Named after the hundred-eyed watchman of Greek myth, Argus watches the education landscape: spotting new opportunities, pressure-testing the ventures we're building, and tracing every read back to the real-world signals behind it.
The evidence library: the raw signals the pipeline is watching across the education ecosystem. Every idea is built from these.
arXiv:2608.04912v1 Announce Type: new Abstract: Extended reality (XR) is increasingly used to help users understand complex virtual environments through multiple viewpoints across different immersion levels, positions, and scales. While numerous techniques address viewpoint transitions for self-guided exploration, many scenarios require externally-guided transitions where a system or presenter controls the user's viewpoint, leaving the user with limited spatial knowledge and control over the transition process, which can increase susceptibility to disorientation and discomfort. We present three transition techniques for externally-guided multi-scale XR viewpoint changes and evaluate them against a fade-to-black baseline in a within-subjects study (N=20). Participants transitioned between world-in-miniature, street-level, and indoor destination views. We combined spatial recall measures, standardized questionnaires, and semi-structured interviews to assess orientation, workload, comfort
arXiv:2608.04910v1 Announce Type: new Abstract: Tutorial videos are widely used for learning feature-rich software, yet following screencast tutorials often breaks down in practice. Through a survey and contextual inquiry, we found that learners frequently rewind or get stuck because critical input information, especially mouse actions and keyboard-modified operations, is often implicit or missing in tutorials without input metadata. To address this problem, we present AutoCue, a multimodal LLM-assisted, human-in-the-loop tutorial augmentation pipeline for externalizing implicit inputs as instructional visual cues. AutoCue integrates frame-to-frame visual changes, narration signals, and operation guidance from official software manuals to infer likely mouse and key-modifier actions, then produces aligned cue layers and editable artifacts for human refinement. Grounded in multimedia learning and cognitive load theory, we further develop a visual cue grammar for representing mouse, keybo
arXiv:2608.04831v1 Announce Type: new Abstract: In 2024, Google introduced "AI Overviews," a feature that displays an AI-generated result summary at the top of many Google search pages. This study investigates the role of AI in Google search using one month of web browsing data from a representative panel of 900 U.S. adults. Our analysis of the panelists' Google searches sheds light on AI Overviews, when they appear in Google search results, and what user behaviors are associated with AI Overviews. We identify several attributes that make a search query more likely to generate an AI Overview, including the length of a query, whether the query begins with a question word, and whether the query contains both a noun and verb. When it comes to user behavior, we find that clicks to sources cited in AI Overviews are very rare, occurring in only about 1% of visits to AI Overviews. We also find that AI Overviews are associated with fewer clicks and higher rates of ending browsing sessions. Imp
arXiv:2608.04798v1 Announce Type: new Abstract: When manipulating objects in immersive platforms through speech and gesture, users naturally construct spatial references, referring to scene entities, their bodies, or the environment. Leveraging spatial cognition theories, this work systematically examines how users construct and communicate spatial intent. Using a custom toolkit, we conducted a Wizard-of-Oz study to observe unconstrained multimodal (speech + gesture) input patterns in Virtual Reality for scene construction. Based on these findings, we formalize a framework that decomposes spatial references into three core components: Source, Anchor, and Frame, while characterizing their compositional strategies and explicitness. We demonstrate the utility of this Reference-based Manipulation framework by implementing an LLM-based pipeline featuring a set of example interaction techniques with a preliminary technical evaluation. Finally, we discuss key lessons learned for supporting re
arXiv:2608.04707v1 Announce Type: new Abstract: Anatomical liver resection (ALR) surgery is the most important treatment for liver cancer, yet preoperative planning demands complex, multi-stage clinical reasoning under competing safety constraints. Current 2D desktop tools are not well equipped to support this process, exhibiting three fundamental limitations: reliance on monolithic interfaces that fail to adapt to the distinct cognitive demands of each planning stage; a perceptual bottleneck caused by limited anatomical spatial representation and missing plane-vessel intersection visualization; and an attention bottleneck stemming from fragmented critical safety criteria display across separate views. We present LiverPlan, a stage-adaptive immersive visual analytics framework for ALR planning, grounded in an 8-month collaboration with two expert hepatobiliary surgeons. Decomposing the surgical planning process into three sequential yet cognitively distinct stages, LiverPlan externaliz
arXiv:2608.04605v1 Announce Type: new Abstract: The development of AI systems capable of emotionally resonant communication remains a significant challenge. This study examines how humans influence emotions in social deception games by comparing professional and non-professional players. We measured electrodermal activity during gameplay to capture physiological emotional responses and analyzed communication patterns during periods of high emotional arousal. Our results revealed distinct communication strategies: professional players maintained persuasion-based approaches under high arousal, while nonprofessional players shifted toward information-focused communication. Statistical analysis confirmed significant differences in expression patterns between expertise levels. Professional players exhibited more stable emotional states during gameplay, indicating better emotional regulation. These findings inform the design of AI systems that can adapt their communication strategies based o
arXiv:2608.04475v1 Announce Type: new Abstract: Despite the promise of virtual reality (VR) for intuitive spatial interaction, volume visualization (VolVis) in VR remains constrained by high rendering costs and motion discomfort. Recent advances have shown that representing volumetric scenes with 3D Gaussian splatting enables high-performance rendering, making this representation well-suited for VR. However, existing Gaussian-based scene editing workflows remain limited by slow offline segmentation and fatigue-inducing manual selection. To address these challenges, we present Super-Gaussian, a novel VolVis framework that enhances scene editing and interaction in VR through intuitive 3D Gaussian selection and natural language interaction (NLI). Our approach groups Gaussian primitives into higher-level units via feature-aware clustering, enabling efficient selection of complex volumetric regions, such as tumors in medical images or filaments in cosmological data, without point-by-point i
arXiv:2608.04435v1 Announce Type: new Abstract: Established channel effectiveness rankings primarily assess magnitude estimation accuracy in complete chart contexts, often neglecting other perceptual tasks such as discriminability, separability, and pop-out. To address this gap, we conducted crowdsourced experiments on seven core visual channels (position, length, tilt, area, curvature, luminance, and saturation) using primitive visual stimuli, a set of visual marks without chart-specific scaffolding to isolate channel-level variation. We evaluated these channels across four perceptual tasks (accuracy, discriminability, separability, and pop-out) and found that channel effectiveness is fundamentally multi-dimensional, with rankings shifting substantially across tasks. For instance, while spatial channels maintain an overall advantage, accuracy depends strongly on whether a fixed spatial anchor is available. Discriminability varies dramatically across channels and value ranges, a patter
arXiv:2608.04416v1 Announce Type: new Abstract: AI assistants are typically reactive, relying on users to initiate interactions. Proactive assistants go beyond this paradigm by autonomously initiating interactions based on users' activity contexts. However, appropriate interaction timing is user-specific and difficult to determine in advance, while online feedback offers valuable signals for personalization. Direct feedback-driven adaptation is therefore appealing, but remains challenging due to sparse interaction-worthy moments scattered across fine-grained user states. To address the issues, we propose Evidence-driven Online Preference Adaptation (EOPA), which grounds a user's interaction-timing preferences in measurable contextual evidence through two evidence carriers: temporal preference anchors and evidence-bearing activity prototypes. At each polling step, EOPA derives temporal and activity evidence from the carriers through user-prior-smoothed evidence estimation and uncertaint
arXiv:2608.04331v1 Announce Type: new Abstract: In human-AI collaborative data analysis, as analyses rapidly evolve, the artifacts meant to capture shared understanding often become incomplete or difficult to interpret, leading to undocumented assumptions, cross-user misaligned intent, context-poor prompts, and unwanted agent behaviors. To address these challenges, we introduce a rule-based coordination layer with two interaction mechanisms, intent scaffolding and prompt-time linting, that make analytic intent explicit and actionable during human-AI collaborative data analysis. We implement them in IntentLint, a proof-of-concept system that infers analytic intent from shared notebooks, represents it as structured, editable rules, and checks users' prompts against shared rules. IntentLint helps analysts externalize and refine their intent and proactively checks prompts for potential conflicts. A study with 16 data analysts shows that IntentLint improves awareness of collaborators' inten
arXiv:2608.04300v1 Announce Type: new Abstract: The use of intracytoplasmic sperm injection (ICSI), an assisted reproductive technique (ART), is increasing widely. ICSI is currently performed by specially skilled embryologists. However, with the increasing demand for ART, the shortage of skilled embryologists has become a problem. Therefore, we propose an immersive micromanipulation system that requires no special skills for efficient and accurate micromanipulation. Our proposed system is composed of a real-time three-dimensional (3D) imaging microscope and 3D operation interfaces. The 3D operation interfaces are stationary pen-type or wearable glove-type interfaces. In this system, an operator wearing a head-mounted display (HMD) and using 3D operation interfaces is immersed in a virtual micromanipulation space. The operator can move the pipettes by 3D operation interface and freely change the viewpoint. We verified that the proposed system improves the speed and accuracy of operating
arXiv:2608.04293v1 Announce Type: new Abstract: Generative user interface design tools enable rapid prototyping but often operate as black boxes with limited transparency and controllability. When outputs diverge from the designer's intent, users are left tweaking prompts via trial-and-error with little insight into the model's reasoning. We present LegoUI, a staged generative framework that structures the interface design process into sequential, interpretable steps along key design dimensions, capturing each step's result in a UI domain-specific language (UI-DSL) enriched with provenance. This approach exposes the model's intermediate reasoning and enables user intervention and iterative refinement. In a technical evaluation on 40 real-world design prompts, LegoUI's requirement analysis stage captured explicit requirements with over 95% accuracy, near-complete coverage, and zero redundancy. In user studies, participants using LegoUI reported significantly greater transparency, contro
arXiv:2608.04274v1 Announce Type: new Abstract: Social media recommendation feeds often optimize for users' immediate impulses rather than preferences they would hold after deeper reflection. Some systems address this misalignment by incorporating users' explicit preferences via a configuration page or in-feed controls instead of just behavioral signals. However, users typically have evolving preferences, and their stated preferences and behavior naturally diverge, necessitating continuous reflection and feed realignment. But existing strategies require the user to take initiative and are often effortful; as a result, in practice they are rarely invoked. We present Compass, a system that aligns a user's feed with their reflective preferences by helping users reflect on and articulate their preferences given their behavior. To enable continuous reflection during everyday browsing, Compass surfaces in-situ reflections via lightweight notifications, while feed alignment is achieved by per
arXiv:2608.04166v1 Announce Type: new Abstract: Generative AI agents are increasingly used in interaction design to facilitate ideation and offer critique, often following their own internal reasoning. These interactions tend to add design ideas and expand the design space. Our work explores an antagonistic role for design agents, prompting designers to engage with stakeholder tension. We built an AI agent inspired by adversarial design theory that enacts constructive conflict. We examine the agent's influence in a between-subjects experiment with 45 design students across three conditions: Self Reflection (unsupported review of the design proposal), Stepwise Guidance (written prompts that walk designers through a constructive-conflict framework), and Interactive Engagement (an AI agent that enacts the constructive-conflict framework interactively by synthesizing stakeholder pushback). The latter two conditions share the framework but differ in whether it is self-enacted or agent-enact
arXiv:2608.04120v1 Announce Type: new Abstract: As political disruption intensifies online discourse, Bluesky has become an important platform for political discussion and public reaction. In this study, we examine large-scale discourse on Bluesky related to U.S. policy developments associated with the Trump administration. Using the historical retrieval API, we collected all available posts matching Trump and related keywords from 2019 to 2026, yielding 38.5 million posts. We leverage a large language model (LLM)-assisted clustering pipeline, combined with human validation, to identify 14 interpretable thematic domains in English-language posts and 19 thematic categories across 258 executive orders (EOs) signed between January 20, 2025, and May 1, 2026. Our findings identify several dominant themes in Bluesky discourse, including executive governance, political identity, and national security, as well as recurring themes in EOs, including executive task forces, border enforcement, and
arXiv:2606.23462v2 Announce Type: replace-cross Abstract: Scientists do not, by profession, wage war. Yet warfare's vocabulary consistently appears in their abstracts. To quantify the extent to which warfare's vocabulary pervades scientific abstracts, we analyze 21.4 million papers (2010-2025; OpenAlex, PubMed). We additionally run a within-subject war-framing experiment ($N = 801$; 32{,}040 trials) designed to provide causal insight into the effects of militaristic language on persuasion. Between 2010 and 2025, the presence of militaristic terms in scientific abstracts rose 48\% in OpenAlex and 32\% in PubMed, with the rise accelerating sharply after 2019 (cross-database $r = 0.96$, $p < 10^{-8}$). The prevalence of militaristic language is conflict-aligned at both country and annual scales (Uppsala Conflict Data Program; $r = 0.77$-$0.84$), with the abstracts from the Global South displaying the fastest rise in militaristic language. Among disciplines, social sciences leads in level
arXiv:2604.26233v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) are proposed as legal decision assistants, and even first-instance decision-makers, across a range of judicial and administrative contexts, it becomes essential to explore how they answer legal questions, and in particular the factors that lead them to decide difficult questions. A specific feature of legal decisions is the need to respond to arguments advanced by contending parties. A legal decision-maker must be able to engage with, and respond to, including through being potentially persuaded by, these arguments. Conversely, they should not be unduly persuadable, deciding cases based on the skills of the advocates rather than the merits of the case. In this paper we explore how frontier open- and closed-weights LLMs respond to legal arguments. We propose a metric to measure persuadability in the trilateral setting in which competing advocates seek to persuade a judge of opposite conclusions. We
arXiv:2604.11459v2 Announce Type: replace Abstract: Algorithmic systems, particularly social media recommenders, have achieved remarkable success in predicting behavior. By optimizing for observable signals such as clicks, views, and engagement, these systems effectively capture user attention and guide interaction. Yet their widespread adoption has coincided with troubling outcomes, including rising mental health concerns, increasing polarization, and erosion of trust. This paper argues that these effects are consequences of a structural functional misalignment between what algorithms optimize - predictable behavior - and the human goals these predictions are intended to serve. We propose that this misalignment arises through three mechanisms: (1) a bias toward modeling fast, reactive behavioral signals over reflective judgment, (2) feedback loops that couple user behavior with algorithmic learning, and (3) emergent collective dynamics that amplify these effects at scale. Together, th
arXiv:2601.23112v3 Announce Type: replace Abstract: AI safety benchmarks are pivotal for safety in advanced AI systems; however, they have significant technical, epistemic, and sociotechnical shortcomings. We present a review of 210 safety benchmarks that maps out common challenges in safety benchmarking, documenting failures and limitations by drawing from engineering sciences and long-established theories of risk and safety. We argue that adhering to established risk management principles, mapping the space of what can(not) be measured, developing robust probabilistic metrics, and efficiently deploying measurement theory to connect benchmarking objectives with the world can significantly improve the validity and usefulness of AI safety benchmarks. The review provides a roadmap on how to improve AI safety benchmarking, and we illustrate the effectiveness of these recommendations through quantitative and qualitative evaluation. We also provide workflow-oriented guiding questions with i
arXiv:2504.14053v2 Announce Type: replace Abstract: Rating systems on accommodation platforms suffer from a familiar problem: nearly every listing displays a nearly perfect score, so the number that is supposed to separate good listings from bad ones barely varies. Whether the review text accumulating beneath those scores still carries usable information is an open question. I ask a dynamic version of it: does the text guests have already written predict where a listing's displayed rating moves next? Treating text and ratings as parallel channels that aggregate guest experience at different speeds, I construct a prespecified sentiment index from the complete review history of each listing in a two-wave panel of more than two hundred thousand listings across 34 U.S. markets. Because the broader project had explored these data before, I locked the model and its falsification checks in advance and reserved half of the markets, untouched, for a single confirmatory estimation. On those held
arXiv:2608.04641v1 Announce Type: cross Abstract: Generative AI is transforming legal translation by introducing opportunities alongside linguistic, technical, legal, ethical and cognitive risks. This chapter examines the implications of AI for professional legal translation and proposes an AI literacy framework tailored to the profession. It argues that AI does not change the fundamental objectives of legal translation but requires an extension of professional competence through AI literacy. The proposed framework comprises four mutually reinforcing dimensions, foundational, procedural, critical and strategic, and conceptualises AI literacy as a transversal component of legal translation competence that fosters digital resilience. It further discusses the pedagogical implications of this framework by proposing classroom activities designed to develop AI literacy in legal translator education, enabling future translators to integrate AI critically, responsibly and in accordance with pr
arXiv:2608.04571v1 Announce Type: cross Abstract: This report analyses data centre (DC) sustainability and circularity, revealing existing synergies and trade-offs: The PUE is too coarse, mixing cooling and power provisioning. It wrongly attributes server fan consumption and transformation losses to IT energy. It does not measure compute but infrastructure efficiency, which is already outstanding. Compute energy, however, is exploding. Better energy metrics for DCs would thus cover i) compute efficiency, ii) transformation efficiency, and iii) cooling overhead. Trade-offs exist between cooling energy and water as well as on-site and upstream water: Consuming water on-site lowers the cooling energy, which also lowers the water consumed upstream in power generation. For 'wet' electricity, there is little competition: It is worth spending more on-site energy to save both electricity and related upstream water. For 'dry' electricity, there is a trade-off. Waste heat recovery brings energy
arXiv:2608.04365v1 Announce Type: cross Abstract: Audits have emerged as a critical instrument for algorithmic governance, providing a mechanism for external scrutiny and governance of machine learning models. However, ensuring the integrity of such assessments remains a challenging issue. For instance in regulatory contexts, audits are typically declared or easily detected, thus enabling model providers to manipulate the process, whether intentionally or inadvertently. This vulnerability is particularly acute in the context of fairness evaluations, in which providers can often infer sensitive attributes and strategically equalize allocation rates between groups to satisfy fairness metrics. In this paper, we introduce a novel audit protocol designed to significantly increase the post-audit detectability of such manipulations by enabling the auditor to query the model in an oblivious manner. Our approach leverages a Private Information Retrieval mechanism to require the provider to labe
arXiv:2608.04056v1 Announce Type: cross Abstract: When people label text for sexism, they often disagree, and not because some of them are wrong: they genuinely perceive sexism differently. Most NLP systems discard this disagreement by collapsing it into a majority vote. We propose the Multi-Agent Perspectivist Preference Optimization (MAP-PO) framework to keep these different perspectives. On the EXIST 2024 dataset of labeled English and Spanish tweets, we first cluster annotators by their labeling behavior rather than their demographic attributes. We then fine-tune one Large Language Model agent per cluster to reproduce that cluster's annotation behavior, and coordinate the agents with preference optimization that combines individual and team-level rewards. We evaluate MAP-PO in four settings defined by two languages and two backbone language models, asking whether each agent reproduces the annotations of its own cluster and whether the agents together reproduce the majority label. T
arXiv:2608.04030v1 Announce Type: cross Abstract: Generative artificial intelligence (AI) has transformed text-to-image synthesis, yet its ability to represent specialized engineering domains remains largely unexplored. As an exmaple in nuclear engineering, general-purpose foundation models frequently generate physically incorrect or conceptually inconsistent images because they lack domain-specific knowledge. This work presents one of the first systematic studies of domain adaptation for nuclear text-to-image generation through fine-tuning of open-source diffusion models. We curate a dataset of 1,000 captioned nuclear energy images spanning reactors, fuel cycles, radiation, and related concepts, and use it to fine-tune three state-of-the-art open-source models: Stable Diffusion XL (SDXL), SD-v3.5-Medium, and the flow-matching Flux.1 model. Their performance is evaluated using both quantitative image-similarity metrics and qualitative expert assessment against the corresponding zero-sh
arXiv:2608.05050v1 Announce Type: new Abstract: Against the backdrop of violence in police interactions with the U.S. public, we explore how deferentially police officers speak to virtual characters depicted as Black adult males in vir- tual reality (VR) simulations. We evaluate the effect of seeing and communicating with these characters through a causal in- ference lens, where the assignment of the Black man character to a police officer and simulation is the treatment variable. Our (marginal) average treatment effect AT E measures the social impact of the character on the deference of officer statements with each turn of the conversation. Soberingly, we find that most officers speak less deferentially to Black man characters, except for White, biracial, and multiracial female officers, es- pecially in settings where the VR character was known to be a suspect. Across a full conversation of a typical VR scene, these marginal AT Es can result in notable changes in def- erence of tone (
arXiv:2608.05008v1 Announce Type: new Abstract: This paper presents the first empirical study of advertising content being rolled out in the user-facing online interfaces of large language models (LLMs). We systematically examine possible demographic differences in ad content shown to U.S. users of ChatGPT using a sock puppet audit methodology. We create and deploy 91 sock puppets in a 3x3 factorial design, using geolocation cues (account IP proxies and location-signaling prompts) to signal three racial/ethnic groups (Black, Hispanic, and White) and three income terciles (low, medium, and high). We conduct data collection starting in February 2026, collecting over 3,000 advertisements from 186 unique advertisers in response to 335 prompts on a range of realistic user queries. We find that accounts begin receiving ads 14 days after account creation, and that lower-income accounts, regardless of race, are more likely to receive ads. In this first phase of ChatGPT ads, the ads themselves
arXiv:2608.04892v1 Announce Type: new Abstract: Artificial intelligence systems that adapt instruction to individual learners are increasingly deployed in K-12 classrooms, yet empirical evidence on their effects in authentic elementary settings remains limited, particularly for students with mathematics learning difficulties. This dissertation examines AI-powered personalized learning during primary school fraction instruction, a domain that is foundational to later mathematics and STEM achievement. The first manuscript presents a systematic review of research on artificial intelligence in mathematics education published between 2020 and 2024. The second manuscript reports a quasi-experimental study evaluating Mathbot, a chatbot-based personalized learning platform, against business-as-usual classroom instruction. Repeated measures ANOVA was used to assess change in fraction comprehension and situational interest across time points. Results indicated modest improvements in fraction com
arXiv:2608.04774v1 Announce Type: new Abstract: Echo chambers are an inevitable consequence of the human cognitive system being evolutionarily designed to prioritize processing of high-relevance information at the small-group scale, combined with algorithms that optimize engagement as their sole objective. Conventional prescriptions that normatively criticize echo chambers and demand individual behavioral change have low feasibility given these cognitive constraints. This paper constructs an Agenda Democratization Index (ADI) that quantities the decentralization of agenda-setting power using four variables barrier to entry, granularity, interactivity, and feedback resolution and a SocialInformation Health (SIH) model that integrates ADI with the strength of bridging mechanisms. Based on this model, we propose domain-selective bridging, which incorporates not only engagement but also bridging into algorithmic scoring functions, optimizing the bridging weight for each information domain
arXiv:2608.04352v1 Announce Type: new Abstract: Stack Exchange is a widely used question-and-answer network that facilitates knowledge exchange across diverse domains. Within this network, the Computer Science (CS) Educators Stack Exchange provides a dedicated platform where CS educators exchange ideas, seek advice, and discuss teaching practices. In this study, we analyzed 79,854,463 Stack Exchange posts, comprising 32,187,805 questions and 47,666,658 answers, with a particular focus on English-language posts contributed by CS Educators participants. Using topic modeling, we identified, manually labeled, and hierarchically organized the underlying discussion topics, then examined their distribution and complexity. Our findings reveal evolving discussion patterns spanning both technical (IT) and non-technical (Non-IT) domains. Within the IT category, programming and software development were the most prominent topics, whereas mathematics, education, and the humanities received substant
arXiv:2608.04273v1 Announce Type: new Abstract: Artificial intelligence is moving the technology sector into domains social work has long served, including crisis response, mental health care, benefits administration, vocational rehabilitation, and child welfare. Social workers meet technology teams as users of their tools, as subjects in their datasets, and as first responders to what those systems deploy, yet they study these systems from outside the settings where the decisions are made. This paper introduces the standard roles on a technology product team and the decisions each one controls, reviews the disciplines around AI-era technology together with the social work scholarship that meets each, and identifies five groups of technology decision roles social workers can hold across the technology industry, human service organizations, and policy institutions, spanning product, governance, organizational technology leadership, grantee collaboration, and policy work. Product managem
arXiv:2608.04251v1 Announce Type: new Abstract: An emerging literature examines the critical question of when and how prediction can be useful in allocating scarce societal resources. We examine a novel variant of this question: What happens when predictive uncertainty differs systematically across the population? This can occur in several situations; for example, when machine learning models have significantly different accuracies across different demographics. We show that this uncertainty has serious implications for resource allocation when coupled with commonly used binary measures of societal benefit from allocation. We formulate a novel mathematical model of scarce resource allocation that accounts for heterogeneous predictive uncertainties and analyze implications for both the allocation mechanism and the realized population-level benefits. We find that when resources are very scarce, maximum marginal benefit (MMB) prioritization favors individuals with lower predictive uncerta
arXiv:2608.04020v1 Announce Type: new Abstract: Artificial societies built from large language model (LLM) agents are becoming a practical research tool in economics, political science, sociology, and computer science. Most attention has focused on the properties of the agents: their prompts, personas, memory, reasoning, and similarity to human subjects. This paper argues that the institutional architecture of a simulation is equally important. I demonstrate the point in a small repeated induced-value market experiment. The same LLM agents face the same private values, costs, history, and payoff-framed instructions, while only the rules of exchange vary across five standard market institutions: a call market, posted-offer market, posted-bid market, continuous double auction, and bilateral bargaining. Outcomes differ sharply. Call markets realize 88.6% of efficient surplus; posted-offer and posted-bid markets realize about 66%; continuous double auctions realize 71.5%; and bilateral bar
arXiv:2608.04019v1 Announce Type: new Abstract: With robots expected to replace humans in some professions, AI presents a new development prospect through the provisions of data work. For over a decade, large Silicon Valley technology firms have been relying on outsourcing of data work via a host of intermediary suppliers and labour platforms to different parts of the globe often the Global South region, such as East Africa. Much of this work is often shrouded in secrecy as firms rarely reveal the extent of their value chains. This results in the poor and marginalised forming the hidden underbelly of the Silicon Valley, training some of their most advanced machines in adverse working conditions. Drawing upon the survey of workers in Kenya, a major hub for data work in Africa, the paper highlights the physical and psychological impacts on workers. Survey data is complemented with in-depth interviews and auto-ethnographic account of two ex-data workers-turned activists who worked for a l
arXiv:2608.04018v1 Announce Type: new Abstract: AI agents are increasingly embedded in organizational workflows, where they interact with external information sources and invoke digital tools to perform operational tasks. As organizations adopt such systems, a critical challenge is identifying and mitigating risks arising from malicious or untrusted external information that can steer agents toward unintended actions. Existing red-teaming approaches largely rely on fixed attack templates or final attack outcomes, providing limited visibility into how attacks unfold through multi-step reasoning and tool use. We argue that agent execution risk should be understood as a trajectory-level phenomenon. Building on this perspective, we propose TrajRed, a trajectory-guided red-teaming framework that uses execution trajectories to uncover vulnerabilities in agentic AI systems. We further develop TrajGuard, a runtime governance layer that uses high-risk trajectories discovered during red teaming
arXiv:2608.04017v1 Announce Type: new Abstract: The rise of open and distance education has made it more important than ever to have communication tools that are simple, flexible, and good for helping students work together, talk to each other, and feel connected. Researchers have already looked at how instant messaging apps like WhatsApp and Telegram can be used for learning. Viber, on the other hand, has not been studied as much, especially when it comes to its use in higher education at a distance. This article builds on a previously published conference case study conducted at the Hellenic Open University (HOU), which examined the use of Viber in distance collaborative projects. The present study extends that work by offering a comparative discussion of communication ecosystems in higher distance education. Using ideas from connectivism learning theory, along with the concepts of social presence and community-based learning, this article looks at how chatting on Viber can add to an
arXiv:2608.04016v1 Announce Type: new Abstract: Social disadvantage is associated with multimorbidity, but the pathways linking social conditions to disease burden remain poorly understood. We developed an AI-driven multimodal mediation framework that integrates socioeconomic, psychosocial, clinical, laboratory, behavioral, and genomic data from the All of Us Research Program. Modality-specific variational autoencoders were used to derive latent representations of each data domain, and mediation analyses were subsequently performed in latent space to evaluate indirect associations between socioeconomic disadvantage, psychosocial factors, and multimorbidity. The final analytic cohort included 20,804 participants with complete multimodal data. Across 800 exposure--mediator--outcome combinations, mediation signals were concentrated within a small number of latent dimensions. The strongest indirect association linked a socioeconomic disadvantage dimension, a psychosocial vulnerability dime
arXiv:2608.04011v1 Announce Type: new Abstract: This article examines the enduring epistemic and methodological crisis of traditional legal practice in light of the opportunities and constraints introduced by artificial intelligence. It proposes an ontologically grounded framework termed the Mecellem semantic protocol as a response to this crisis. The analysis focuses on the structural tension within law between maintaining normative coherence and adapting to evolving social and institutional conditions, and shows why approaches based solely on codification, positivist systematization, or quantitative methods such as jurimetrics are insufficient. The article argues that legal reasoning cannot be reduced to data retrieval or statistical pattern recognition. Instead, it is grounded in the premise that meaning is context-dependent and must be dynamically reconstructed through ontologically defined entity categories and differentiated layers of knowledge. Within this perspective, Mecellem
It's critical that schools create an environment where students thrive and teachers and staff feel supported and empowered.
Rather than replacing student thinking, when teachers design and guide AI experiences, the technology is most often used to deepen critical thinking and strengthen instruction
The recent Instructure/Canvas breach should be a wake-up call for every school and university relying on third-party platforms to power teaching and learning.
Community schools boost student success by connecting learning with mental health, family support, and local community resources. The post What If School Offered More? The Case for Community Schools appeared first on Getting Smart .
Despite the lack of grammatical capitalisation, ispring is a really useful teaching tool.
Tech & Learning has partnered with the Ed-Tech Leadership Collective to explore how market pressures are affecting districts' vendor choices
If you lead professional learning, whether as a school leader or PD facilitator, your goal is to make each session relevant, engaging, and lasting. AI can help you get there by streamlining prep, differentiating for diverse learners, combining follow-ups with accessibility for absentees, and turning feedback into actionable improvements.
Judi Health expanded its healthcare network to offer specialized virtual care for cancer, autoimmune and GI conditions while helping employers reduce overall healthcare costs. The post Judi Health Taps Color Health, WellTheory, Cylinder for Clinical Ecosystem appeared first on MedCity News .
A roundup of the seven most notable hospital mergers and acquisitions announced or completed this summer, including Prisma Health’s acquisition of Erlanger Health and Sanford Health’s purchase of North Memorial Health The post 7 Hospital Mergers from Summer 2026 appeared first on MedCity News .
Keaton Wadzinski has been on both sides of the student voice equation — as a young person who was sometimes tokenized, sometimes genuinely empowered, and as an adult now designing the conditions for youth agency to flourish. In this deeply personal and strategically rich essay, he challenges education leaders to move beyond advisory committees and symbolic inclusion toward something far more honest and far more powerful. This is essential reading for any leader who believes young people deserve a real role in shaping the future of learning. The post Student Voice, All Grown Up appeared first on Getting Smart .
By all appearances, she is a typical fourth-grade girl. She has long, dark brown hair and an infectious smile, and she dreams of becoming a police officer when she grows up. But unlike most 10-year-olds, a year ago this child left her family and her home, walking for days with little to eat. To protect […]
Over the past few years, Ginger Williams has watched the number of children learning English inch up at the six Head Start sites she runs across Snohomish County, Washington, just north of Seattle. The federally funded preschool program has long been required to provide special support for what it calls “dual language learners.” If a […] The post English-only proposal for Head Start would set kids back, say parents and program leaders appeared first on The Hechinger Report .