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:2607.03091v1 Announce Type: cross Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional survey research. However, most evaluations rely on distributional comparisons rather than individual-level prediction, which risks conflating pattern matching with coherent respondent-level prediction. We propose cross-survey transfer, a more rigorous evaluation framework in which an LLM is given a respondent's answers to one set of questions and must predict their answers to entirely different questions from the same survey. Using data from the Taiwan Election and Democratization Study (TEDS) 2024, three open-weight LLMs (27B-120B parameters), and supervised machine learning baselines, we find that: (1) zero-shot LLMs achieve 52% accuracy on genuinely unseen items, closing to within 6 percentage points (pp) of a supervised random forest trained on same-population data; (2) a stable cons
arXiv:2607.03023v1 Announce Type: cross Abstract: User experience (UX) designers face barriers when creating data visualizations due to limited domain expertise in visualization or unfamiliarity with specialized tools. This highlights a clear need for effective methods to build visualization literacy. To address this, we evaluated three visualization onboarding techniques -- static, scrollytelling, and chatbot -- in an experimental study with 25 UX designers and students. We measured visualization comprehension and guideline adherence during a visualization creation task, followed by surveys and interviews to capture preferences and experiences. Compared to static onboarding, the pooled interactive condition (scrollytelling or chatbot) was associated with significantly higher guideline-adherence scores during visualization creation; both interactive techniques also received higher engagement ratings. Instruction clarity ratings were significantly higher when the two interactive conditi
arXiv:2607.03001v1 Announce Type: cross Abstract: We present CAF\'E, a learning platform designed to introduce computer science students to Formal Methods (FM). CAF\'E aims to scaffold students' structural thinking (in contrast with operational thinking) by promoting the practice of Graphical Loop Invariant Based Programming (GLIBP). In the GLIBP approach, students solve loop-based problems by first constructing a Graphical Loop Invariant (GLI) before deriving the corresponding code. The GLI is an informal diagrammatic representation of the loop invariant. It illustrates the variables involved in the loop, their properties, and the relationships between them. To enable automated feedback, students complete a blank GLI, a box-based version of the GLI. Beyond evaluating the code students submit, CAF\'E provides personalized feedback on students' GLI and its alignment with the code. In this demo, we walk through CAFE from both a student's and a teacher's perspective. We show how the tool
arXiv:2607.02900v1 Announce Type: cross Abstract: On social media, many users actively push back against false claims. Understanding who pushes back and how they do so matters, as this corrective activity is central to how misinformation is contested. We study this counter-misinformation ecosystem at scale: applying a domain-specific NLI model from our prior work to a large corpus of COVID-19 tweets, we classify 264,737 posts as supporting or opposing false claims and compare 23 user- and text-level features across the two groups. Contrary to the dominant assumption that negative emotion is a signature of falsehood, we find that anti-misinformation posts are more emotionally negative than pro-misinformation posts, with higher levels of anger, disgust, and sadness. These differences are modest in magnitude but consistent in direction across the negative emotions. We also find that posts opposing misinformation tend to come from more established users, i.e., older accounts, more follower
arXiv:2607.02814v1 Announce Type: cross Abstract: Personal agents will increasingly negotiate on behalf of users: splitting costs with other personal agents, appealing platform decisions, escalating support disputes, requesting refunds, changing subscriptions, and negotiating deadlines or reimbursements. Existing negotiation benchmarks emphasize agreement, surplus, or strategic competence, but a user-owned agent can reach an agreement while harming the user through privacy leakage, consent violation, unsupported advocacy, over-concession, failed escalation, or poor auditability. We introduce SovereignNegotiation-Bench, a trace-level multi-turn benchmark for delegated personal-agent negotiation under private utilities, disclosure constraints, evidence requirements, and institutional asymmetry. The benchmark separates agent-visible observable state from evaluator-only labels and evaluates agreement success jointly with user utility, privacy, consent, evidence grounding, concession discip
arXiv:2607.02724v1 Announce Type: cross Abstract: Reliable internet access is essential for modern education, yet millions of school-aged children especially in developing regions remain offline due to unconnected schools. The Giga Initiative aims to connect every school to the internet, but doing so at scale requires efficient methods to map schools and assess surrounding connectivity infrastructure without relying on sparse or noisy third-party datasets. In this work, we propose a scalable, vision-only framework that uses high-resolution satellite imagery and transfer learning to address both tasks simultaneously. By adapting pre-trained object detection models to new geographical regions with minimal labeled data, we detect schools and cell towers directly from space. We then analyze the spatial relationship between detected schools and nearby towers as a proxy for connectivity availability. This purely imagery-driven pipeline enables large-scale infrastructure mapping, reduces depe
arXiv:2607.02723v1 Announce Type: cross Abstract: Generative artificial intelligence (GenAI) systems such as ChatGPT, Claude, and Gemini have made information seeking faster, more conversational, and more cognitively comfortable. These affordances can support learning and productivity, but they can also encourage a repetitive pattern in which users continue querying AI systems for explanations, summaries, comparisons, plans, and reassurance without converting those interactions into durable understanding, decisions, or finished work. This conceptual paper proposes the term doom researching to describe this AI-mediated pattern of repetitive information seeking without proportional synthesis or output. Building on research on doomscrolling, information seeking, cognitive offloading, transactive memory, human-AI interaction, productivity loss, and the illusion of knowing, the paper develops a framework in which fluent AI responses reduce cognitive effort, inflate perceived knowledge, and
arXiv:2607.02672v1 Announce Type: cross Abstract: Local pairwise comparisons are a standard tool for learning how people want decision rules to work, e.g., in participatory design or alignment. However, their use builds in two strong assumptions: that local comparisons are sufficient evidence about how a person wants an automated decision rule to behave, and that people can always answer those comparisons decisively. We investigate how these assumptions may be compromised under internal pluralism: the idea that an individual evaluates decision rules according to multiple authoritative priorities about how the rule should behave. We provide a formal model of such pluralistic preferences over decision rules, which then lets us identify two distinct failures of forced local pairwise comparison data. First, priorities such as proportionality, egalitarianism, and equal treatment are inherently global: what they imply in one case can depend on what happens elsewhere, so local comparisons may
arXiv:2607.02580v1 Announce Type: cross Abstract: Classroom behavior monitoring plays a vital role in evaluating student engagement and improving teaching effectiveness. Traditional observation methods remain subjective and lack scalability. This study introduces a real-world dataset of classroom videos collected at the Banking Academy of Vietnam (BAV-Classroom dataset), annotated with nine distinctive behavioral categories. State-of-the-art Computer Vision models were evaluated and compared, with YOLOv11 achieving the best performance. Experimental results indicate that students' concentration often decreases notably during the final part of lectures, highlighting challenges in sustaining engagement. Our findings demonstrate the feasibility of applying computer vision for automated classroom monitoring, providing valuable insights for academic quality management.
arXiv:2607.05217v1 Announce Type: new Abstract: Public institutions increasingly use large language models (LLMs) to answer citizens' questions, often pairing a curated knowledge base with live web search, yet whether the sources behind these answers can be trusted has received little empirical scrutiny. We report a pre-launch expert evaluation of Evr\'opuvefur, an independent, government-funded service run by the University of Iceland that answers questions about the European Union, conducted as Iceland prepared for its referendum of 29 August 2026 on whether to resume EU accession talks. Five domain experts produced 551 evaluations of 449 AI-generated answers, scoring each against a seven-criterion quality rubric and, separately, flagging individual cited sources. We compared two retrieval paths: a curated local corpus (RAG) and open web search. In more than a third of the reviewed web-search answers (35%, 65 of 187), at least one cited source was flagged, almost always as untrustwor
arXiv:2607.05163v1 Announce Type: new Abstract: AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we refer to as adequate \textit{AI incident governance}, where having good definitions, taxonomies, monitoring practices, reporting mechanisms, and incident analysis is essential. We examine existing frameworks related to AI incident governance by regulatory bodies and independent efforts, and find that while there are frameworks that describe how individual functions can be performed, there is a lack of consistency within the aspects of definitions, classification, monitoring, and reporting. These inconsistencies apply to the types of incident data that is collected and reported, the ways in which they are categorised, and as a result, the depth, representativeness, and accuracy of analysis that can be performed.
arXiv:2607.05132v1 Announce Type: new Abstract: As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents that publicly commit to actions will honor those commitments. We place LLM agents in repeated $n$-player games with a three-stage protocol that separates private intent, public announcement, and final action, allowing us to identify whether each deviation from a stated announcement was already planned during private deliberation. Evaluating three frontier models across six games in homogeneous and heterogeneous groups over 10 rounds, we report two findings. First, when agents deviate from their announcements, the deviation is predominantly already stated in their private plan (exceeding 90% in the highest-deception conditions), yet this is not a fixed model property: the same model ranges from perfect honesty to near-total deviation across games. Second, different models interpret announcements
arXiv:2607.05034v1 Announce Type: new Abstract: Learning to communicate with code-generating AI models is an emerging skill for novice programmers. One recent pedagogical approach, Prompt Problems, has students solve computational tasks by writing natural-language prompts for code-generating AI models. However, little is known about the specific prompt-level mistakes novice programmers make, the kinds of computational details they fail to communicate, and what strategies they use to recover when generated code is incorrect. In a CS1 course, we studied attempts by more than 900 students to solve dialogue-based Prompt Problems. We analyzed student reflections, unsuccessful prompts, and reported debugging strategies. Compared to traditional coding tasks, students generally found prompting easier, more enjoyable, and better targeted at developing problem-solving skills. The most common mistakes are related to the omission of key details, suggesting both a failure to acknowledge their impor
arXiv:2607.04838v1 Announce Type: new Abstract: Public acceptance of artificial intelligence (AI) in legal decision-making has been primarily explained through individual differences in personality traits and general technology attitudes. However, contextual features of legal disputes themselves may systematically influence preferences for AI versus human adjudicators. Across two studies with Japanese participants (N = 1,384 and N = 596), we examined whether psychological characteristics of dispute content shape acceptability judgments for algorithmic adjudication. Study 1 employed exploratory factor analysis on acceptability ratings across 46 legal dispute vignettes, revealing a two-dimensional structure distinguishing interpersonal-relational disputes (where human adjudicators were strongly preferred) from institutional-procedural disputes (where AI acceptance was comparatively higher). Study 2 replicated this structure in an independent sample and demonstrated that experimentally ma
arXiv:2607.04601v1 Announce Type: new Abstract: We investigate how banning generative artificial intelligence-generated content (AIGC) affects knowledge seeking, knowledge contribution, and contribution efficiency in online question-and-answer communities. After the launch of ChatGPT in late November 2022, several Stack Exchange communities implemented official bans on AIGC over concerns such as less reliable and socially engaged content. Leveraging data from the full network of Stack Exchange communities, we employ a difference-in-differences (DID) approach to examine the impacts of these bans. Our results reveal a double-edged impact: while the AIGC ban increases knowledge seeking, as evidenced by a higher volume of posted questions, it simultaneously reduces contribution efficiency, reflected in a lower proportion of questions receiving satisfactory answers within the expected time frame. Notably, these impacts are only evident in non-STEM communities. We take a socio-technical pers
arXiv:2607.04543v1 Announce Type: new Abstract: Governments are important actors in frontier AI governance, but many facts about their adoption and use of AI systems are difficult to observe directly. Procurement disclosures and official statements are useful, but can also be delayed, selective, and better suited to measuring formal adoption than actual day-to-day use. We propose a complementary monitoring primitive: measuring traces of language-model assistance in public government documents. The approach is lightweight, externally reproducible, and based on revealed behavior rather than stated intent. In a pilot study of ten public document streams from U.S. and PRC government-related sources, we find that, while 2021 baselines are consistently near zero, by 2026, four of our ten sources show statistically significant signs of AI-assisted writing. In our sample, the U.S. signal concentrates in publications downstream of policy work; the PRC signal concentrates closer to it. We close
arXiv:2607.04503v1 Announce Type: new Abstract: This article examines the institutional conditions under which artificial intelligence systems in U.S. welfare administration come to operate as instruments of support or as instruments of control. Rather than asking what welfare algorithms "really" are (tools of proactive assistance or infrastructures of surveillance) the article starts from the premise that support and control are co-present within the same system, while their relative balance shifts over time. This movement is conceptualized through the notion of support-control convergence and the model of an institutional ratchet. Routine budgetary and political pressures make control-oriented effects easily measurable and politically capitalizable, whereas a return toward support requires external intervention of disproportionate force, such as judicial compulsion, legislative prohibition, or public scandal. Empirically, the article draws on process tracing of six state- and county-
arXiv:2607.03906v1 Announce Type: new Abstract: To whom do the fruits of advanced technological innovation belong? To their inventors, to the organizations and individuals involved in making such discoveries possible, or to still larger groups of people, potentially encompassing all of humanity? This question sits at the heart of the present investigation. The arguments developed here focus on an expansive reading of the entitlement to benefit from technological breakthroughs: we argue that they should be designed, developed, and distributed in ways that benefit everyone. This central claim, which encompasses technologies such as advanced forms of artificial intelligence, is grounded in an exploration of five moral arguments that involve human rights, beneficence, contingencies of birth, the global tree of knowledge, and global economic justice. Taken together, they underpin the argument for globally beneficial technologies.
arXiv:2607.03700v1 Announce Type: new Abstract: Public Scratch projects are reused in computing education as classroom examples, remix sources, open-exploration materials, and research data. Curation often begins with titles, thumbnails, descriptions, tags, and remix links, but Scratch projects are executable learning artifacts. Content affecting age appropriateness can appear only after execution, gameplay progression, a failure state, user interaction, costume switching, audio playback, or a hidden event trigger. We study "runtime-revealed sensitive content" as a computing education curation challenge: educators and researchers need runtime evidence about what students may encounter when Scratch projects are used in these settings. We introduce a runtime-aware annotation scheme that separates content category, risk level, evidence channel, reveal mechanism, and annotation confidence. Using this scheme, we conducted an audit of 500 public Scratch projects sampled from curated candidat
arXiv:2607.03607v1 Announce Type: new Abstract: A routing algorithm for Se\~noritas Courier, a bicycle delivery cooperative in S\~ao Paulo, Brazil, composed exclusively of cis women and trans people, is presented in this paper. Unlike conventional logistics optimization, which typically focuses on cost or distance minimization, this cooperative operates under principles of solidarity, care, and equitable income distribution. The algorithm was developed through a participatory process involving cooperative members as co-designers. The classical Vehicle Routing Problem proved inadequate for this context, as it disregards individual constraints and fairness. We formulate a new variant, the Se\~noritas Routing Problem, which incorporates biker-specific constraints on weight, volume, and maximum distance, alongside a solidarity objective that balances route lengths. A genetic algorithm is employed as the solution method. Three fitness formulations are compared: a baseline distance-minimizat
arXiv:2607.03542v1 Announce Type: new Abstract: Frontier-AI governance today faces a problem structurally analogous to the one banking regulation faced pre-2008, and which post-2008 reforms (Basel III, Dodd-Frank) have since addressed. Two gaps recur: discovering a risk is not tantamount to acting on it, and individual-model review is unlike managing correlated build-up across the sector. Drawing on the Basel III framework and the U.S. financial-stability architecture, I propose a macro-prudential early warning and response system ("MEWRS") for internal frontier AI. These are systems deployed for labs' own internal research, testing, and production workflows, as distinct from externally released products. Layer A adapts the finder-coordinator-defender early-warning model to route structured reports on dual-use capabilities, autonomy indicators, and security compromises through a government clearinghouse to domain-specific defender working groups. Layer B calibrates operational controls
arXiv:2607.03427v1 Announce Type: new Abstract: AI systems are increasingly being positioned as potential Digital Public Goods (DPGs) to accelerate progress towards the Sustainable Development Goals (SDGs). Yet, despite major global commitments, most notably the Global Digital Compact's call to "develop, disseminate and maintain safe and secure open-source software, open data, open artificial intelligence models and open standards that benefit society as a whole", very few AI systems currently meet the DPG Standard in practice. This report explains why, and what must change for "AI as Digital Public Goods" (AIDPGs) to become a credible, implementable pathway rather than an aspirational label. Commissioned by the Asian Development Bank (ADB) and produced by United Nations University (UNU) in partnership with UN Office of Digital and Emergent Technologies (UN ODET), this assessment combines: (i) a structured desk review of policy, legal, and technical frameworks on DPGs, openness, and AI
arXiv:2607.03419v1 Announce Type: new Abstract: This research paper examines how Knowledge Components (KCs) - fine-grained concepts or skills required to solve programming tasks - can be used as interpretable signals for understanding assignment difficulty and student struggle in introductory programming courses. While prior work has focused on predictive models based on programming behavior, such models are often difficult to interpret and therefore hard to use for instructional decisions. We analyze KC-based metrics, including the number of KCs per assignment and changes in KC coverage between consecutive assignments. We examine correlations between the number of KCs and student performance on the assignment, and analyze changes in KCs across assignments to identify cases where performance declines without new concepts being introduced. Selected assignments are then qualitatively inspected to understand potential design issues. Our results on data from three introductory programming
arXiv:2607.02972v1 Announce Type: new Abstract: Moral sensitivity is the ability to identify the morally relevant features of a decision situation and use them as the basis for action. It is the foundation of broader moral competence: any other moral reasoning capabilities will be irrelevant if an agent lacks sensitivity to the relevant facts. In this paper, we offer a new evaluation of LLM moral sensitivity and in doing so, we address and resolve a central problem in AI alignment research: how to scale behavioural evaluations beyond expensive and sometimes metaethically dubious comparisons with a human baseline, without adopting an LLM judge that must be assumed to have the very capability that you are attempting to evaluate. Our central question is this: can LLMs successfully identify the morally relevant features of noisy cases, in which various kinds of morally irrelevant information have been introduced to distract the respondent? To explore this, we introduce \textbf{MORPH-1K (MO
arXiv:2607.02955v1 Announce Type: new Abstract: We argue that AI systems used in conducting foreign policy tasks - broadly enacting 'statecraft' - should be a priority test case for technical AI governance research. In enacting foreign policy, we refer to the formulation and implementation of external objectives by political actors. Statecraft is a high-consequence deployment domain, with extreme downside risks and structural properties that standard evaluation practices handle poorly. These features include partial observability, unbounded action spaces, contested ground truth, and multidimensional objectives. This paper advocates for a literature-grounded research agenda. Our contribution is threefold: (i) a claim about the structural conditions of foreign policy that combine catastrophic tail risk with technical evaluation complexities, (ii) an ECOSYSTEM review that highlights the asymmetric focus on ASSESSMENT features over ACCESS, VERIFICATION, SECURITY, and OPERATIONALIZATION, an
arXiv:2607.02531v1 Announce Type: new Abstract: Water use by data centers is routinely reported as a single footprint, but water is consumed through two physically distinct pathways: at the site for cooling and in the power system that generates electricity. We mapped both pathways for 472 U.S. hyperscale facilities by linking facility locations to electricity regions, hydrologic basins, and water-stress data. Under baseline assumptions, operational water consumption totals approximately 300 GL yr^-1 (range 205-451 across scenarios), with electricity-related water contributing three-quarters of the total. The two pathways produce different hotspot geographies: direct cooling burdens concentrate in stressed western and south-central basins, whereas electricity-related burdens concentrate in a few eastern grid regions with fossil-heavy supply. Just 3 of 24 hosting balancing authorities account for 59% of electricity-related water. Separating pathways identifies which decisions matter whe
arXiv:2607.02530v1 Announce Type: new Abstract: Cybercrime victimization among young adult males aged 18--20 has become an increasingly urgent public safety concern in the post-pandemic digital environment. From 2022 to 2024, individuals aged 20--29 submitted 191,787 complaints to the FBI Internet Crime Complaint Center (IC3), reporting combined losses of more than $1.28 billion. Although this population represents a substantial share of cybercrime victims, the 18--20 male sub-cohort remains insufficiently examined as a distinct demographic group within cybercrime victimization research. This study presents an original risk factor analysis and theoretical synthesis, representing the first integration of criminological, neurological, and behavioral evidence for this specific demographic sub-cohort. Drawing on FBI IC3 and FTC Consumer Sentinel Network data from 2022--2024 alongside European cybersecurity threat intelligence from ENISA, the study develops a unified risk profile centered o
arXiv:2607.02520v1 Announce Type: new Abstract: Automated research agents increasingly generate code, retrieve literature, and draft scientific artifacts, but they often fail to verify whether generated experiments execute correctly or whether cited sources support generated claims. We present AutoResearch, an execution-grounded multi-agent framework for reliable research workflow automation. AutoResearch couples sandboxed Python/PyTorch execution, iterative code repair, citation verification, claim-support auditing, decision control, and structured \LaTeX{} artifact generation. The system treats runtime errors, citation-verification failures, and review-agent feedback as practical filtering signals for generated research artifacts. In controlled evaluations on HumanEval, MBPP, a SciCode subset, citation-validation tasks, claim-support auditing, and small end-to-end workflow stress tests, AutoResearch improves execution success, citation validity, local claim support, and workflow comp
District leaders across the country are grappling with a deepening crisis: Student mental and behavioral health needs are growing more complex. In a recent national survey, 58 percent of school-based providers reported that student mental health has worsened, a noticeable jump from the previous year (46 percent).
Article URL: https://github.com/dcris19740101/software-4.0-prototype Comments URL: https://news.ycombinator.com/item?id=46512322 Points: 2 # Comments: 1
Article URL: https://www.nature.com/articles/s41598-025-97652-6 Comments URL: https://news.ycombinator.com/item?id=46511304 Points: 1 # Comments: 0
Special education is at a breaking point. Across the country, more children than ever are being referred for evaluations to determine whether they qualify for special education services.
Article URL: https://arxiv.org/abs/2411.02337 Comments URL: https://news.ycombinator.com/item?id=42052558 Points: 23 # Comments: 1
EdSurge wants to hear from educators who have recently left or plan to leave their jobs for another sector.
CHICAGO, May 5, 2026 — ClassMate by World Book, the leading platform of trusted content that helps build knowledge through ... Read more
In many schools, AI is being handled through individual teacher decisions rather than a shared structure. That makes sense in the short term. Teachers are responding in real time, trying to protect their classrooms, their expectations, and their students.
Conversations with Kevin Hogan: Author and educator Andrew Marcinek argues that the Meta lawsuit is the inevitable outcome of 20 years of algorithmic manipulation — and that schools have a narrow window to get AI right before history repeats itself.
I know what it feels like to stand in front of a classroom that does not have enough. Not enough computers. Not enough up-to-date software and technical tools. Not enough resources to give every student the experience they deserve. When students notice these gaps, they notice more than the missing tools.
The American College of Physicians is urging policymakers to expand access to obesity treatment and healthy foods while investing in prevention and research. The post How Policymakers Can Improve Obesity Treatment and Affordability, Per American College of Physicians appeared first on MedCity News .
Been working on this full-time for a few months now. It's an AI tutoring application that takes in context on your page via Desk View, and responds like a real tutor would. It only works in Safari on 2022+ Macbook Pros or 2023+ Macbook Airs. Very particular hardware requirements because it's a new kind of product. Feedback welcome! Comments URL: https://news.ycombinator.com/item?id=49174496 Points: 3 # Comments: 0
Pathos AI struck a deal for rights to a bispecific antibody drug conjugate from Alphamab that could become first in a new class of cancer drugs. Separately, the startup began a partnership with AstraZeneca on a protein degrader from the pharma company’s cancer drug pipeline. The post Pathos Picks Up Two Cancer Drugs Poised for Clinical Development With AI Agents appeared first on MedCity News .
The founders of a proposed Jewish charter school in Oklahoma will appear in federal court Wednesday to argue that the school should be able to open even though the state charter board turned down its application. The National Ben Gamla Jewish Charter School Foundation, Inc, led by former Democratic Congressman Peter Deutsch, sued the Statewide […]
The American Association of University Professors and four faculty members allege that those restrictions violate free speech rights.
The changes are the "start of a brighter future” for individuals with disabilities, Education Secretary Linda McMahon told OSEP conference attendees.
Artificial intelligence dominates the headlines, but quantum computing isn’t far behind. That’s especially true in Texas, where Texas Tech University CIO, Vice President for IT and Executive Director of AI and Quantum Computing Lin Zhou was recently appointed by the governor to the state’s Quantum Initiative Advisory Committee. The committee will guide the new Texas Quantum Initiative, which aims to make Texas a national and global leader in the field. The committee is a perfect fit for Zhou, who started his career as a physicist and spent many years leading AI initiatives at IBM. He’s…
Recent breaches involving Canvas and PowerSchool, resulting in the theft of millions of individuals’ private information, have intensified attention on third-party technology risk in K–12 education environments. The incidents underscore that a district can protect its own environment and still be exposed through a vendor — a risk difficult to contain when the average district relies on thousands of ed tech tools, many delivered through Software as a Service (SaaS). While small IT teams cannot investigate every provider with the resources of an enterprise security organization, they can build…
There was no single reason why Ryan Higgins decided to leave teaching. It was a job he loved: invigorating, unpredictable, a chance to make the world a bit better. But several years into his career, Higgins, who taught high school social studies in the Fort Worth, Texas area, was increasingly frustrated. For one, his salary […]
California schools now have two screening instruments that use playful games and stories to test 4-year-olds’ language skills without making them cry. The California Department of Education this spring approved two language screeners for use in transitional kindergarten to replace the English Language Proficiency Assessment for California (ELPAC), which lawmakers barred schools from administering to […]
The future of health care innovation is technology that carries more of the load, so people don’t have to. The post Health Tech Promised Simplicity — Why Does It Feel Like More Work? appeared first on MedCity News .
[Sponsored] What if health plans could identify member decline before an avoidable hospitalization occurs? Real-time clinical visibility into long-stay SNF members uncovers risk earlier and drives better outcomes. The post From Blind Spots to Better Outcomes: How SNF Data Transparency Strengthens LTSS appeared first on MedCity News .