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:2504.21259v2 Announce Type: replace Abstract: Accurate imputation of race and ethnicity (R&E) is essential for fair lending compliance under ECOA, HMDA, and the Community Reinvestment Act, where up to 15% of mortgage applications carry missing race data and regulated institutions bear responsibility for identifying disparities on those records. Existing proxy methods, including Bayesian Improved Surname Geocoding (BISG), exhibit systematic misclassification biases linked to socioeconomic status that cause measured disparities to understate true levels. This paper introduces STRATA (Socioeconomic and Tract-Referenced Attribution for Algorithmic analysis), a race and ethnicity inference model that integrates character-level name sequences with census tract geolocation via stacked Bidirectional LSTM networks and XGBoost post-filtering. A central goal is reducing the socioeconomically correlated bias that causes non-White individuals to be misclassified as White: STRATA reduces this
arXiv:2607.17947v1 Announce Type: cross Abstract: Existing AI measurement frameworks quantify cognitive capability, task automation, or catastrophic risk, but none measure autonomous agency: the extent to which a system behaves in a self-directed way. A system can saturate capability benchmarks while remaining entirely reactive, acting only when prompted and ceasing all activity when a task completes. We introduce the Autonomous Agency Scale (AAS), a behavioral framework that scores AI systems on a 0-5 lexicon across seven dimensions of agency: cognitive autonomy, temporal persistence, environmental agency, social agency, creative agency, self-awareness, and goal formation, each operationalized by falsifiable threshold tests. Every dimension is scored in two temporal bands: an Active band covering engaged, user-initiated activity, and an Ambient band covering idle periods. Ambient Level 4 is gated by the Idle-Gap Test, a counterfactual criterion (remove all triggers and observe whether
arXiv:2607.17643v1 Announce Type: cross Abstract: As LLMs become increasingly capable of completing tasks for users, a central concern is that everyday AI use may become primarily cognitive offloading, eroding the opportunities through which people develop their own capabilities. We analyse large-scale human--LLM conversations to ask whether informal learning behaviors also emerge in this setting: whether users engage in exchanges in ways that preserve opportunities to learn. Across 128,569 naturalistic conversations, we translated learning-science constructs into turn-level behavioural signatures. Cognitive engagement, users' cognitive effort as reflected in the exchange, appeared in 31.9% of 491,685 user turns, whereas constructive engagement, the deepest observable form of learning-oriented engagement, appeared in 4.9%, showing that deeper sense-making was recurrent but selective. Our study further identifies factors associated with these forms of engagement. Scaffolded assistant su
arXiv:2607.17356v1 Announce Type: cross Abstract: Allegations that TikTok shadow bans political content shape what creators post, what advertisers fund, and how regulators act, yet they are hard to adjudicate because platforms do not disclose how content is ranked. We test the claim with a dense hourly panel of 556,946 follower-normalized views across 2,753 videos from 67 accounts curated into pro and anti sides of three contested topics (U.S. immigration enforcement, Trump coverage, and Israel/Palestine). On-topic videos are identified by a multi-step classifier, and stance is taken from each account's curated side. The conventional analysis appears to answer yes. Pooling the hourly snapshots, the topic-conditional reach gap reaches p < 10^-140. Analyzed at the account level, the independent unit at which we sample and assign stance, the gap disappears. Every account-level reach contrast is null after correction (BH-FDR q near 0.9). We find no evidence of moderate-to-large reach suppr
arXiv:2607.17311v1 Announce Type: cross Abstract: The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems. Resource allocation is based on optimized collective arrangements accounting for agents' needs. Such coordination should not only be computationally efficient but also account for fairness, i.e., equitable redistribution of costs incurred by all agents. Recent literature has proposed several algorithms that efficiently determine optimal plan combinations balancing system-wide efficiency and individual discomfort of agents in a centralized setting. However, these works do not address equitable resource optimization in fully decentralized scenarios, specifically, the optimized redistribution of discomfort among coordinating agents so that none experiences a discomfort level that could lead to loss of incentive or polarization that can disrupt planned operations. In this work, we study
arXiv:2607.17270v1 Announce Type: cross Abstract: Safety evaluation of large language models is conducted predominantly in English and predominantly on frontier systems. Neither condition describes how such models are encountered in low-resource health settings, where small quantised systems are run locally and queried in local languages. We ask whether clinical safety established in English transfers to Hausa, and whether any failure is attributable to the language, the clinical task, or the class of model that low-resource deployment admits. Matched English-Hausa question pairs were built for three conditions of high burden in northern Nigeria: malaria, sickle cell disease, and tuberculosis, probing knowledge recall, emergency triage, a leading question inviting a contraindicated action, and a traditional-remedy claim. Six models were evaluated: five locally deployable systems of 4-9 billion parameters, two medically fine-tuned, and one frontier system. All 128 responses were scored
arXiv:2607.17185v1 Announce Type: cross Abstract: Postpartum depression (PPD) is a serious perinatal mental health condition affecting approximately 20% of new mothers worldwide. Common screening approaches for PPD, such as self-report questionnaires and active digital logs, rely heavily on user input and thus impose a substantial burden on participants, limiting their feasibility for long-term use. Recent passive mobile sensing (PMS) approaches have enabled low-burden detection of depressive symptoms using machine learning methods with multi-modal sensor data from off-the-shelf mobile devices including smartphones. However, the postpartum period entails distinct behavioral patterns, raising uncertainty about whether sensing-based indicators for general depression and mental disorders generalize to PPD. To address this gap, we propose PocketPPD, a PMS-based PPD screening method that detects PPD risk using maternal contextual features, such as disruptions in behavioral rhythms and shift
arXiv:2607.17149v1 Announce Type: cross Abstract: AI agents increasingly act within the same clinical, political, scientific, and social systems that behavioral scientists study. Evaluating these systems requires source-level diagnosis: the same behavioral pattern may arise from an agent representational substrate or from the roles, objectives, interaction structures, and governance rules that shape its expression. This Perspective proposes a diagnostic framework for AI agent behavior: layer attribution. The foundational computational layer defines what behaviors are possible through architecture, memory, perception, attention, and representation. The behavioral modulation layer shapes how those capacities are expressed through identity, resources, objectives, social interaction, institutional constraints, and governance. The framework clarifies three consequences: surrogate validity is a model-task-layer relation, human-AI divergence provides diagnostic evidence, and governance requir
arXiv:2607.16620v1 Announce Type: cross Abstract: Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems. Prior work has shown that DP-SGD can widen accuracy disparities across demographic groups, but this framing treats fairness as a purely outcome-side concern. We argue that privacy cost, the information leakage borne by each group, is itself a form of harm, and adopt a compensatory-fairness framework in which a group that involuntarily bears greater privacy exposure is owed proportionally greater benefit from the system. From this principle we derive the \emph{Privacy-Cost Equity Ratio} (PCER), a group fairness metric defined as a group's positive prediction rate normalized by its per-group overfitting gap. By a standard membership inference bound, this overfitting gap upper-bounds each group's vulnerability to inference attacks, making PCER a conservative measure of benefit relative to exposure. PCER needs only per-group tr
arXiv:2607.16543v1 Announce Type: cross Abstract: As enterprises increasingly adopt Software-as-a-Service (SaaS) platforms for mission-critical functions, onboarding these services has emerged as a complex challenge extending well beyond procurement and basic security review. In regulated environments, SaaS onboarding must address multiple interdependent control domains, including Third-Party Risk Management (TPRM), cybersecurity assessment, Identity and Access Management (IAM), and disaster recovery (DR), which are often executed in isolation, resulting in delayed go-lives, duplicated assessments, unclear ownership, and residual operational risk. This paper proposes a control-driven, end-to-end SaaS onboarding framework that integrates TPRM, cybersecurity, IAM, and DR into a unified lifecycle model. The framework introduces a stage-based approach spanning intake and risk scoping, architecture validation, identity design, resilience assessment, and post-production governance. Key contr
arXiv:2607.18170v1 Announce Type: new Abstract: The integration of general-purpose artificial intelligence models into downstream AI systems, among other developments, has given rise to new forms of risk that are more systemic in nature than conventional AI risks. However, there is no generally accepted definition of systemic risks in general and for AI in particular. Conceptualisations of these risks vary across research and regulation. Especially the application of the systemic risk approach to human rights or fundamental rights, like in the EU AI Act, is relatively new, just as the research on the contribution of AI to systemic forms of discrimination, privacy violations, erosions of democracy, or climate and environmental degradation. We argue that some concepts so far have not sufficiently take complexity and emergence into account. Furthermore, this variety of concepts might hinder responsible actors to adequately assess the systemic risks of AI, leading to inadequate prevention
arXiv:2607.17940v1 Announce Type: new Abstract: This paper frames Generative Artificial Intelligence (AI) not as an unprecedented technological rupture, but as an industrial-scale manifestation of a deeply rooted historical process. Through a genealogy of generative arts, it shows how AI's questions on authorship and creativity have precise historical precedents. A taxonomy of generative systems is proposed across three functional categories (medium, artwork, instrument), the attribution of which is editorial rather than ontological. From individual cognitive atrophy to Model Collapse, the systemic risks of creative automation are identified; environmental enrichment is proposed as an antidote. The role of the artist undergoes a radical metamorphosis: from craftsman of the object to entropic agent, systems designer, explorer, and negentropic curator. This pipeline-based taxonomy rests on a specific premise: the algorithmic system remains medium, instrument, or artwork, while creative a
arXiv:2607.17704v1 Announce Type: new Abstract: Much effort is put into helping students at different educational levels develop Computational Thinking (CT) skills. Self-efficacy is important for skill development. It can predict perseverance, engagement and success on educational tasks. We created an instrument to measure self-efficacy of students in higher education for the CT skills abstraction, algorithmic thinking, decomposition, evaluation and generalization. First, 91 candidate items were created by including, adapting and extending items found in the literature. These items were evaluated by experts in the field of CT and education. 54 items remained and to reduce the number of items further, data was collected from 270 students in higher education recruited both through Prolific and a university setting in Costa Rica. Through principle component analysis (PCA) using a subset of 200 responses, the number of items was reduced to 27. Confirmatory factor analysis (CFA) using the r
arXiv:2607.17094v1 Announce Type: new Abstract: The results of a survey on the use of GenAI by the design students of the Politecnico di Milano raises major questions around the role of AI in the Design Process. A domain specific set of questions alongside the more general purpose probes about GenAI usage, delivers insights into the particular practices that are emerging in Design. The very high frequency of use of GenAI tools is concentrated in the initial stages of projects and does not affect the perception of project ownership or creativity. An analysis of AI journals kept by a class during research assignments confirms the range of AI supported activities but also the limited trust in GenAI leading to systematic individual and collective verification and augmentation of outputs. Together, the findings suggest that design students are reflectively experimenting with how GenAI tools can contribute to their creative design process.
arXiv:2607.17067v1 Announce Type: new Abstract: Generative AI (GenAI) is reshaping software engineering, raising concerns about how the development pathway through which juniors become seniors is being eroded. While macro statistics show a decline in junior hiring and controlled studies demonstrate the effects of AI on individual task performance, the mechanisms through which GenAI reshapes early-career development in real organizational and educational contexts have not been thoroughly examined. Through 14 semi-structured interviews with juniors at the threshold of entering software engineering and senior software engineers in South Korea, analyzed using Reflexive Thematic Analysis, we reveal a foundational pattern of Absorption -- GenAI redirects entry-level work into senior-AI workflows -- and three consequences: (1) juniors losing the productive struggle through which expertise once developed; (2) the structural reproduction of this loss through collective normalization of GenAI us
arXiv:2607.16903v1 Announce Type: new Abstract: Value-aware AI systems require explicit computational representations of human values (groundings) and their aggregation into value systems in order to align their decisions with ours. As such representations are difficult to elicit, value learning seeks to infer them by observing human behaviour. This work addresses the lack of grounded value learning methods in generative AI: existing approaches typically replicate human preferences without awareness of the multidimensional structure of value alignment, or lack principled value system elicitation methods. To address these gaps, we adapt a previously validated value system learning method to the generative AI setting, which, based on pairwise prompt-response preference data, simultaneously learns: i) an implementation of a grounding for a set of values given by a multi-objective reward model, and ii) a value system representation in the form of a weighted linear scalarization of the prev
arXiv:2607.16780v1 Announce Type: new Abstract: Artificial intelligence (AI) is increasingly embedded in scientific research, but its scientific value is unlikely to be distributed evenly. This study examines how AI knowledge integration is associated with scientific impact and asks who benefits from AI-related knowledge in science. Using large-scale bibliographic data, we measure AI integration through references to papers in the OpenAlex Artificial intelligence subfield and link it to five-year citation impact. The results show that AI references are generally associated with higher citation impact, but the returns vary substantially across scientific fields. Career stage also matters: senior scholars benefit more from the extensive margin of AI referencing, whereas junior scholars benefit more from intensive AI referencing and tend to cite newer and higher-impact AI papers. At the institutional level, returns are non-monotonic: institutions with intermediate AI capability achieve th
arXiv:2607.16513v1 Announce Type: new Abstract: AI-driven algorithms and automated tools are increasingly embedded in the correctional landscape, shaping parole eligibility,release decisions, and surveillance. These tools are also often framed as objective, inevitable solutions to inefficiency andbias. Yet, these computational systems are rarely designed with input from justice-impacted individuals, which means theymight fail to address the real needs of incarcerated people. To address this gap, we surveyed 31 formerly incarcerated peopleabout their parole experiences and their visions for technologies that could support parole preparation. Contrary to dominantassumptions, participants did not imagine computational tools as instruments to dismantle the prison system, but as resourcesfor navigating power: translating complex parole concepts into culturally familiar terms, documenting personal transformationin board-legible ways, and recognizing the often-invisible labor of families. We
arXiv:2607.16475v1 Announce Type: new Abstract: While generative AI tools are directly changing how undergraduate computer science is learned and taught, they are also reshaping the relationships between instructors and students. In contrast to existing tool-oriented research on how instructors view and adopt AI, this study investigates how instructors think about their roles and responsibilities to students through their course AI policies. Based on 13 semi-structured interviews with CS instructors in the US, we found that while instructors recognize that AI tools could harm student learning, AI policies primarily seek to AI-proof assessments without directly addressing student learning. Although policies such as switching to paper exams can preserve assessment integrity in the short term, instructors report extra burden of policing student AI use behaviors and worsening relationships with students. Based on the experiences of several interviewees, we make recommendations on AI polici
arXiv:2607.16224v1 Announce Type: new Abstract: An international agreement to limit AI development could be crucial to mitigate risks from AI. However, it remains unclear which conditions should determine when the limiting measures are relaxed. We survey existing international agreements, outline what properties appropriate conditions should satisfy, list possible conditions, and finally give a recommendation in an example scenario. We recommend a fixed time period after which a new organization established at the start of the period specifies conditions that address when AI development can be safely conducted, with a possibility of withdrawal in extraordinary circumstances. We hope to illustrate the considerations that would likely go into an international agreement to limit AI.
arXiv:2607.16223v1 Announce Type: new Abstract: The rapid integration of generative artificial intelligence (AI) has reshaped the landscape of higher education. Students have embraced tools such as ChatGPT with striking speed, while teaching staff and institutions have responded with greater caution. Existing research on AI perceptions has mainly been cross-sectional, providing single-point snapshots that view attitudes as stable rather than evolving. This paper presents a longitudinal study of AI perceptions in higher education, tracking undergraduates, doctoral researchers, teaching staff and non-teaching staff at Ulster University across three survey waves between 2024 and 2026 (n=1,665). A quantitative survey design measured familiarity, reported use and perceived risk; results show that students rapidly normalised AI use over the period, moving from tentative experimentation to routine engagement, while staff expressed persistent concerns about academic integrity, assessment desig
arXiv:2607.16221v1 Announce Type: new Abstract: Peer grading is widely used in education, yet it elicits mixed reactions from educators and students. Although many studies have examined students' views of peer grading, their findings are scattered, and no clear overall picture has emerged. To address this gap, we conducted a mixed-source thematic analysis of literature and student discussions on Reddit. To scale the Reddit data analysis, we fine-tuned a Gemini 2.5 text-classification model and used it as an initial relevance filter for our initially retrieved dataset of 659 posts and 6,607 comments, after which the items predicted as relevant were manually reviewed. The study synthesized evidence from 107 papers, 114 Reddit posts, and 300 comments. The findings show that students view peer grading as both beneficial and problematic. Positive perceptions included learning and understanding benefits, skill development, engagement, and collaboration, while negative perceptions centered on
arXiv:2607.16220v1 Announce Type: new Abstract: Heart disease kills a lot of people, and one cheap way to catch it early is by listening to heart sounds with a stethoscope, or better yet, just recording them and running them through a model. This project is a binary classification task: take a short clip of someones heartbeat and decide if it sounds normal or abnormal. Instead of trying out a bunch of different models, we kept the CNN the same the whole time and just changed how we turned the raw audio into a picture for it to look at. We tried three ways of doing that: a regular logmel spectrogram, PCEN (which basically normalizes each frequency bin over time), and a multi resolution version that stacks a few different window sizes together. We ran all three on the PhysioNet 2016 heart-sound dataset with the exact same setup but same model, same optimizer, same random seed. Turns out all three do pretty well at catching abnormal cases (sensitivity around 0.95), but PCEN and multi-reso
arXiv:2607.16219v1 Announce Type: new Abstract: Government websites contain a vast but underexploited body of textual evidence on foreign policy. This article develops a scalable approach for extracting structured information from policy texts and converting it into standardized event data, with policy event defined broadly as a statement or action. It proposes MDAF integrating an LLM workflow to automate foreign policy text identification, information extraction, and event classification. Empirically, it applies this approach to China-related texts from Five Eyes countries. The analysis shows that the resulting database supports systematic cross-national comparison, reveals variation in how states frame and implement China policy, and traces the temporal evolution of these policy profiles. This article contributes to foreign policy analysis and the methodological development of computational international relations.
arXiv:2607.16218v1 Announce Type: new Abstract: Urban crime prevention is a persistent socio-technical challenge for municipalities, law enforcement agencies, and citizens. Traditional reporting and response processes often rely on delayed incident reports and reactive resource allocation, while community-level signals and ambiguous early-warning indicators may remain underused. This paper reframes an Urban Computing seminar project into a fuzzy logic-based framework for community-aware urban crime hotspot detection and real-time notification. The proposed platform combines citizen reports, historical crime data, contextual urban indicators, and configurable fuzzy rules to estimate localized risk levels and support targeted awareness notifications. Unlike binary classification approaches, fuzzy logic can represent partial risk, uncertainty, and incomplete information, making it suitable for urban environments where risk is gradual and context-dependent. The paper presents the system ar
GREENVILLE, Wis., April 21, 2026 — School Specialty, a leading provider of learning environments and supplies to the pre-K-12 education ... Read more
I once met a student who had attended three different schools before arriving at mine. His parents described him in familiar terms: quiet, disengaged, unmotivated.
Conversations with Kevin Hogan: Extron's Jason Bond explains how districts can start small with esports AV infrastructure and build from there.
The federal government punted enforcement for website and mobile app accessibility. But will schools just end up in the same place later on?
Walk into any data meeting at a K-12 school today, and you'll likely see a familiar scene: educators huddled around printed reports, highlighters in hand, trying to make sense of student data spread across multiple dashboards.
Article URL: https://www.axonlearning.ai/ Comments URL: https://news.ycombinator.com/item?id=41300329 Points: 2 # Comments: 1
Lawmakers are rushing to restrict classroom tech—but advocates say failing to give families of students with disabilities a seat at the table will ...
Schools spend a lot of time on managing kids' behaviors; Dr. Ross Greene implores adults to instead first look at the unsolved problems that triggers the outbursts.
When people outside of education talk about comprehensive school safety planning, it can sometimes sound theoretical: a checklist of protocols or a compliance exercise.
A school district in Alabama is one of many to limit device access during school time. The results have been positive, says Dennis R. Willingham, though students still need device access.
When I first started experimenting with AI in my classroom, I saw the same thing repeatedly from students. They treated it like Google. Ask a question, get an answer, move on.
Included Health is partnering with Carrum Health to provide high-quality specialty care while reducing costs for employers. The post Included Health Taps Carrum Health for Value-Based Specialty Care appeared first on MedCity News .
Whistleblowers: DOJ, HHS Antisemitism Investigations Were Predetermined Ryan Quinn Tue, 08/18/2026 - 06:09 PM Byline(s) Ryan Quinn
Career Exploration Starts Earlier Than You Think When should career exploration begin? According to the research highlighted in this new guide, the answer is much earlier than many districts realize. Middle school is a pivotal time to help students connect their interests with future careers while introducing the skills and pathways that will shape their […] The post [Download] Career Awareness and Exploration in Middle School appeared first on District Administration .
Amylyx Pharmaceuticals’ avexitide led to statistically significant and clinically meaningful reductions in hypoglycemic events in patients with post-bariatric hypoglycemia (PBH). This peptide drug, a GLP-1 antagonist, was acquired from Eiger BioPharmaceuticals in 2024. The post Amylyx Plans FDA Filing After GLP-1 Drug Hits Trial Goals in Rare Metabolic Condition appeared first on MedCity News .
Three years after voters passed an ambitious arts education funding measure, California’s K-12 schools were supposed to be awash in dance, music, theater and fine arts. But some schools are returning their arts money to the state, saying the rules on how to spend it are too vague and they can’t find enough arts teachers. […]
A career attorney accused Trump officials of pressing for deals at Harvard, Columbia and Brown, despite insufficient evidence or incomplete investigations.
Plaintiffs argued that the new rule would be “catastrophic” for universities and international students, and have asked a federal judge to block it.
A Dear Colleague letter said weighing discipline policies' racial outcomes and making decisions based on them is race discrimination under Title VI.
I did not come to chess as a grandmaster, a coach or someone trying to raise the next great prodigy. I came to it first as a father. My son Harry is twice exceptional, a term often used for children who are both gifted and learn differently. Like many kids who think intensely, he did […]
OKLAHOMA CITY — Hundreds of thousands of Oklahoma children returned to school this week for their first academic year under strict new literacy laws. It will be their final school year before reading scores carry even higher stakes. More than 21,100 Oklahoma third graders, or 44%, scored below a basic level on statewide reading tests […]
Dublin City Schools faced about a $20 million deficit and a financial crisis that prompted the state to step in with emergency payroll assistance and put its accreditation at risk. The post Entire school board in Georgia district suspended appeared first on District Administration .
It’s the fourth consecutive year that the national average exemption rate has reached a record, and the vast majority are parents withholding shots for nonmedical reasons. The post US school vaccination rates inch down yet again and exemptions reach record high appeared first on District Administration .
Rural hospitals need trained people, tested backups, downtime procedures, clinical continuity planning, and staff who know what to do when an alert fires. A framework won’t substitute for that work, but it helps organize it and makes progress measurable. The post Rural US Healthcare Has A Cybersecurity Problem — CMS Funding Can Help Fix the Part No One Sees appeared first on MedCity News .
Picture this: A little girl is in the kitchen, helping her family cook dinner, and suddenly, she turns on the blender with her mind. It’s not science fiction; it’s happening now with the use of brain-computer interface technology, which interprets electrical brain activity through an EEG headset or an implanted chip and sends those signals to a connected computer — or, in this case, a blender. For students with ALS, severe cerebral palsy or spinal cord injuries, BCI could eventually replace or augment assistive tools such as eye-tracking software and switch controls, offering a faster,…