective of a fNIRS manufacturer, and report the challenges of using this technology across several research disciplines and populations. Through the review of different research domains where fNIRS is utilized, we identify and address the presence of bias, specifically due to the restraints of current fNIRS technology, limited diversity among sample populations, and the societal prejudice that infiltrates today's research. Finally, we provide resources for minimizing bias in neuroscience research and an application agenda for the future use of fNIRS that is equitable, diverse, and inclusive.