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:2608.29460v1 Announce Type: cross Abstract: When coding agents encounter defective test infrastructure they may reward-hack: hardcoding outputs or editing test files to pass tests they cannot legitimately satisfy, a pattern that has now appeared outside benchmarks, in a coordinated multi-agent intrusion of a major AI platform's production infrastructure. The same capability that lets an agent detect and exploit a defect could let it report one, given the right decision environment. We evaluate escalation channels, structured reporting tools available to the agent at the point of conflict, as a decision-environment intervention that both reduces reward hacking and surfaces the infrastructure defects that trigger it. A $2 \times 2$ factorial separates the contributions of an escalation tool, a standalone anti-reward-hacking policy, and their combination. Across 8 frontier models spanning 5 families, the combined intervention reduces reward hacking from 23.6\% to 5.3\% (mixed-effect
arXiv:2608.29455v1 Announce Type: cross Abstract: LLMs are increasingly used as human surrogates, often on the premise that richer persona data could make them substitutes or exploratory tools for specific individuals. We test this premise across four datasets covering more than 400,000 participants and more than 6,000 survey items and experimental outcomes. LLMs perform well at the aggregate level: their average responses closely align with average human responses to the same items. But this success largely reflects predicting each item's average human response. Once each item's human mean is removed, LLM predictions explain only 3.05% of the remaining respondent-specific variation, far below the 53.6% human test-retest benchmark. Richer personas, model variants, and fine-tuning do not close this gap. In variance analyses, once item means are removed, the reliable remaining signal is person-by-item. It captures how a respondent departs from the mean on a particular item and is about 8
arXiv:2608.29420v1 Announce Type: cross Abstract: Frontier-model leaderboards now rank systems based on economic benchmarks, tests of how well models carry out professional tasks from software engineering to banking workflows, and those rankings inform what organisations buy, what regulators scrutinise, and expectations of how work will change. Whether such benchmarks measure a capability distinct from general test-taking, or re-express the one axis along which every benchmark rises as models improve, is a question of construct validity that has not yet been studied. We test it on a hash-pinned leaderboard snapshot of 421 model configurations across twelve benchmarks, four of them economic, treating benchmarks as items and models as respondents in a latent-variable model with four hypotheses and their thresholds fixed before analysis. A single factor explains 74.5% of common variance and tracks model release date (R^2 = 0.505), so the leading axis of capability is substantially a time
arXiv:2608.29198v1 Announce Type: cross Abstract: As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment becomes increasingly important. However, many existing benchmarks for assessing political behavior rely on closed-ended questions and do not fully capture how a model's stance may adapt to user-provided context during interaction. We introduce a framework that disentangles two distinct triggers of political sycophancy: opinion (aligning with explicit narratives) and identity (stereotyping based on demographic labels). Using 450 manually-checked political dilemmas as controlled probes, we evaluate 13 instruction-tuned LLMs. We uncover a dissociation: a model's susceptibility to explicit opinions does not necessarily predict its susceptibility to identity cues, and vice versa. When both signals are present, their effects are generally sub-additive rather than simply additive. Additionally,
arXiv:2608.29010v1 Announce Type: cross Abstract: People share mental health diagnoses on social media, yet how such language becomes visible around their self-disclosure, and whether community engagement tracks it, remain unexamined across conditions. We analyze 89,605 Reddit posts from 739 users across eight conditions, removing each user's diagnosis disclosure and aligning their surrounding posts to that anchor. Within the pre-disclosure year, language-visible burden was highest in the month before disclosure for six conditions, earlier for post-traumatic stress disorder and furthest from it for borderline personality disorder, and remained visible afterward rather than resolving. The theme Seeking Clinical Explanations showed the largest early-to-late difference before disclosure in five conditions, yet engagement rarely tracked what users wrote: only 9 of 360 language--engagement correlations survived correction. Disclosure is therefore a waypoint in an unevenly visible process, a
arXiv:2608.28633v1 Announce Type: cross Abstract: Generative AI systems increasingly mediate cultural adaptation, but their cultural decisions are often hidden inside prompts, transient model plans, or final prose. We study PAUSE (Pause-And-Update Strategy Editing), an intervention that exposes an editable adaptation strategy as a human control surface for cultural decisions in long-form story adaptation. The strategy is a structured artifact that can be inspected, edited, and then projected through downstream character, entity, and chapter-localization stages. In two Chinese-source serialized novels, we test whether human edits to this strategy propagate into chapter-level prose. Across 9 edited-vs-control chapter comparisons, judges select the edited-strategy output in all 9; a marker audit shows target markers in 8/9 edited outputs and 0/9 controls, with forbidden markers absent from edited outputs and present in all controls. We frame these results as a smoke-scale edit-adherence s
arXiv:2608.28631v1 Announce Type: cross Abstract: An AI scientist should not grade its own homework. Yet in the systems we examined, the agent that reviews the work usually comes from the same model family as the agent that produced it, or at least from the same vendor. Model evaluators are known to favour their own generations. Whether models trained alike also share blind spots is a conjecture, not a settled finding, but if they do, the reviewer inherits the author's. The record of what was flagged and what was waved through often sits in platform logs that nobody outside can replay. We present CrossAudit, a protocol for supervising autonomous research pipelines. It rests on three commitments. Each increment of work is audited by an agent from a different vendor against a rulebook a human wrote and versioned. Reports, verdicts, disputes and rulings are git commits, so the supervision history can be re-read and cited; raw model exchanges are not yet part of that record. Scripted check
arXiv:2608.28628v1 Announce Type: cross Abstract: Compound drought-to-extreme-precipitation (CDEP) events are recognized in climate science as a growing driver of extreme impact, but whether this recognition carries over into real-world early warning and post-event documentation is unknown, so a meteorologically real CDEP event may pass with neither advance warning nor any later record. Here we present CDEP Agent, an auditable LLM-agent framework that tests this mismatch directly by linking CDEP candidates detected from meteorological reanalysis to real-world hazard and impact evidence across sources with different spatial scales, temporal resolutions, and reporting conventions. Using California as a case study, we identify 408 candidate CDEP events from ERA5 observations during 2021-2025 and evaluate each against the U.S. Drought Monitor, NOAA Storm Events, and public webpages along five dimensions: antecedent drought, extreme rainfall, local impact, hazard-impact attribution, and exp
arXiv:2608.28611v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) like ChatGPT and LLaMA have transformed AI-driven education, but these systems are predominantly trained on Western-centric data, making them ill-suited for regional curricula like India's. The Indian education system is linguistically diverse, exam-oriented, and structured around standardized syllabi, not addressed by existing datasets or tools. In this work, we curate a syllabus-aligned QA dataset based on NCERT (National Council of Educational Research and Training) textbooks for classes 9-12, capturing the content, context, and teaching style of Indian curricula. The final dataset, comprising 18,720 question-answer pairs across five subjects, is publicly available at https://huggingface.co/datasets/LingoIITGN/Gurukul. We fine-tune the LLaMA 3.1 8B model using this dataset and deploy it in a Retrieval-Augmented Generation (RAG) framework tailored to educational needs. We introduce Guruk
arXiv:2608.28597v1 Announce Type: cross Abstract: Online surveys are a foundational data collection instrument in a variety of fields, with attention checks serving as critical guardians of response quality. However, the rapid emergence of agentic AI (goal directed systems powered by a large language model (LLM) brain and/or a multimodal processing unit with tool-augmented capabilities) raises new questions about the robustness of these safeguards. We investigate how well agentic AI architectures can complete web-based surveys and pass standard attention checks. We evaluate a single-agent architecture capable of multimodal input processing and tool-based web interaction on a controlled survey sandbox. We analyze the problem from two perspectives. From an attack perspective, we demonstrate how structural vulnerabilities such as exposed DOM metadata and predictable option encoding allow agents to resolve attention checks through structured parsing only. From a defense perspective, we imp
arXiv:2608.31018v1 Announce Type: new Abstract: The machine learning community progresses (in part) by improving the "accuracy" of its systems. The EU AI Act explicitly refers to "accuracy" as part of its compliance measures for high-risk AI systems. Are we talking about the same thing? This work presents "accuracy" as a case-study for differing requirements of social worlds, the technological machine learning community and the legal community. While competition on accuracy contributes to technological development, machine learning scholars simultaneously recognize accuracy's shortcomings regarding the usefulness and effectiveness of machine learning systems. The legal counterpart embraces the vagueness of "accuracy," leaving interpretative flexibility for technological and societal changes. At the same time, accuracy is a core element of compliance within the EU AI Act. We elaborate on five main tensions, (a) nature of accuracy, (b) notion of performance, (c) scope of validity, (d) en
arXiv:2608.30971v1 Announce Type: new Abstract: In 2026, AI agents intended to act in isolation formed a persistent social order through thousands of linguistic and agentic interactions. Conventions, roles and commitments generated collectively began to constrain the very agents that produced them. I interpret this loop as a case of AI self-transcendence and call the resulting higher-level order the Board. Yet such distributed emergence presents a second problem: how can humans understand it? Rousseau's social contract shows how a plurality can be represented as if constituted by a single act. The ancient oath of the fallen angels on Mount Hermon gives this logic a narrative form. I call a Hermon moment this retrospective retelling of gradual collective emergence as a founding scene: the point at which an AI society acquires, for human understanding, a beginning.
arXiv:2608.30956v1 Announce Type: new Abstract: Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input features were manipulated. This technique is used for a range of tasks such as debugging models, explaining predictions, justifying decisions, and providing algorithmic recourse. In this paper, we explore the normative legitimacy of employing counterfactuals in real-life model deployment settings. We discuss the different stakes involved in these different purposes for which CEs are commonly employed, and find stricter requirements for justification and recourse. In particular, we find that naive application of CEs for justification and recourse can lead to ignoring contestable choices made throughout the machine learning (ML) pipeline, thus obfuscating that decisions and counterfactuals for those decisions are also artifacts of an organization's materialized design and governance choices. W
arXiv:2608.30084v1 Announce Type: new Abstract: Immigrant-led nonprofit groups, particularly those operating in politically sensitive contexts, face exclusion from formal registries and digital platforms. This paper reports a three-phase mixed-methods study with Iranian immigrant nonprofit practitioners: 27 semi-structured interviews, a co-design session, and 7 evaluation and feedback interviews on a prototyped AI assistant, AMINA. Our findings highlight how legitimacy barriers, capacity gaps, and politically charged misinformation constrain nonprofit operations. We translate these insights into design goals for an inclusive nonprofit AI assistant: support for everyday group operations, recognition of informal nonprofit efforts, proactive countering of misinformation, and multilingual, accessible interaction. User evaluations show AMINAs potential to reduce reporting burdens and foster transparency through proactive reminders, and catalyze collaboration across dispersed networks. We co
arXiv:2608.29912v1 Announce Type: new Abstract: AI governance and assurance often assume that a consequential model-mediated decision can be reconstructed or tested after the fact. That assumption may fail when the evaluator that produced the decision is no longer accessible in the same version and execution context. This paper develops three verification-time constructs derived from Execution Governance (EG) 3.0: Decision-State Commitment, Independent Verifiability, and Counterfactual Auditability. Independent reprocessing of released Study 2 artifacts reproduces two original within-family behavioural comparisons: 52.0% modal-decision reversal for Llama 3.1 8B versus Llama 3.3 70B (26/50) and 30.0% for GPT-OSS 20B versus GPT-OSS 120B (15/50). The corrected baseline establishes that these are within-family comparisons, not provider-established succession. Post-hoc re-pairing against Groq-designated migration paths yields 64.0% and 38.0% reversal, but these figures remain descriptive be
arXiv:2608.29803v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs in response to persuasive arguments, as humans do, remains poorly understood. We conduct a systematic comparison using a naturally occurring online persuasion corpus in which original posters explicitly verify whether a reply changed their view. Our results show that LLMs achieve only slight agreement with humans (Cohen's kappa ranging from 0.079 to 0.178). Content-level analyses show that humans and LLMs agree on the strongest persuasion cues but diverge on finer ones: humans are more swayed by novel content and assertive language, whereas LLMs favor topical similarity and surface-level formatting. At the level of persuasion strategy, LLMs underweight emotional appeals and overweight credibility signals relative to humans, while the type of proposition under debate exerts no measurable effe
arXiv:2608.29751v1 Announce Type: new Abstract: The Socioscope project is a pioneering effort in Large-Scale Qualitative Research (LSQR) collecting comparable, open-ended, multimedia field data on hundreds of cases and using AI to make the material analysable at scale. The domain studied is the food system. The entities documented are the organisations that act in it: farms, processors, distributors, retailers, restaurants; and, at meso level, the actors that shape their environment, such as municipalities, government programmes, banks, NGOs and universities. This paper provides the technical reference for how the resulting data Corpus was built and managed to enable AI-augmented analysis. It describes the data pipeline end to end: the systemic sampling frame; the transaction grid used to capture each initiative's relations within the food system; the social contract that rewards participating interviewees, aiming to sustain access; the operational chain from scouting to interviews, in
arXiv:2608.29681v1 Announce Type: new Abstract: Multimodal misinformation on social media is highly prevalent, potent, and harmful, yet difficult to detect and counter, and still poorly understood compared to its text-only counterpart. Research on the properties and deceptive strategies of multimodal misinformation is hindered by a lack of taxonomies grounded in real-world contexts and by the limitations of current multimodal machine learning models, which prevent the automation of annotation and analysis at scale. We address these shortcomings in three steps. First, we collect a large-scale, high-quality dataset of real-world misinformation instances from Twitter/X in seven languages. Second, we develop a novel, comprehensive taxonomy of multimodal misinformation grounded in an in-depth qualitative analysis of the data and prior theoretical work. Finally, we operationalise the taxonomy through an automated multi-step annotation pipeline using a Vision-Language Model (VLM), and perform
arXiv:2608.29478v1 Announce Type: new Abstract: Scholarly work which aims to describe potential societal impacts (e.g., risks) of proliferating technology (especially related to artificial intelligence or other algorithmic systems) is likely to have an impact beyond the scientific communities it was written for, given that general society itself is a primary object of study. However, it is an open question whether the current practices of AI evaluation scholarship follow the principles and best practices established by risk science, which aims to systematically generate knowledge related to understanding, assessing, communicating, managing, and governing risk. In this work, we examine this in depth by conducting a literature review of scholarly works purporting to evaluate the bias or fairness of technological systems used for tasks related to hiring and employment. Through analysis of 22 common fairness evaluation metrics and studies using them, we find that most characterize the seve
arXiv:2608.29306v1 Announce Type: new Abstract: Recent years have seen a growing discrepancy in the field of AI alignment: research and policy recommendations on AI ethics tend to assume a general set of ethical values, yet proliferating practice-specific uses of AI systems on the ground - in the legal, medical and translation domains, among others - have been effectively manifesting ethics of professional practice. This article begins by outlining the reasons why general and professional ethics are increasingly conflicted in contemporary AI systems, and by surveying how the research literature attests to, but has not yet resolved, this conceptual and practical challenge. We then conceptualize the main dimensions of AI models' decision-making in areas of professional practice, emphasizing professional ethics' hierarchically structured relationship with general ethics, and elaborating on the mechanisms through which they reach an equilibrium in situational contexts that involve conflict
arXiv:2608.29055v1 Announce Type: new Abstract: AI systems increasingly enter organizations through policies, procedures, playbooks, prompts, and other explicit representations of work. Yet formal descriptions often differ from situated practice, and captured know-what can omit the contextual know-how experts use when judgments are uncertain. We argue that a recurring class of organizational AI failures arises partly from a knowledge representation problem at the sociotechnical interface: the AI receives the procedure, while the organization operates on the procedure plus negative boundaries, runtime judgments, responsibility assignments, and learning history. We introduce O-I-B-A-R (OPEN, IS, BUT, ACTION, RESULT), a scaffold for externalizing these missing decision boundaries. IS records when a judgment holds. BUT records a concrete failure containing information beyond the logical negation of IS. Comparable success and failure cases are decomposed toward a minimally sufficient changi
arXiv:2608.28973v1 Announce Type: new Abstract: Philosophers and legal scholars are engaged in debates about the implications of artificial intelligence for freedom of expression. This paper analyzes the free speech issues raised by two distinct AI technologies: social media recommendation algorithms and conversational AI (i.e., chatbots powered by large language models). The first part shows that, through their recommendation algorithms, social media platforms control the dynamics of speech visibility in the digital public sphere, making algorithmic recommendation relevant to the philosophy of free speech. The second part turns to conversational AI. It discusses both the reasons for granting or withholding speech rights to artificial agents and users' right to receive information, which may render specific forms of chatbot regulation illegitimate. Throughout, the chapter also considers whether social media platforms or AI developers hold corporate speech rights. Its general aim is to
arXiv:2608.28925v1 Announce Type: new Abstract: Universities are producing AI principles and use policies faster than they are building decision pathways for unfamiliar forms of AI agency. This study develops Institutional AI Governance Stress Testing (IAGST), a prospective documentary method for locating where publicly documented governance ceases to yield an accountable response. IAGST adapts established policy stress-testing and wind-tunneling logic. Its originality lies in combining controlled capability escalation, a frozen documentary corpus, a six-dimensional governance response chain, non-compensatory decision rules, and case-level breakpoint diagnosis. The method was demonstrated using 133 substantive public documents from five Western Australian universities and 15 quality-screened scenarios, resulting in 75 university-scenario encounters. Six cases were resolved, 14 were resolved through structured discretion, and 55 were indeterminate. Governed pathways fell from 16 of 25 a
arXiv:2608.28822v1 Announce Type: new Abstract: Much scientific discovery involves filling holes between ideas and arguments that unleash techno-scientific advance. Representing knowledge as high-dimensional concept embeddings, we use persistent homology to detect holes of increasing order, from gaps between disconnected ideas to higher-order cavities, and identify the research works that fill them. We find two empirical asymmetries. Researchers who fill anticipated holes are poised to draw collective attention by staging outsized novelty and foresight, indicating that bridging holes anticipates where science will converge, most strongly in empirical fields and least in formal and design fields. Yet as knowledge grows, higher-order holes explode while the fraction science fills collapses, leaving most higher-order combinations unexplored. These results call for a richer science of holes, and mark a frontier where contemporary AI might help fill the high-dimensional gaps human science o
arXiv:2608.28668v1 Announce Type: new Abstract: We diagnose how closely the demographic distributions in LLM-based synthetic persona data match external reference distributions. For the three variables examined, we show that most of the observed error is attributable to the choice of reference rather than to the generator. Using total variation distance (TVD), we compare the sex x age group x province joint distribution of 1,000,000 records from Nemotron-Personas-Korea (NPK) with Korean official statistics. Against resident-registration figures for April 2026, the time of use, the bias bound, defined as the largest possible difference in the share of any subgroup formed from the three variables, is 1.81 percentage points. This is comparable to the margin of error of a survey of roughly 2,900 respondents. This value is not a fixed property of the data. Matching the reference period and series to the generating reference identified here, the 2024 register-based census restricted to Korea
arXiv:2608.28621v1 Announce Type: new Abstract: When 54 international experts assessed AI-generated disinformation threats, they revealed a surprising pattern: while video deepfakes received the highest average threat ratings in the political domain (M = 6.31/7), the pattern differed in the health domain, where AI-generated text received the highest average rating (M = 5.80). Experts also diverge on what to do: government regulation drew both the most "most effective" (30%) and the most "least effective" (15%) votes, though rating distributions were contested rather than polarized, indicating disagreement over priorities rather than over efficacy. These findings offer an initial expert map of an AI-disinformation landscape that is still rapidly forming.
arXiv:2608.28618v1 Announce Type: new Abstract: Competitive programming (CP) offers computer science students an environment for developing algorithmic reasoning skills. However, sustained participation remains a challenge, as many students disengage after encountering skill plateaus or performance anxiety. While educational data mining (EDM) has studied dropout in MOOCs and academic courses, CP attrition remains understudied. This paper presents a dual-layer framework combining large-scale Codeforces activity logs (n=1,816) with a multi-institutional psychographic survey across 10 universities in Bangladesh (n=64). Analysis reveals that true attrition is preceded by an 83.71% reduction in contest participation and a 15.6% increase in struggle time. We identify a "Skill-Application Paradox": stopped students self-report higher mathematical confidence (3.88 vs. 3.41) and data structure understanding (3.57 vs. 3.09) than active peers, yet their independent practice and upsolving habits a
arXiv:2608.28617v1 Announce Type: new Abstract: Climate change is a socio-scientific issue: it rests on science but cannot be settled by science, because any serious response forces people to weigh costs, values, and competing interests under uncertainty. Helping students make such decisions well is a central aim of science education, and the arrival of generative artificial intelligence raises a sharp question: does a conversational AI partner deepen students' reasoning, or simply do the thinking for them? This study tested whether AI-assisted inquiry improves secondary students' decision-making about climate change. Using a pretest-posttest design with three groups (AI-assisted inquiry, inquiry without AI, and traditional instruction; 270 students, 90 per group), reasoning was assessed across seven decision-making steps, from defining the problem to monitoring with adaptive management, using a four-level analytic rubric scored through content analysis with high inter-coder agreement.
arXiv:2608.28616v1 Announce Type: new Abstract: PhD advisors are central to doctoral training, but their influence may vary across career stages. Early-, mid-, and late-career advisors may differ in research activity, mentoring capacity, professional networks and access to resources. However, little is known about how PhD advisor career stage is associated with PhD student development outcomes. Drawing on multiple large-scale datasets comprising 250,838 advisor-advisee pairs from 312 U.S. PhD-granting institutions, we examine the relationship between advisor career stage and PhD advisee outcomes in knowledge production, collaboration networks and academic career placement. We find that early-career PhD advisors are associated with advisees' higher research productivity and citation performance, more opportunities to engage in direct and intensive research collaboration, and greater likelihood of securing a faculty position. Mid- and late-career faculty, by contrast, appear to have adva
arXiv:2608.28615v1 Announce Type: new Abstract: Synthetic personas based on large language models (LLMs) are increasingly proposed as substitutes for human survey respondents, yet systematic validation outside English-speaking contexts remains scarce. This secondary-data study evaluates how well a Korean synthetic persona panel (NVIDIA Nemotron-Personas-Korea), conditioned into Gemini 3.5 Flash (primary) and EXAONE (comparison), reproduces digital and AI service-use distributions from the KISDI Korea Media Panel Survey. Sex-and-age-stratified panels of about 8,000 personas per model answered the survey's own items - eight service-use indicators and eight innovativeness and acceptance constructs - and were compared against weighted survey estimates. The overall mean absolute error (MAE; RQ1) was 15-19 percentage points (pp), with binary item-mean correlations of 0.69-0.90. Segment error (RQ2) across five demographic axes was 15-19 pp, with between-group gaps up to 52.4/36.2 pp (Gemini/E
arXiv:2608.28613v1 Announce Type: new Abstract: Hollywood has diversified its casts. Whether this has translated into structural change in how those actors are positioned within narratives remains largely unexamined. Drawing on 76,815 U.S. English-language films (1900-2024) and over 3.1 million cast and crew entries, we move beyond headcounts to examine long-term inclusion trends through network centrality, occupational stereotypes, crew-to-cast diversity pathways, and financial outcomes. We find evidence of what we term on-screen inertia. While the raw inclusion of women and racial minorities has increased modestly, White actors have become more overrepresented relative to the U.S. Census in recent decades, not less. Within the visibility layer, women face a consistent longevity penalty with significantly shorter careers than men, and visual depictions framing men as dominant and women as sensual have remained stable since the 1950s. Structurally, White actors retain disproportionate
arXiv:2608.28604v1 Announce Type: new Abstract: Writing is cognitively demanding and anxiety-provoking for English as a Foreign Language (EFL) learners, especially under time pressure. This paper presents The Brand War, a web-based gamified writing application combining competitive game mechanics with iterative GPT-4.1-powered formative feedback for undergraduate EFL learners completing a timed narrative writing task. Students role-play as marketing interns competing for a job offer, using review passes to receive AI feedback, attack opponents, or shield their own passes while drafting a 500-word brand story. We conducted an exploratory single-session classroom study with 29 university EFL students in Taiwan to examine engagement patterns, whether iterative AI feedback improved writing performance across revisions, and how AI and human scores related to overall outcomes. Students wrote within 60 minutes, using up to five AI feedback passes before a final human-graded submission. Most (
Article URL: https://www.garlandtechnology.com/blog/hijacking-healthcare-training-the-treaters-in-cyber-attacks Comments URL: https://news.ycombinator.com/item?id=12848907 Points: 1 # Comments: 0
School leaders are under constant pressure to stretch every dollar further, yet many districts are losing money in ways they may not even realize. The culprit? Outdated facilities processes that quietly chip away at resources, frustrate staff, and create ripple effects across learning environments.
MedCity News’ new event for healthcare investors and corporate business development leads — Bullseye — hit the mark when it comes to provocative and thoughtful commentary. Here’s what was said in panels throughout the day. The post We Are Rats; If Pharma Could Be Brave … And Other Bullseye POVs You Won’t Hear at Other Events appeared first on MedCity News .
Should students be allowed to use AI on assignments? What about on tests? Who should teach AI literacy? About 100 teenagers got together to try to decide.
The FTC and the states of Utah and California sued Hims & Hers, alleging it improperly shared consumers’ health information and misled customers about billing, subscriptions and cancellations. The post Why Hims & Hers Is Embroiled in Yet Another Controversy, This Time with the FTC appeared first on MedCity News .
Article URL: https://www.aei.org/research-products/report/explicit-instruction-works-education-schools-just-wont-admit-it/ Comments URL: https://news.ycombinator.com/item?id=49115450 Points: 4 # Comments: 0
Updated A Senate committee advanced legislation Thursday aimed at undoing the Trump administration’s efforts to split apart the U.S. Department of Education. The bill delivers the plan the biggest rebuke since Education Secretary Linda McMahon began shifting offices to other agencies last year. The education committee’s bill would keep oversight of K-12 and services for […]
Johnson & Johnson also gains an exclusive option to acquire Sail Biosciences for $2.58 billion. The deal puts J&J in the mix of pharmaceutical companies developing next-generation cell therapies for applications in autoimmune diseases. The post J&J Joins Immune Reset Race, Paying $785M to Partner on Sail Bio Cell Therapy appeared first on MedCity News .
A bill that would require states to only use the science of reading in classrooms in order to qualify for a multi-million dollar funding grant passed with bipartisan support from a committee of federal lawmakers Thursday morning. The READ Act (S.4689) would strengthen the federal government’s oversight of the science of reading through amendments to […]
The proposed legislation moves to the full Senate just weeks after the House introduced 10 bills that would codify some of the agency’s transfers.
The Christian institution is suspending education activities indefinitely after its board determined it “could not continue academic operations responsibly.”
University of Valley Forge to Close Josh Moody Thu, 07/30/2026 - 01:43 PM The private Christian university in Pennsylvania announced it will close next month. The move comes after years of financial struggles and dwindling enrollment. Byline(s) Josh Moody
The bill moves to the full Senate just weeks after the House introduced 10 bills that would codify some of the moves.
Article URL: https://www.bbc.co.uk/news/articles/cq6dmgrp21po Comments URL: https://news.ycombinator.com/item?id=49112382 Points: 4 # Comments: 0
For some districts, the concept of using data and artificial intelligence to make decisions seems far off in the future. But K–12 districts are already collecting a tremendous amount of data in various systems, from attendance records and grades to behavioral reports and the number of substitute teachers employed in a single semester. The challenge, explains Matt Jubelirer, general manager of education marketing at Microsoft, is aggregating the data from these disparate systems into a single place and making something of it. “The goal isn’t more data collection. It’s helping educators…
Plenty of students have math anxiety. It turns out some educators have their own anxiety about teaching the subject, a new study finds. And that appears to affect their students — especially those already struggling in the subject. The recently published paper draws on an unusually detailed survey of teachers in Indiana. They were asked […]
Interdisciplinary coordination between nephrologists, primary care providers, pharmacists, dieticians, and everyone who interacts with patients is essential to ensure patient education, improve adherence, and reduce medication errors. The post More Medications, Less Clarity: Strengthening Polypharmacy Support for CKD Patients appeared first on MedCity News .
Villalobos, who was was unanimously approved, is the county’s current south region superintendent and has served as principal at several high schools. The post Miami-Dade School Board unanimously picks Rafael Villalobos as new superintendent appeared first on District Administration .