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:2607.02197v1 Announce Type: new Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this imperative, this paper presents an overview of AI risk assessment (identification and analysis) and management methodologies. It begins by reviewing the worldwide regulatory landscape that drives the need for systematic AI risk assessment. Then we characterize the spectrum of AI-related risks identified in the literature, from technical failures to ethical and social impacts. Subsequently, it reviews key risk assessment methodologies proposed for AI systems, focusing on general frameworks. The paper highlights best practices and illuminates methodological gaps, highlighting areas for further research on AI risk assessment.
arXiv:2607.02144v1 Announce Type: new Abstract: While AI promises major benefits, its development and deployment can shift costs onto others, including environmental pressures on local communities, labor and creative displacement, and systemic risks from rapid frontier development. Taxation is an integral part of policy design, and recent academic, industry, and policy debates have begun to consider whether tax instruments can help address these harms. In this paper, we explore the viability of AI taxation. More broadly, AI taxation should not be understood only as Pigouvian correction. In the AI context, taxation can also correct harmful activity, redistribute unevenly borne costs and gains, and fund regulatory capacity. We discuss the main externalities associated with AI and survey possible tax instruments, including corporate income and rent-based taxes, consumption taxes on AI-related services, and excise instruments tied to specific AI activities. We further assess the benefits a
arXiv:2607.01913v1 Announce Type: new Abstract: Organizations increasingly make strategic decisions about AI systems whose behaviour, failure modes, and institutional effects cannot be fully known at design time. This technical report reframes strategic red teaming as a board-level governance discipline for testing the assumptions under which AI-enabled strategies are approved, funded, and supervised. The report proposes a six-component model for strategic red teaming in AI governance: an explicit assumption register, an adversarial mandate, independence criteria, evidence grading, a board-facing decision record, and a follow-up mechanism for unresolved findings. The model is intended to make strategic uncertainty inspectable before it becomes operational exposure. It treats red teaming not as penetration testing, scenario theatre, or generic risk review, but as structured adversarial testing of the claims on which governance decisions depend. The contribution is conceptual and design-
arXiv:2607.01776v1 Announce Type: new Abstract: In the age of AI, what will be good knowledge? This article, which is accepted and forthcoming in a special issue of Modern Fiction Studies on "Cultural AI" in 2027, applies digital humanities methods to map epistemic virtues (like "true," "accurate," "creative") used in a corpus of 553 journal articles on AI published in 2024. "Creativity" comes in for special attention as an example. Exploring this discourse of value, the article considers how a framework might be developed for evaluating the knowledge-worth of AI -- one less locked into values formed around pre-AI "knowledge work" agents or structures, and more open to the future values of "generativity." The essay is supported by an online digital kit for exploring data models of the corpus of articles on AI it studies.
arXiv:2607.01750v1 Announce Type: new Abstract: Open source software (OSS) is not homogeneous. A project's purpose, governance, and funding shape how its community forms, who contributes, and how the software is maintained, yet empirical research often samples OSS broadly and reports findings as if they held for open source as a whole. We argue that OSS comprises distinguishable sub-genres, and that the sub-genre a study samples bounds how far its findings generalize. Using a light, multi-source review that screens 3,925 unique papers, we synthesize a typology of fourteen OSS sub-genres, from well-studied ones such as community-driven, company-backed, foundation-governed, research and scientific, and open source for social good (OSS4SG), to under-studied ones such as multi-company co-opetition, protestware, and open-source appropriate technology. We place the sub-genres in a framework that records each one's primary driver, governance, and funding, with its maturity in the literature a
arXiv:2607.01460v1 Announce Type: new Abstract: Human-annotated data remains foundational for machine learning and social media analysis. However, traditional data collection often relies on cumbersome pipelines that isolate content from its original source, compromising ecological validity. To address these challenges, we present Social-Annotate, a flexible browser extension that facilitates direct data collection on online platforms. By injecting customizable forms into webpages, the tool captures annotations while users interact with the native environment. Social-Annotate offers no-code design interface for the survey forms for non-technical users. Since injecting custom elements directly into host platforms creates a brittle dependency on evolving interfaces, we integrate a self-healing agent powered by large language models. This automated pipeline autonomously detects structural changes, regenerates valid target selectors, and validates them within a live browser environment. Ou
arXiv:2607.01258v1 Announce Type: new Abstract: The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in AI infrastructure, hardware, and software. In particular, graphics cards have become central to AI training, with frequent hardware updates required to meet escalating computational demands. However, the environmental damages of graphics cards production remain understudied. This study addresses this gap by estimating the environmental damages associated with graphics cards production over the past decade (2013-2025). We analyze trends in energy consumption, carbon emissions and resource depletion. We compile and provide a dataset documenting the environmental damages of NVIDIA workstation graphics cards production since 2013. Our analysis of this dataset reveals a steady increase in production-related impacts over the period. Our finding highlights the need for
arXiv:2607.01257v1 Announce Type: new Abstract: The rapid digitalisation of financial systems has improved operational efficiency and financial inclusion while simultaneously increasing exposure to sophisticated forms of cyber-enabled fraud and electronic financial misconduct. Conventional auditing systems, which largely depend on retrospective verification and rule-based monitoring, increasingly struggle to address the complexity and speed of modern financial crime. Consequently, financial institutions are progressively adopting Artificial Intelligence (AI)-enabled Accounting Information Systems (AIS) and Natural Language Processing (NLP) technologies to strengthen fraud detection, continuous auditing, and institutional monitoring. This study examined the influence of AI-enabled AIS on auditing and fraud detection effectiveness within Nigeria's financial services sector while additionally evaluating the moderating role of NLP. Anchored on the Fraud Diamond Theory and the Technology Ac
arXiv:2607.01256v1 Announce Type: new Abstract: Overwhelmed courts in the United States review millions of default judgments each year. Unfortunately, such manual reviews are time-consuming and prone to error. In an audit of 188 debt collection cases granted default judgment by the Superior Court of Los Angeles, we find that 4% contained major defects that should have entirely prevented default judgment, 10% contained inconsistencies requiring reduced judgments, and 32% contained errors requiring amendment prior to judgment. To support courthouses in default judgment review, we collaborated with courthouse attorneys and judges in designing a Default Assistant. The Default Assistant employs large language models to evaluate a case with respect to predetermined legal requirements and provide cited recommendations for an expert user's review. We equip users to verify these recommendations by grounding the assistant's explanations in cited quotes and tables from the original case filings.
arXiv:2607.01255v1 Announce Type: new Abstract: Universities have responded to generative artificial intelligence (GenAI) in noticeably different ways, both internationally and within Spain. So far, the dominant reaction has been defensive, this is, most institutions frame the debate around AI detection, plagiarism, academic integrity and a presumed drop in student effort, prioritizing basic training for academic staff over students. Other group of pioneering universities is doing the opposite, pursuing deeper adoption, and assuming that any policy built on prevention or sanction will not hold. This paper sides with that second view. Obsessing about detection is a dead end, since generated text is increasingly hard to distinguish from human writing, and detectors still misfire too often to be trusted. What universities need instead is a coordinated effort to set clear, course-by-course rules for GenAI use, redesign assessment toward authentic and interdisciplinary assessment that foste
arXiv:2607.01254v1 Announce Type: new Abstract: Benchmarks are the primary instruments through which AI capability is measured, compared, and governed. This paper argues that the validity of frontier AI benchmarks is a function of the quality of human judgment embedded in their construction, and that this quality is structurally scarce in ways that standard scaling narratives obscure. As foundation models approach ceiling performance on existing evaluation suites, discriminating signal concentrates in the hardest benchmark items, precisely those requiring elite expert judgment to design. We term this the benchmark ceiling problem: the progressive exhaustion of evaluation signal as models saturate the easy majority of items while the difficult tail, authored by a thin stratum of highly expert evaluators, remains the only source of genuine discrimination. The paper develops this argument in three steps. First, we present a formal model of benchmark signal depreciation. Benchmark scores a
arXiv:2607.01253v1 Announce Type: new Abstract: Rationale. The diagnostic radiologist's role in 2035 will not look like it does today. Imaging AI is already changing how worklists are organized, how reports are generated, and which cases require a radiologist's attention. What remains genuinely contested is not whether the role changes but how. Approach. Three subject-matter experts (two radiologists and one health tech professional with more than 20 years of experience in medical imaging IT) independently authored 2035 job descriptions for the diagnostic radiologist using a shared template. Each author wrote from a distinct vantage point: one optimistic, one framed as a trade-off view incorporating workforce economics, and one structured around professional stratification. The three versions were published openly and subjected to a structured comparison across seven dimensions. Key findings. The three versions agree on direction but disagree on magnitude. All three describe a radiolog
arXiv:2607.01252v1 Announce Type: new Abstract: Background: Dermatology AI has mainly focused on image-based diagnosis, while chronic disease workflows have received less attention. We surveyed Indian dermatologists to map routine clinical challenges, with a focus on atopic dermatitis (AD), and assess current AI use. Methods: A nationwide cross-sectional survey commissioned by the Society for Eczema Studies included 377 practicing Indian dermatologists. The survey assessed clinical challenges, AD workflow barriers, AI use, adoption barriers, and ethical concerns. Analyses used descriptive statistics, chi-square tests, false discovery rate correction, and multivariable logistic regression. Results: Patient adherence (61.3%) and treatment planning in difficult or refractory cases (57.0%) were reported more often than diagnostic uncertainty (48.0%). In AD care, severity scoring was reported as a challenge by 47.7% and had the lowest satisfaction among measured workflow areas. Current AI u
arXiv:2607.01251v1 Announce Type: new Abstract: Debate, where AI agents argue opposing positions, has emerged as a key approach to scalable oversight. However, debate faces a fundamental tension: models are incentivized to be persuasive to the judge, which may not always align with epistemic honesty. In this work, we propose an alternative paradigm: disagreement resolution, which reframes the interaction mechanism from adversarial debate to collaborative truth seeking. Drawing on principles from human mediation and conflict resolution, where mediators facilitate dialogue to help disputing parties reach consensus rather than adjudicating between them, we design an automated pipeline that adapts these strategies to AI oversight. Unlike standard debate where models argue for fixed positions, our pipeline directs models to collaboratively identify points of disagreement, examine the evidence for conflicting claims, and converge toward consensus or isolate the specific ''crux'' of their dis
arXiv:2607.01250v1 Announce Type: new Abstract: Sociotechnical alignment concerns the social desirability of AI behavior and is thus inherently normative, not merely technical. While NLP research increasingly addresses its technical aspects, it often leaves underspecified what such "social desirability" entails. We argue that this reflects a fundamental gap: the absence of a systematic way to specify how sociotechnical alignment defines, justifies, and evaluates socially desirable AI behavior. To address this gap, we introduce a human-centered framework for specifying sociotechnical alignment. We draw on social-scientific accounts of sociobehavioral desirability to ground the basis for behavioral desirability judgments and use this framework to analyze how alignment is specified in practice. Our systematic literature review identifies recurring patterns: normative concepts grounding desirability judgments are often unspecified or conflated with alignment targets for (desired) system be
arXiv:2607.01248v1 Announce Type: new Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation. In these settings, users may obtain highly structured answers, plans, and judgments without sufficient domain practice. This paper proposes a practice auditing framework for LLM use and AI-generated content governance. It introduces collective empiricism to describe how LLMs compress and reorganize large-scale human experience into outputs that appear empirical and rational, and pseudo-rational cognition to describe how users may mistake AI-generated structured expression for their own rational understanding. The paper analyzes AI subjectivity illusion, subjectivity structures in input materials, template loops in AI-AI conversations, statistical misjudgment in AIGC detection, and memory pollution when generated content enters future contexts, long-term memory, retrieval spaces, or agent skill systems. To r
arXiv:2607.01247v1 Announce Type: new Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate steps. They are also difficult to grade at scale because instructors must apply partial-credit rubrics consistently while giving feedback that helps students repair misconceptions. This paper evaluates six contemporary large language model (LLM) configurations, Gemini 3.1 Pro Extended, Gemini 3.5 Flash, ChatGPT 5.5 Pro Extended, ChatGPT 5.5 Thinking, Claude Pro Opus 4.7, and Claude Sonnet 4.6, as grading assistants for an undergraduate discrete mathematics examination. The study compares two grading policies. The BASELINE policy uses a stricter rubric-following prompt that emphasizes explicit evidence and complete justification. The LIBERAL policy was added after preliminary grading showed that the baseline condition sometimes applied harsh point deductions and failed to recognize valid parti
arXiv:2607.01246v1 Announce Type: new Abstract: Recent proposals for HTTP-based sustainability disclosure focus on \textbf{what} environmental information should be transmitted at the protocol boundary, for example through response headers, but leave open the practical question of \textbf{how} such per-request values can be generated in realistic deployments. This paper addresses that implementation gap. We present a model-based approach for estimating resource consumption and $CO_2e$ per HTTP request without requiring fine-grained production power telemetry. The approach benchmarks endpoints offline under controlled conditions, derives compact endpoint-specific energy models from observable request features, and evaluates these models online at the HTTP server boundary. We implement this mechanism as an nginx extension that loads a JSON model registry and emits per-request metadata for energy, grid intensity, embodied emissions, and total request-level impact. We show that heterogeneo
As children, we play hide-and-seek. There is a kind of logic to it: If you cannot see me, then I cannot see you. As adults, and sometimes as leaders, we can fall into a similar pattern.
The rapid rise of generative AI has turned classrooms into a real-time experiment in technology use. Students are using AI to complete assignments, while teachers are leveraging it to design lessons, streamline grading, and manage administrative tasks.
In many K–12 schools today, fragmented student data has quietly become one of the most significant barriers to effective decision-making and day-to-day operations. While digital tools have expanded rapidly in classrooms and administrative offices, the systems managing student information, communication, and reporting often remain disconnected.
New edtech products that have caught our attention this month
The global math gender gap: “Whatever we do, the action we take to address the issue must start quite early and must be very targeted.”
The Centers for Disease Control and Prevention (CDC), as part of its continuing effort to reduce public burden and maximize the utility of government information, invites the general public and other federal agencies the opportunity to comment on a proposed information collection, as required by the Paperwork Reduction Act of 1995. This notice invites comment on a proposed information collection project titled World Trade Center Health Program Stakeholder Experience Feedback Collection. The data collection seeks to solicit feedback from World Trade Center Health Program members and providers about their experiences with Program services, communications, administrative processes, and operations.
The Centers for Disease Control and Prevention (CDC), as part of its continuing effort to reduce public burden and maximize the utility of government information, invites the general public and other federal agencies the opportunity to comment on a continuing information collection, as required by the Paperwork Reduction Act of 1995. This notice invites comment on a proposed information collection project titled Medical Monitoring Project (MMP). The purpose of this data collection is to describe the health-related behaviors and clinical outcomes of adults diagnosed with HIV in the United States to guide national and local HIV-related service organization and delivery and monitor receipt of HIV treatment and prevention services and clinical outcomes.
In compliance with the requirement for opportunity for public comment on proposed data collection projects of the Paperwork Reduction Act of 1995, HRSA announces plans to submit an Information Collection Request (ICR), described below, to the Office of Management and Budget (OMB). Prior to submitting the ICR to OMB, HRSA seeks comments from the public regarding the burden estimate, below, or any other aspect of the ICR.
The Food and Drug Administration (FDA, the Agency, or we) is proposing to amend the drug establishment registration and drug listing requirements with respect to establishments engaged in distributed manufacturing and foreign drug establishments. This action, if finalized, will provide a pathway for a distributed manufacturing establishment that manufactures drugs at multiple different physical locations to register as a single drug manufacturing establishment and align drug establishment registration and drug listing regulations applicable to foreign drug establishments with statutory changes made by the Preparing for and Responding to Existing Viruses, Emerging New Threats, and Pandemics Act (PREVENT Pandemics Act).
In accordance with the Paperwork Reduction Act of 1995, ED is requesting the Office of Management and Budget (OMB) to conduct an emergency review of a revision of a currently approved information collection request (ICR).
In accordance with the Paperwork Reduction Act (PRA) of 1995, the Department is proposing a new information collection request (ICR).
I just started working in an emergency department that has a locked psych unit. I'm new to the ED but I knew I would have to rotate through this unit. What wasn't clear to me was that I would be assigned to the psych unit once every three shifts. I honestly don't know if I can handle it. I'm not the worst at handling psych/behavioral/mental health crisis type of stuff but it's definitely not my thing. The amount of psych/behavioral issues we deal with in the regular ED is enough for me. I'm not easily spooked but I genuinely feel on edge regarding my physical safety there. ED nurses, what do we think about this? submitted by /u/calypsoorchid [link] [comments]
I’m going into my fourth year teaching and pretty excited for it except for a few things. The main one I’m nervous about is that my schedule is going to be a 6 period schedule with a 5th period prep. I teach middle school (all grades, elective), and I’ve been fortunate enough to have had a prep in the middle of the day the past three years. I’m really nervous to be on this different schedule. My teacher friends tell me that having a prep towards the end of the day usually means I won’t have any energy to be productive at all. It’s gonna change my whole daily mentally schedule cuz when do I even use the restroom? I guess class change but I just had such a good routine last year ugh. Additionally, my room has a hole and mold in the ceiling. They need to make massive repairs. I have no idea the timeline. Good news is they have to replace a lot of stuff in my room (very necessary) but bad news is I have no idea how long it will take. I have larger classes and I emphasize procedures/routine
I would really appreciate any form of advice or encouragement. I have two years of nursing under my belt, and started on a new unit five months ago. This week I had a complete mental breakdown at work. Snot, chest grabbing, hyperventilating breakdown. It happened in front of my coworkers and I’ve honestly never been so embarrassed in my life. I feel like I’ve spent the last five months giving absolutely everything I have to this job, and despite that I still ended up getting in trouble with management due to a patient complaint. The complaint was filled with false accusations yet i still had to take time out of my shift to practically beg for their forgiveness. It feels like no matter how hard I work or how much I care, it’s never enough. I genuinely do not remember why I chose this career to begin with. Financially, I am unable to take time off yet the thought of stepping back onto that unit makes me want to puke. I’m starting to wonder if I need to leave nursing altogether because I
I keep hearing stories about nurses getting fired, sued, or having their licence taken away. I am really afraid this may happen to me because I am a very "scatterbrained." I am not a nurse yet, but I am in school. What should I do to prevent this from happening. What should I do/not do when I am a nurse. Also is it really that common? submitted by /u/Previous-Answer-1853 [link] [comments]
Recovering alcoholic here. I was in the ER about once a month for a year stretch due to extreme withdrawals. Each time, even in the complete turmoil of my physical and mental state, I had an acute sense of the disappointment in your faces, or maybe just the pure sadness of having to watch someone continuously do this to themselves. I’m still in early sobriety, a little over a year, but I want you to know how much I appreciate you and that your efforts aren’t in vain. Addiction is a lifelong battle so never say never but I hope you know some of us do crawl out of that abyss you find us in, and it’s thanks to you submitted by /u/Professional_Bike467 [link] [comments]
She fights the rest of us when it’s time for lights to go off (usually between 9-10pm, and obviously they’re not OFF off just dimmed). She skips around at 4:30 am and cranks the lights back on. No warning, just flips them all on and prances off. I’ve tried politely addressing it with her and even provided a personal sunlamp for her to use. Others have also spoken to her. But it hasn’t mitigated the issue and I’m personally nearing my boiling point with her. So what would you do/say? Unfortunately this is not her only annoying quality but I’m trying to pick my battles 😂 Edit to clarify: This chick is also a night shift nurse and refuses to go to days. I’ve already tried getting our manager to offer her a day position. 😭 submitted by /u/Jinxicatt [link] [comments]
I’m a new teacher, and I was put into this PLTW training; TLDR this curriculum training is a huge waste of time and money. The lessons are completely random and I’m very confident that a non engineer wrote this entire curriculum. The materials section is by far the worst. In addition to being completely out of place from the previous lessons, there’s very little substance in them. The substance that’s in them is flat out wrong or poorly explained. My advice for anyone forced to take this training: be nice to the trainers but just finish the work as soon as possible. Also, use as little of their curriculum as possible too. submitted by /u/Idontwantthismanga [link] [comments]
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Your child still has to do work believe it or not. I got so tired of filling out questionnaires on suspected gifted. No Karen, your child is not gifted. They “gifted” in task avoidance and missing their assignments, if that’s what you mean. But I’m not going to waste my time bumping up the rigor just for skittle Tommy to ignore that work too. submitted by /u/Emergency-Pepper3537 [link] [comments]
The other night the nurse I worked with was complaining to the supervisor about how hard the day shift nurse was on him during report. Supervisor asked why and he explained she was upset because he didn’t do the q4 bladder scan ordered. Supervisor of course asks why he didn’t do it if it’s ordered that way. Because of course the following nurse would be aggravated and now she’s aggravated because he just told her. He starts arguing with her that she’s wrong too because, “I asked ChatGPT and it said I didn’t have to do it if the patient was sleeping”. Supervisor tells him ChatGPT didn’t write the order so if doesn’t allot for sleep, then it still has to be done. He hits her with, and I do indeed quote, “ChatGPT has all the knowledge in the world, it knows more than every doctor in the world, I’ll trust it first” I’m not shocked healthcare workers are using chatGPT, there’s always dumb apples, but what a wild statement. And over something so small. As the kids say, we’re cooked, chat. 🙃
I got chosen to do a second interview for a school district for a general elementary job meaning the whole district is doing multiple hires from the same set of interviews. For the second interview they asked that we do a “performance task” however it’s worded very vaguely and I’m not sure if I’m supposed to teach a lesson to the panel or present a lesson plan… Here is the wording…. As part of the Round 2 interview, you will complete a 15-minute performance task. A description of the performance task is attached to this email to assist you in your preparation. Following the performance task, you will participate in a 20–25-minute interview with the interview panel. During this portion of the interview, panel members will ask additional questions related to your instructional practices, professional experiences, and your ability to support student learning in Performance task we believe highly effective teachers create engaging, rigorous, and inclusive learning experiences where every s
Hi fellow nurses, last night I had a very violent, aggressive patient who spat and kicked one of the CNAs very hard when she was trying to take vitals. This patient was brand new to our unit (a transfer) and because it was shift change, I barely had a moment to sit down and read through all of the notes thoroughly, but the few things that I did read is that this patient has always had a sitter/in q2h bilat wrist restraints (we were too short staffed last night for a sitter so we had him on careview monitoring), and the other thing was the family specifically stated they do not want their spouse on any antipsychotic medications. After my CNA notified me she had been assaulted, I immediately messaged the Provider to get oral Zyprexa added as a 1x dose. Before administering, I wanted to be courteous and to let the spouse know that I would be giving this medication because of the patient assaulting staff, and an anti-psychotic would be necessary. The wife initially agreed. I gave the patie
I just wanted to share in a way possible for people to know with anxiety, anything is possible! I avoided this test since I graduated with my bachelor’s and master’s in education. Today I can say I passed my teacher certification subject area exam today. It was the exceptional student education K-12 . I feel so grateful and happy about it! I never thought I could do it because I didn’t think it was possible, I was so afraid. Even though I taught in different settings so I had knowledge. All the random studying and working with sweetheart students with disabilities made a difference. submitted by /u/Apprehensive-Cry2104 [link] [comments]