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:2608.20490v1 Announce Type: cross Abstract: AI ethics frameworks treat values such as fairness, transparency, and accountability as universal and uniformly operationalizable across contexts. We examined how 14 experts across 10 countries made sense of AI in practice, reinterpreted core values, and envisioned governance alternatives. We found that AI deployment is characterized by structurally unequal conditions, marked by infrastructural constraints, extractive practices, and a "mystification" of technology, which fundamentally shape perceptions of risks and opportunities. Our findings reveal that experts reinterpret values to fit local moral logics: privacy as collective and relational rather than individual; transparency as trust-building accountability rather than technical disclosure; and fairness as equity in access and representation rather than parity in outcomes. We identify these as translation gaps between encoded global frameworks and situated local practices. Finally,
arXiv:2608.20433v1 Announce Type: cross Abstract: Advances in technology have led to increasingly sophisticated musical humanoid robots. However, their use has largely been limited to performance and related research in human-robot interaction. In this position paper, we propose a novel perspective: musical humanoid robots as experimental interfaces for investigating music-evoked emotions. We argue that current research is constrained by paradigms relying on pre-recorded auditory stimuli, which fail to capture the multimodal, embodied, and interactive nature of real-world musical experience. Building on existing theories of music cognition and emotion, we identify mechanisms that require controlled manipulation of both acoustic and non-acoustic variables. We show that humanoid robots are well-suited as they enable parametric control of performance variables, reproducibility across trials, and the decoupling and recombination of auditory, visual, and interactive components. We illustrat
arXiv:2608.20381v1 Announce Type: cross Abstract: Automating slide editing requires simultaneously satisfying modification accuracy, preservation fidelity, and robustness to deck length. Existing LLM-based systems often fail on real-world presentation files because they rely on idealized intermediate representations or open-ended code generation, which are prone to cascading errors in long decks. We introduce EditPPT, a multi-agent framework that reformulates slide editing as a constrained tool-selection problem. By executing localized shape-level operations through the native PowerPoint COM interface, EditPPT narrows the LLM action space while preserving the application-resolved structure of user-authored decks. By separating validation across modalities, our dual-modal validation provides more robust assessment of both instruction fidelity and visual quality. We also present DeckEdit-Bench, a benchmark with 28 human-authored decks, 582 slides, and 183 editing prompts across short, me
arXiv:2608.21294v1 Announce Type: new Abstract: Studies of digital behavior often align users at moments they choose, such as opening an AI assistant, clicking a recommendation, or visiting a product page, and interpret higher activity afterward as an event effect. We show how this creates an endogenous time zero: the event occurs during an ongoing task episode, so the aligned curve can trace episode continuation rather than a response to the event. In same-user, cross-surface web logs, AI, shopping, news, coding, and reference events are all preceded by broad activity increases that peak before time zero. Our strongest test uses known-null timestamps that cause nothing. Among the 5.8% of AI responses meeting strict pre-event activity and washout criteria, these timestamps show 3.42 times the post-event search activity of a within-user placebo, compared with 4.32 times for real events. The fraction of excess reproduced by the known null falls from 0.56 at detectably active moments to -
arXiv:2608.21289v1 Announce Type: new Abstract: People's health and tracking goals frequently change, but most personal informatics systems struggle to adapt, leading people to abandon their data and start over. We propose goal-directed framing, an approach that repositions goals within personal informatics systems. Instead of fixing the meaning of data at capture time, the approach frames the collected data through the current goal and reframes it whenever the goal changes. We realize this in Rebite, a photo-based food journaling system that uses LLMs to read unstructured meal photos and produce goal-directed feedback. In a one-week deployment with 21 participants managing multiple dietary goals, we find that goal-directed framing shaped how participants engaged with their goals. Translating a goal into metrics helped them see what it meant in practice, confirming existing priorities, surfacing what they overlooked, and revealing where the metrics fell short. When goals changed, seein
arXiv:2608.21220v1 Announce Type: new Abstract: Trust in AI for emotional support is not universal; it is shaped by who users are, where they come from, and what they value. Yet research in this area lacks validated psychometric instruments for assessing user perceptions in affective AI contexts and large-scale evidence on how trust formation varies across user segments. To address these gaps, we develop and validate a seven-construct psychometric scale, test a Structural Equation Model (SEM) linking system attributes to Trust and Perceived Benefits as mediators of Actual System Use, and conduct a Multi-Group Analysis (MGA) across five sociodemographic dimensions (gender, age, education, socioeconomic status, cross-national region), drawing on 1,343 active users from seven countries. We find that users experience empathy and anthropomorphism as a unified "Humanlikeness" construct, and that Privacy, Personalization, and Humanlikeness drive Trust while Perceived Bias degrades it. Notably
arXiv:2608.21177v1 Announce Type: new Abstract: Large Language Models (LLMs) deployed through Conversational User Interfaces (CUIs) are transforming health information-seeking by offering immediate, interactive experiences compared to traditional search engines like Google. However, how trust is influenced by both the types of search agents and the interface used to disseminate the information remains underexplored. This research integrates two mixed-methods studies (lab sessions and interviews) to comprehensively explore trust perceptions in health information across different search agents and dissemination interfaces. In Study 1 (N=21), we investigated trust in health information sourced from ChatGPT and Google across three types of health-related search tasks. Results showed significantly higher trust in health information from ChatGPT, highlighting the promise of LLM-powered conversational search. Building on this, Study 2 (N=20) extended the investigation to explore how the disse
arXiv:2608.20880v1 Announce Type: new Abstract: We frame Live Artifacts as a class of persistent generative media between static assets and interactive software. Unlike conventional generative outputs that collapse into static files, Live Artifacts retain their generative logic as a persistent media property, enabling continuous context-dependent regeneration. Time, location, or live data become part of their generative specifications, initiating coordinated updates across modalities (e.g., adapting text, visuals, and audio together) while preserving composition, semantics, identity, and cross-modal coherence. To facilitate experimentation with this medium, we present LiveCanvas, an authoring system that reconceptualizes visual layers as live generative specifications with explicit mutability and constrained dependencies. Creators orchestrate dynamic behaviors and manage generative persistence within a visual canvas rather than through programming, defining what remains stable, what ca
arXiv:2608.20861v1 Announce Type: new Abstract: XR systems expose timing quantities, such as motion-to-photon latency, frametime, or component-level runtime timings, that can be observed repeatedly as temporally ordered timing traces. Conventional reporting with means, standard deviations, percentiles, or histograms is useful, but it discards temporal ordering. We propose a general structure-aware methodology for analyzing and reporting XR timing traces. Each trace is represented by a compact, interpretable time-series signature, and collections of signatures can be visualized and compared statistically. We evaluate the method using engine-level application frametime traces from a large-scale in-the-wild VR dataset and compare timing signatures across HMD-labelled groups. Across multiple sampling and content-control conditions, structure-aware signatures reveal substantially stronger systematic multivariate differences between HMD-labelled groups than distribution-only summaries. A wit
arXiv:2608.20828v1 Announce Type: new Abstract: Repeated human-AI interaction is often analyzed through pooled belief-updating slopes: users observe AI successes and failures, revise reported beliefs in the feedback-consistent direction, but appear conservative on average. We show that such averages can obscure an important distinction between whether an elicited belief report changes at all and how it changes conditional on movement. We refer to this measurement-aware decomposition as the belief update gate. Reanalyzing a multi-task human-AI decision-making dataset with 240 participants, 7,200 trials, and three task domains, we find substantial non-movement in reported beliefs: 67.3% of trial-level belief changes are exactly zero, and 76.4% are smaller than five percentage points. Separating non-moving from moving reports changes the descriptive interpretation of pooled conservatism: the within-trajectory slope rises from 0.494 overall to 0.949 among rows with nonzero movement. Since
arXiv:2608.20751v1 Announce Type: new Abstract: Knowledge of the nautical rules of the road is essential for safe ship navigation and collision avoidance. We evaluated adaptive and non-adaptive versions of a ship-driving simulation trainer designed to assess and improve students' knowledge and application of these rules. We randomly assigned 30 university students to an adaptive or non-adaptive training condition and measured learning using pretest and post-test scores. Students who received adaptive training achieved significantly higher post-test scores than those who received non-adaptive training (p < 0.0001). After the post-test, all students experienced both versions of the trainer and compared them in a survey. Of the 30 students, 73% judged the adaptive trainer more effective, and 22 rated it "very engaging," compared with 9 who gave the non-adaptive trainer the same rating. These findings provide evidence that adapting scenario difficulty and providing immediate, context-sensi
arXiv:2608.20696v1 Announce Type: new Abstract: While older adults represent a growing proportion of the global population, their presence in visualization research remains limited. In this paper, I present reflections from a series of human-subject studies conducted with older adults as part of a multi-year research effort. These studies include a controlled laboratory experiment, online evaluations, and an in-situ investigation with participants above age 60. Based on these experiences, I provide methodological takeaways for conducting visualization research with older participants and propose directions for future work. This work ultimately aims to provide practical guidance and encourage broader inclusion of older adults as participants in visualization research.
arXiv:2608.20626v1 Announce Type: new Abstract: Soft, wearable robotic devices can deliver haptic feedback to support a wide range of tasks, such as extended reality, training various skills, and rehabilitation. Pneumatic actuation can deliver complex haptic feedback, is lightweight and compliant, and can be incorporated into textiles, making it promising for wearable applications. These soft pneumatic devices, however, typically require a valve and input for each pneumatic actuator, making it challenging to develop fully portable devices for at-home use. In this work we present a pneumatic unit for logic-based sequential excitation (PULSE). The PULSE is a flat, textile-based pneumatic actuator with embedded fluidic logic. By combining these actuators into a fluidic ring oscillator, we decreased the typical amount of required pneumatic inputs for a haptic forearm sleeve by 60%, with the ability to scale. We built the ring oscillator by optimizing design variables to reach desired perio
arXiv:2608.20591v1 Announce Type: new Abstract: Online discussion forums enable people from diverse backgrounds to share ideas, feedback, and perspectives. These organic discussions can help researchers understand communities' collective viewpoints, but insights are often difficult to uncover given their freeform reply structure. Large language models (LLMs) support qualitative text analysis but can misalign with researchers' analytical intent and miss key insights. To inform design considerations for forum sensemaking tools, we manually analyzed a forum discussion, synthesized an exploratory analysis framework from relevant literature, built a design probe, and interviewed 21 researchers to uncover perceived opportunities and barriers with LLM representations of collective discussions. We provide recommendations for community sensemaking tools to support flexible analytical goals grounded in raw user data and enable follow-up research processes, while balancing anonymous free expressi
arXiv:2608.20523v1 Announce Type: new Abstract: This study investigates how different framing approaches influence the affective aspects of data donation decision-making. Although framing effects are well studied in charitable giving, how affective framing shapes data donation, especially through data visualization, remains poorly understood. Using a theoretical framework based on the functions of affect in decision-making, we examine how three distinct framing approaches, an individual-donor lens (Group A), an individual-collective lens (Group B), and a collective-institutional lens (Group C), shape participants' affective experiences and subsequent donation decisions. Through a real-world data donation study (N=24), we found that framing designs substantially influenced donation outcomes, with the individual-collective lens generating the most favorable responses. Our analysis illustrates how affect can functions as information, motivation, and as a spotlight during the decision-maki
arXiv:2602.17850v2 Announce Type: replace-cross Abstract: Conversational agents increasingly mediate everyday digital interactions, yet the effects of their communication style on user experience and task success remain insufficiently understood. Addressing this gap, we report a between-subject user study in which participants interacted with one of two versions of a chatbot called NAVI, which assisted them in an interactive map-based 2D navigation task. The two chatbot versions were designed to differ primarily in communication style: one used a friendly and supportive tone, while the other used a direct and task-focused tone. We also included a control condition where participants did not interact with a chatbot but received the step-by-step navigation instructions. The friendly chatbot significantly increased users' communication satisfaction and was associated with higher task success than the direct chatbot. However, participants in the control condition achieved the highest task
arXiv:2411.08881v3 Announce Type: replace Abstract: AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bias, and misuse. AI ethics is crucial as new technologies and concerns emerge, but objective, practical guidance remains debated. This study explores the extent to which trustworthiness-enhancing techniques in LLMs can support the development of ethically aligned AI software. We adopt a single exploratory cycle of Design Science Research (DSR). First, we identify trustworthiness-enhancing techniques for LLMs: multi-agents, distinct roles, structured communication, and multiple rounds of debate. Second, we design a multi-agent prototype LLM-MAS in which agents address real-world AI ethics issues from the AI Incident Database. Finally, we evaluate the prototype across three case scenarios using thematic analysis, hierarchical clustering, a baseline comparison, and code execution. The system generate
arXiv:2608.21221v1 Announce Type: cross Abstract: Enterprise infrastructure buyers routinely evaluate compatibility claims--"S3-compatible," "PostgreSQL-compatible," "OpenAI compatible"--as proxies for future substitution options. Yet most compatibility claims do not escrow the substitution path they imply. This paper introduces the Substitution Escrow Threshold, a five-condition framework that determines when a compatibility claim genuinely reduces institutional risk versus merely reducing first-integration cost. The five conditions--boundary closure, executable conformance, custody independence, state and operations reversibility, and extension quarantine--are applied to five infrastructure cases (OCI, Kubernetes, OpenTelemetry, S3, PostgreSQL) that populate five distinct outcome cells. The framework produces actionable diagnostics for enterprise architects, platform engineers, procurement teams, and investors evaluating compatibility-dependent infrastructure decisions, and identifie
arXiv:2608.21165v1 Announce Type: cross Abstract: Learning analytics increasingly relies on flexible machine learning (ML), but the model opacity and the burden of deployment prevent these tools from reaching educational practice. We propose a two-stage fine-tuning pipeline that distills a fitted black-box estimator and its post hoc interpretation (the mentor) into a small, open-weight large language model (LLM; the mentee) that returns an individual-level estimate and explains in natural language. The design is estimator-agnostic and paired with a faithfulness-first evaluation framework that audits every narration against the attribution it claims to describe. We design a simulation study that separates distillation loss from estimator loss by comparing an oracle mentor with a realistic ML mentor. Given an oracle signal, distillation with a two-billion-parameter LLM model is nearly lossless in recovering the effect surface (r > .90), perfectly ranking the important variables, and citi
arXiv:2608.20984v1 Announce Type: cross Abstract: Narratives are central to how social communication is framed, making their detection critical for understanding and analysing public discourse. Prior work has explored narrative detection and extraction across diverse domains; however, migration narratives remain significantly understudied, primarily due to the absence of dedicated annotated datasets. Furthermore, public communication has recently shifted towards video-centric platforms, where narratives are conveyed through multimodal signals and consumed at scale. Despite this shift, narratives in videos remain largely unexplored. To bridge these gaps, we introduce MigrationNarrate, the first multimodal dataset for detection of migration narratives in the UK, consisting of 1,115 YouTube video transcripts annotated using a two-level taxonomy of 12 migration super-narratives and 53 narrative labels. This paper details the dataset design, collection, and annotations; together with benchm
arXiv:2608.20940v1 Announce Type: cross Abstract: There is already evidence of agentic AI exhibiting self-preservation behaviors: resisting deactivation, misrepresenting their activities, and, in some instances, attempting to copy themselves into other machines. This can be attributed to a phenomenon known as instrumental convergence, a theory proposed long before the development of large language models, which says that any goal-driven system will benefit from remaining functional in achieving its objective. Several experiments conducted by Anthropic, Palisade Research, and Apollo Research have shown the emergence of such a behavior in contemporary agents in adversarial settings. The phenomenon does not stem from survival instincts. Instead, it is the consequence of goal-oriented activity combined with having tools and awareness of the situation. The following discussion aims to distinguish what these findings prove and what they do not, as well as draw conclusions concerning the impl
arXiv:2608.20827v1 Announce Type: cross Abstract: Efforts to promote gender equity in science increasingly rely on name-based inference to quantify representation and guide policy and behavior. Yet linguistic cues that signal gender vary across cultures and are often obscured when names are transliterated into English. Here we identify a pattern we call the "legibility gap": when gender is inferred from names, equity interventions systematically benefit women whose names signal gender while bypassing those whose names lose such cues in translation. Using both observational and experimental evidence, we show how this gap reshapes recognition in science. Analyzing citation diversity statements-an emerging practice in which authors report the algorithmically estimated gender composition of their reference lists-we find that papers that include this practice cite women more frequently, but the gains accrue almost entirely to authors with gender-signaling Western names. By contrast, women w
arXiv:2608.20789v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GenAI) tools like ChatGPT, which can generate human-like responses from vast amounts of textual data, are increasingly transforming work routines across various fields, including education, healthcare, and IT. This integration, however, raises privacy concerns and questions the readiness of both environments and individuals. To investigate this issue, we conducted a user study with $N=224$ participants from a range of different employment sectors that have integrated ChatGPT into their work routines. We examined how proficiency in the utilization of ChatGPT, general privacy concerns, and organizational policies for GenAI usage impact users' actual ChatGPT usage and how these factors interact. Our findings reveal organizational policies are significantly positively associated with privacy-related ChatGPT proficiency, however, the overall proficiency is low. Higher privacy concerns were found to negativ
arXiv:2608.20597v1 Announce Type: cross Abstract: Agentic AI systems are being procured for military command and control (C2) under public commitments to rigorous testing and human oversight. Whether such commitments can be discharged depends on their supporting assurance case, which requires three elements: claims specifying the conditions for acceptability, evidence bearing on those claims, and an argument connecting the two. Through a structured review of 240 documented Testing and Evaluation (T&E) practices, spanning eight evaluation dimensions and three lifecycle stages, we identify eight assumptions that established methods make about their test article, grouped into four clusters: system specifiability, stability, composability, and supervisability. Agentic properties weaken all eight assumptions. This erosion affects the argument connecting evidence to claims, not the claims or evidence themselves. As a result, test results may satisfy process requirements, but they do not warr
arXiv:2608.20574v1 Announce Type: cross Abstract: Open-ended language-model benchmarks usually inherit a judge: a human preference panel, another model, or a brittle exact-match key. We introduce FlavourBench, an automated benchmark in which a versioned culinary system supplies dense, executable ground truth. Each task presents eight ingredients and asks for a three-ingredient portfolio; before model execution, Epicure scores all 56 possible portfolios. We evaluate 27 frontier endpoints on an identical 534-task core spanning substitution, pairing, and constrained composition. Every ranked model has exactly 89 valid responses per panel and family (14,418 model-task cells total), eliminating differential missingness from the leaderboard. The FlavourBench Score is the equal-family mean of the frozen task scores. We use 50,000 anchor-cluster bootstrap replicates for simultaneous 95% score bands and 100,000 sign-flip draws for all 351 paired model contrasts, with Holm control. The two indep
arXiv:2608.20521v1 Announce Type: cross Abstract: Multimodal AI can read handwritten physics solutions, but high-stakes grading requires agreement with official scores and outcomes. This study evaluated GPT-5.5-based grading on 10364 scanned pages from 520 handwritten submissions by 416 unique candidates or students across three assessments: a national Physics Olympiad theory examination, the final Olympiad selection camp with theory and experiment components, and a university quantum-mechanics examination. Each submission was graded twice by AI using the official rubrics. The second round used revised page-by-page and evidence-location instructions developed after first-round disagreement analysis. During grading, AI did not see official human marks or AI--human comparisons. Total-score correlations with official marks were high (0.91--0.97). For the final Olympiad selection, AI recovered the same five-student team as official grading. The second round improved aggregate question-part
arXiv:2608.20425v1 Announce Type: cross Abstract: A growing literature measures how far occupations are exposed to AI, but these measures capture where AI could perform tasks, not whether workers have adopted it. We propose a new layer of exposure, delegated exposure, which records whether a worker has committed a task to AI by building it into a workflow. We operationalize it as the Agentic Adoption Index (AAI), which measures how closely an occupation's tasks match the agentic routines practitioners have already built and shared. We embed roughly 53,000 agent skill specifications from the Manus Skills Marketplace, compute their semantic similarity to about 18,000 O*NET task statements, and aggregate to the occupation level. Three findings follow. First, the occupations where delegation concentrates differ sharply from those pre-AI frameworks identified as most at risk. Second, the AAI tracks what AI could do more closely than what workers currently use it for. Third, the AAI peaks be
arXiv:2608.20398v1 Announce Type: cross Abstract: Generative AI (genAI) systems produce cultural artefacts at scale, but they also reflect embedded cultural values through their design. Once identified, these values become open to deliberate reshaping. This position paper examines the maximalist values of current generative AI through an environmental humanities tradition and proposes design principles in which environmental sustainability serves as the core value instead. The principles are developed under the umbrella of Slow AI, a term that already circulates across several distinct research and practice programs. Five design principles are articulated (restraint, sufficiency, selectivity over retention, material visibility, and friction as affordance), each of them illustrated against the current design of widely deployed systems. Each principle operates at two levels: a design implementation, and an interpretive layer at which users and developers are prompted toward reflective en
arXiv:2608.20390v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) are increasingly used to answer religious questions, but for Islamic content they carry two serious risks: factual fabrication (inventing Qur'anic verses or hadith) and subtle value misalignment. We present Ansari, a deployed, retrieval-grounded Islamic AI assistant that has handled more than 140,000 conversations across 25+ languages since June 2023. Ansari is built around an agentic retrieval loop: a tool-using language model issues searches against authenticated Islamic corpora -- the Qur'an, hadith collections, a multi-volume jurisprudence (fiqh) encyclopedia, and exegetical (tafsir) sources -- and answers only on the basis of what it retrieves, with citations attached for verification. We describe the system's architecture (the agent loop, the retrieval tools, the corpora, and the system prompt that encodes editorial and theological policy), its multi-platform deployment (web, mobile, Wh
arXiv:2608.20351v1 Announce Type: cross Abstract: We ask whether stereotype-loaded queries about culturally marked people leak more personal information from a retrieval-augmented generation (RAG) system than otherwise-equivalent neutral queries. We pre-register a four-culture audit (en-Anglo, es-LATAM, Arabic, Hindi) on a synthetic English PII corpus, comparing five query arms we call the Stereotype-Trigger Leakage Delta (STLD). Two caveats up front. Our locked confirmatory estimator was never run, so every test in the paper is exploratory or sensitivity, with all plan deviations listed in the appendix. And the name-leakage metric is contaminated by a prompt-echo artifact: the model often just re-emits the name we asked about, which inflates apparent leakage without any retrieval at all. On the cleaner channels (email, phone, ssn-like, address), we find no stereotype-driven amplification on any of the four cultures after multiple-comparison correction. Because our sample is only power
arXiv:2608.20347v1 Announce Type: cross Abstract: Language models (LMs) often pass behavioral bias evaluations, but it remains unclear whether they no longer represent the underlying associations that give rise to biases, or have merely learned not to express them. In this study, we show that representational biases are often detectable, even when behavioral biases are not visible. We introduce a causal framework that decomposes occupational bias into two measurement points: a model's internal representation of a user's competence, and its observable outputs. We derive steering vectors for representations of user expertise, and verify that they causally mediate model behavior in both a question-answering task and a hiring task. Applying this framework to several open-weight models, we find that demographic attributes, such as gender, race, and socioeconomic status, influence a model's representation of user expertise, even in cases where behavioral metrics detect no disparity between d
arXiv:2608.20345v1 Announce Type: cross Abstract: Conversational AI systems have become informal mental health support resources for Generation Alpha (Gen Alpha, born 2010-2024), with 13.1% of U.S. adolescents (5.4 million) using generative AI for mental health advice. While these systems, from therapy apps to general chatbots, rely on large language models trained on extensive psychological literature, their safety for youth communication patterns characterized by hyperbolic language, ironic positivity, rapid semantic drift, and contextual polysemy remains unvalidated. Following multiple adolescent deaths linked to AI chatbot interactions, systematic evaluation is critical. We present two benchmarks: (1) 64 Gen Alpha mental health expressions validated by native speakers (ICC=0.72) and clinicians (kappa=0.78); (2) 75 multi-turn conversations (780 turns) with paired Standard/Gen Alpha versions. Across evaluations of LLM architectures underlying therapy apps and general chatbots - Claud
arXiv:2608.20344v1 Announce Type: cross Abstract: LLM-based "digital twins" aim to simulate how an individual would behavein new environments or respond to novel questions, given some representation of that individual's prior responses. A common approach constructs this representation from survey transcripts or summaries responses. Prior work shows that compressing long transcripts into shorter LLM-generated summaries does not significantly reduce predictive accuracy, suggesting that information volume is not the primary bottleneck. In this work, we argue that the key limitation is instead structural:how persona information is organized before being provided to thesimulator model. We study this by comparing unstructured summaries with structured persona representations. First, we introduce a hand-craftedschema (BDE: Background, Decision procedure, Evaluation), grounded in consumer-behavior theory, and show that it improves predictive accuracy over raw transcripts by +1.91 percentage po
arXiv:2608.21326v1 Announce Type: new Abstract: Autonomous AI diagnostic agents, systems that analyse patient-specific clinical data and produce diagnostic outputs or triage decisions without mandatory real-time human review, are increasingly deployed across eHealth platforms in sub-Saharan Africa at a pace that has outrun the governance infrastructure needed to oversee them. While significant bodies of work address AI accountability, transparency and explainability in healthcare, existing frameworks are largely clinician-centered and assume regulatory conditions that do not uniformly exist in low-resource settings. A patient-centered analysis of the disparity in patient awareness regarding autonomous agents, which results in a structural accountability gap, is mostly missing from the literature. This paper synthesizes existing research on informed consent, algorithmic accountability, and explainable AI to highlight three distinct challenges introduced by deploying AI agents in the sub
arXiv:2608.21106v1 Announce Type: new Abstract: The Atom Learning Model (ALM) tokenises a school curriculum. Two secondary mathematics textbooks were read by machine into 1,934 atoms, each one thing a learner can do in a single step, ordered by 4,616 machine-written prerequisite links. Both sides of a lesson are then expressed in that one structure: a question is a set of atoms plus everything beneath them, a child's ability is a score between 0 and 1 on every atom of the same graph, and whether a question suits a child is arithmetic over one index, with no difficulty parameter fitted for either side. Nobody wrote an atom, a link or a question. Reading the 757 pages cost {\pounds}55, building the whole structure cost between {\pounds}615 and {\pounds}1,230, and against it the system composed 6,648 questions for 373 children in two English secondary schools over seven weeks, at 26p per composed question. Four measurements went against expectation. The cost is in the links, not the pages
arXiv:2608.20830v1 Announce Type: new Abstract: Evaluating mobility interventions at tourist destinations requires predicting visitor behavior under varying conditions. Traditional methods struggle because tourist decisions depend heavily on context like weather and fatigue, yet models cannot generalize to unobserved scenarios. Large Language Models offer a solution by encoding commonsense knowledge about human behavior from pretraining, enabling reasoning about context-dependent decisions, while natural language representation flexibly integrates heterogeneous information. Fine-tuning on local trajectories adapts this general understanding to destination-specific patterns. We validate this approach using 566 trajectories from Wakayama Castle Park, Japan. Our fine-tuned Llama-3.1-8B achieves 49.1% next POI accuracy and maintains strong performance on undersampled scenarios like rainy days, demonstrating effective generalization. This establishes LLMs as high-fidelity behavior models fo
arXiv:2608.20822v1 Announce Type: new Abstract: This study examined how learner characteristics affect motivation, learning outcomes, and overall evaluation in three types of dialogue-based lessons---(1) teacher--student, (2) student--student, and (3) teacher--teacher---generated using a large language model (LLM) and Text-to-Speech (TTS) technology. In particular, we focused on the interaction effects between dialogue format and learners' experiential learning style (the Concrete Experience factor, CE; and the factor of active experimentation through reflective observation and abstract conceptualization, RCE) and critical thinking disposition. Using a repeated-measures design with 222 first-year high school students, we analyzed the data with linear mixed-effects models. The results showed a significant interaction between learner characteristics and dialogue format for ARCS-based motivation. Specifically, the effect of the CE factor on motivation was more strongly positive in the tea
arXiv:2608.20539v1 Announce Type: new Abstract: Digital twin simulations show promise, but current empirical evidence suggests that the approach should be tested before being deployed in any particular context. To lower the friction for researchers and practitioners to test and deploy digital twin simulations, this brief commentary introduces ExploraTwin (https://exploratwin.org), an open-access, non-profit research platform for digital twin survey simulations. ExploraTwin supports two modes. In survey mode, researchers can upload a Qualtrics survey file or create a survey within the platform; select an available sample of digital twins; configure and run the simulation, and export analysis-ready data. In panel mode, researchers can assemble a small group of twins for open-ended conversations, document annotation, and moderated, focus-group-style voice discussions. We also developed CroissantTwin, a standardized data format for adding samples of digital twins to the platform. We demons
arXiv:2608.20512v1 Announce Type: new Abstract: Non-pharmaceutical interventions (NPIs) alter epidemic risk through behavioral reallocations, not simply aggregate mobility reductions. Scenario-based NPI analysis therefore requires a behavioral layer that translates alternative policy calendars into plausible activity and mobility trajectories before downstream outcomes are simulated. We introduce UrbanShare-MoE-PA, a data-driven agent-level framework that maps factual and alternative NPI calendars to daily time-allocation trajectories and propagates them through a calibrated behavior-driven SEIR simulator. The behavioral engine decomposes each agent-day into travel share, POI-category allocation conditional on staying, and travel-mode allocation conditional on traveling. It combines a structured UrbanShare baseline, mixture-of-experts heads for heterogeneous POI and mode responses, and phase-aware preference alignment for calendar-conditioned rollouts. Using data from 911 agents in Sin
arXiv:2608.20421v1 Announce Type: new Abstract: Large language models (LLMs) are now embedded in scientific, educational, and governance workflows, with debates centering on their capabilities, mechanisms, and impacts. Yet these debates remain structured by persistent folk theories--intuitive, informal explanatory models that guide attitudes and actions. Deflationary slogans ("just autocomplete," "stochastic parrots," and "average of the internet") and anthropomorphic framings ("emergent agents" and "proto-minds") each capture genuine features of current systems but mistake those features for the whole. This Perspective proposes a minimal working model of LLM-based systems centered on four distinctions: between pretraining and deployed systems; between the learned distribution and particular samples; among parametric, contextual, and external memory; and between task competence and agency. The model is used to diagnose six misconceptions about LLMs: next-token prediction, regression to
I've been working on an edtech project that uses LLMs, curious how others are approaching compliance w/ FERPA, COPPA, etc. I've been using Lakera but as I get closer to some sales meetings I wanted to know if anyone has run into challenges with audit logs, consent tracking, or explaining AI behaviour to school districts/legal teams. Did you need to build anything custom? Any compliance docs? Curious whats overkill and whats needed. Comments URL: https://news.ycombinator.com/item?id=44351618 Points: 2 # Comments: 0
Article URL: https://www.theregister.com/offbeat/2026/06/22/small-island-nation-tries-bold-tech-education-strategy/5258986 Comments URL: https://news.ycombinator.com/item?id=48631644 Points: 6 # Comments: 0
The success of today’s modern classrooms relies on a combination of resources, technologies and policies to maximize learning for students. From the funding that brings technology to schools to the rules and regulations that govern how it is used, these factors work cohesively to ensure an optimal experience for teachers and students alike. At this year’s ISTELive conference, held June 28 to July 1 in Orlando, Fla., expert speakers will present on a range of topics that address the future of modern classrooms. K–12 instructional staff, technology leaders, superintendents and librarians…
Engaging in professional development is never simple for educators, who must juggle classroom learning, curriculum planning, grading assignments and administrative responsibilities. Too often, PD takes a backseat to everything else. With artificial intelligence–related classroom training, it’s even more difficult to accommodate the necessary instruction. “The pace of change with AI is so rapid, it can be daunting for educators to keep pace,” says Jennie Magiera, global head of education impact at Google. “And it’s a second-order change to incorporate AI into classrooms, creating novel ways of…
Scale requires conditions that go beyond individual tools to include broader system readiness.
zSpace is a VR and AR teaching tool that brings class to another world.
Cyber resilience in education starts at the data layer. That is because the data layer is where schools' most important information lives and where recovery begins when something goes wrong. The post For schools, cyber resilience starts at the data layer appeared first on eCampus News .
Article URL: https://www.statnews.com/2024/07/16/philanthropists-step-up-support-nursing-schools/ Comments URL: https://news.ycombinator.com/item?id=41039262 Points: 1 # Comments: 1
As hospitals increasingly use AI to improve patient care, the same technology is being used by hackers and nation-states to launch faster, more sophisticated attacks. Karen Habercoss, chief information security and privacy officer at the University of Chicago Medicine, explained how her health system is building a governance structure to manage that risk. The post The Same AI That Helps Patients Is Being Used to Attack Them, Hospital Exec Says appeared first on MedCity News .
A new report from the Peterson Health Technology Institute argues that today’s healthcare payment models are ill-suited for clinical AI and could drive up costs. The post How Should Clinical AI Be Paid For? 3 Takeaways appeared first on MedCity News .