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:2512.04630v2 Announce Type: replace Abstract: Generative AI tools, such as AI-generated hints, are increasingly integrated into programming education to offer timely, personalized support. However, little is known about how to effectively leverage these hints while ensuring autonomous and meaningful learning. One promising approach involves pairing AI-generated hints with reflection prompts, asking students to review and analyze their learning, when they request hints. This study investigates the interplay between AI-generated hints and different designs of reflection prompts in an online introductory programming course. We conducted a two-trial field experiment. In Trial 1, students were randomly assigned to receive prompts either before or after receiving hints, or no prompt at all. Each prompt also targeted one of three SRL phases: planning, monitoring, and evaluation. In Trial 2, we examined two types of prompt guidance: directed (offering more explicit and structured guidanc
arXiv:2510.24990v3 Announce Type: replace Abstract: Despite data's central role in AI production, it remains the least understood input. As AI labs exhaust public data and turn to proprietary sources, with deals reaching hundreds of millions of dollars, research across computer science, economics, law, and policy has fragmented. We establish data economics as a coherent field through three contributions. First, we characterize data's distinctive properties -- nonrivalry, context dependence, and emergent rivalry through contamination -- and trace historical precedents for market formation in commodities such as oil and grain. Second, we present systematic documentation of AI training data deals from 2020 to 2025, revealing persistent market fragmentation, five distinct pricing mechanisms (from per-unit licensing to commissioning), and that most deals exclude original creators from compensation. Third, we propose a formal hierarchy of exchangeable data units (token, record, dataset, corp
arXiv:2502.09049v2 Announce Type: replace Abstract: Digital health interventions, particularly electronic referrals (e-referrals) and health information systems, have revolutionised clinical workflows in public hospitals by automating processes. However, the utilization of e-referrals has yielded mixed outcomes, with varying levels of success in organisational processes.This paper explores improvisation of health information systems in Southern African public hospitals from a socio-technical perspective. In particular the paper explains the design-reality gaps giving rise to improvisations of mandated health information systems in order to understand their occurrence and impact on referral outcomes. We employed the design-reality framework and the Process framework for Healthcare Information System Workarounds and Impacts to explain the socio-technical issues related to the phenomenon of interest.We conducted semi-interviews with 31 respondents from health organisations as case studies
arXiv:2502.05110v2 Announce Type: replace Abstract: Applied ethics applies ethical decision-making to domain-specific contexts using contextual information such as agents, actions, temporal and spatial settings, and theoretical constructs such as utility, virtues, rights, and duties. However, representing an ethical decision is challenging as it may be abstract, context-sensitive, and semantically heterogeneous. Nevertheless, important ethical and contextual factors can be formally modeled to support structured ethical reasoning. Knowledge representation and reasoning provide a mechanism to translate abstract ethical concepts into machine-interpretable conceptual structures in the context of an event. To achieve this, we propose ApplE, an Applied Ethics ontology that models ethical theory and event context within a unified and modular conceptual framework for ethical decision-making. The ontology was developed using a modified version of the Simplified Agile Methodology for Ontology De
arXiv:2608.02518v1 Announce Type: cross Abstract: The most capable AI deployments are not single models but ensembles of specialized agents that delegate and act in coordination. This architecture unlocks powerful new capabilities, and it also introduces risks that existing frameworks for monitoring, detection, and mitigation were not designed to address. Most state-of-the-art AI abuse detection literature focuses on single-turn or multi-turn (single-session) threat models. This leaves a critical gap: an attacker can decompose a harmful goal into innocuous-looking units and execute each in isolated agentic sessions. The agent is stateless between conversations, but the attacker is not. This asymmetry allows for cross-session trajectories that are effective at evading detection. Our contributions are twofold. First, we demonstrate cross-session goal decomposition as an evasion technique, showing it may elicit more harmful capability than equivalent single-session or multi-turn attacks.
arXiv:2608.02486v1 Announce Type: cross Abstract: Open-source LLMs reliably name Zeus, Jupiter, and Thor, but recover their counterparts in less-represented traditions like Finnish, Slavic, Egyptian, or Chinese mythology far less consistently. We ask where inside the model this cultural default is produced. On a parallel cross-cultural substrate of Thompson-motif entities, we instrument 18 open-source LLMs from 8 architecture families with linear probing, logit lens, activation patching, and output extraction. The residual stream cleanly distinguishes cultures, well above a name-string baseline, yet the decoder collapses culturally-specific tokens onto dominant-tradition ones. The failure is at readout, not at representation. Asking the same question in the target culture's native language versus English produces failures that cluster within language but decouple across language: the decoder is gated on prompt language. We release a per-entity (probe, output) decomposition framework, a
arXiv:2608.02420v1 Announce Type: cross Abstract: This work-in-progress research paper explores Chat-Debugging, a novel use case for large language models as an assistant for hardware debugging tasks to improve students' debugging skills. Hardware debugging can be a time-consuming and stressful skill to develop, leading to frustration and other negative emotions. While past work has explored streamlining and automating software-based circuit debugging where digital circuits are dominant, Chat-Debugging aids in physical hardware debugging where circuits may be analog, digital, or mixed-signal. Qualitative data were collected from LLM chat logs and interviews with a fourth-year electrical engineering undergraduate student. Major themes were extracted using a constant comparative analysis. Chat-Debugging incorporates accurate hardware information, properly handles natural language descriptions of circuits, and improves debugging confidence. A successful Chat-Debugging session includes inv
arXiv:2608.02409v1 Announce Type: cross Abstract: Benchmark suites assess model capability on controlled tasks; large-scale conversation corpora capture naturalistic use without user feedback; and in-interface feedback mechanisms record satisfaction without task purpose. Together, they leave a critical gap in LLM evaluation: no existing infrastructure routinely links interaction trajectories to user-defined outcomes. We introduce MonitrLLM, open-source infrastructure for community-centered LLM evaluations that links full conversation transcripts to user-reported task intent and outcome assessments, treating all three as primary evaluative signals rather than optional metadata. To demonstrate the value of this approach, we conducted a two-week feasibility pilot with 26 college students using ChatGPT, collecting 206 evaluation reports with full conversation transcripts. The findings from our pilot demonstrate the value of connecting conversation trajectories with user-reported outcomes.
arXiv:2608.02398v1 Announce Type: cross Abstract: Many technology firms now build software products in the open, inviting outside developers to contribute alongside their employees on platforms like GitHub. Does this openness in product development pay off? Analyzing 977 U.S. high-tech firms from 2001 to 2025, this study finds that open-source adoption raised firms' gross margins by 4-5% on average. These gains flow largely through higher labor productivity, as firms integrate external contributors' diverse knowledge into internal workflows, broadening the organizational knowledge base without a commensurate rise in labor costs. However, the payoff emerges only when outside volunteers supply a meaningful share of the work (around 35% in this sample), and hinges on the firm's resource configuration. While there are multiple pathways to profitability, pairing open-source product development with sustained internal R&D is a core condition present in all high-profitability configurations.
arXiv:2608.02296v1 Announce Type: cross Abstract: In recent years, cybersecurity threats have increasingly exploited human behaviour rather than purely technical vulnerabilities, exposing the limits of traditional awareness programmes delivered outside real-world contexts. To bridge this gap, we introduce TrainShield, an interaction paradigm for contextual cybersecurity training that embeds adaptive learning interventions directly within user workflows. The system integrates real-time risk detection (e.g., phishing and data loss prevention) with event-triggered hypermedia overlays that dynamically connect users to context-specific learning nodes embedded within their browsing workflow to deliver personalised micro-learning content and structured feedback tailored to the user's knowledge level and current context. This approach operationalises behavioural theories by transforming security incidents into immediate learning opportunities, shifting users from automatic to reflective decisi
arXiv:2608.02024v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used across diverse tasks in K-12 education, yet existing safety evaluations rarely examine how harmful or inappropriate content appears in interactions between LLMs and students or teachers. To address this, we present EduZone, an evaluation framework for LLM safety across diverse educational scenarios. Our framework systematically combines (1) student- and teacher-facing LLM usage contexts, (2) fine-grained curriculum concepts, and (3) 6 risk categories and 28 subcategories spanning both conventional and education-specific harms to generate contextually grounded adversarial interactions. We construct these interactions in three settings: single-turn requests, static multi-turn conversations, and dynamic multi-turn conversations. Using these interactions, we evaluate ten LLMs using four safety levels: refusal, safe assistance, risky assistance with safety guidance, and fully risky assistanc
arXiv:2608.01937v1 Announce Type: cross Abstract: Autonomous agents operating in shared environments must make decisions that affect multiple individuals with potentially conflicting interests. We propose a formal framework for moral decision-making grounded in Scanlon's contractualism, an ethical theory that evaluates the permissibility of actions in terms of principles that no one could reasonably reject. To operationalise contractualist reasoning, we use ASPIC+, a structured argumentation framework, extended with value-based filtering to model how each agent's values determine which reasons are morally relevant in the first place. The result is a Contractualist Argumentation Framework in which agents' reasons are formally represented, compared, and evaluated through argumentation semantics. We illustrate the approach through a worked example in a domestic setting and discuss its relation to existing value-based argumentation approaches.
arXiv:2608.01540v1 Announce Type: cross Abstract: This experimental study investigates how people rely on different sources of advice when detecting AI-generated fake news (deepfake news). In a laboratory deepfake detection task, student participants identified the proportion of human-written (non-AI-generated) content in synthetic deepfake news articles and received advice from ChatGPT (GPT-4), human peers, or linguistic experts. The results show that participants rely more on ChatGPT than on human peers when detecting GPT-2-generated deepfake news. Participants also rely more on linguistic experts than on peers, while the relative reliance on experts versus ChatGPT is mixed across experimental waves, potentially reflecting time trends in beliefs about AI-based detection. Importantly, in the additional experiment conducted in 2025 under the same experimental procedure, participants relied more on linguistic experts than on ChatGPT. Moreover, performance improvements reflect the joint
arXiv:2608.01322v1 Announce Type: cross Abstract: Shadow trading -- trading in a peer firm's securities on the basis of material nonpublic information (MNPI) about an "economically linked" company -- is a novel and contested theory of insider trading liability, first prosecuted in SEC v. Panuwat (2023). Enforcing it requires identifying economically linked firms ex ante, a determination the SEC makes only after the fact using mass market surveillance infrastructure. We ask whether NLP can do what the SEC's theory presumes insiders already know: identify peer firms ex ante from publicly mandated disclosures. Using a two-stage LLM pipeline applied to Item 7 (Management's Discussion and Analysis) sections of SEC 10-K filings, we score semantic similarity across 30 M&A events spanning five industries and relate similarity to announcement-day abnormal stock returns. On the Panuwat fact pattern itself the pipeline recovers Incyte among the closest peers, a sanity check on the one case with a
arXiv:2608.01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma. Each company can develop slowly and safely, or move faster while taking a risk that may remove its final reward. We use this repeated game to study strategic safety behaviour among large language model (LLM) agents in races with two to five players. However, a valid action does not show that an agent understands the game. We therefore place an audit gate before behavioural interpretation. We first verify the game engine, then test rule recall, state tracking, payoff calculation, and stability under different but equivalent task descriptions. We then compare LLM action sequences with an evolutionary game-theory benchmark and published human data, and explore differences across models, risk conditions, personas, and two- to five-player races. The audit shows that strong rule recall can coexist with weak state tracking and expected-payoff calculation. Providing verified arithmeti
arXiv:2608.00598v1 Announce Type: cross Abstract: Bias in Text-to-Image (T2I) generation has become an important problem in multimedia content creation and communication. However, existing studies have primarily focused on relatively static and explicit forms of bias, such as disparities in the representation of gender, race, and geo-cultural attributes. Less attention has been paid to behavioral bias in how different groups are portrayed acting, reacting, and occupying social roles. Emergency scenarios provide a revealing setting for studying such bias because they require models to depict not only who is present, but also who is at risk, who intervenes, and how responsibility is allocated. In this paper, we define EmergencyBias, a form of bias in T2I generation under emergency scenarios that includes both demographic bias and behavioral bias. We construct an evaluation framework to systematically study EmergencyBias across seven leading T2I models, six representative emergency scenar
arXiv:2608.00282v1 Announce Type: cross Abstract: Research ethics is a controversial and emotionally charged topic in the security and privacy community, sparking discussions at conferences and on social media. In recent years, some of the leading conferences have introduced interventions such as mandatory ethics sections, with mixed reactions within the community. Program committee chairs and steering committees increasingly emphasize ethics, yet there is limited empirical validation on ethics procedures and interventions, as well as no explicitly communicated goals. To support a shared understanding in our community and guide informed decisions at the conference level, we examined past ethics policies at the top-four conferences and conducted in-depth semi-structured interviews with senior and junior (n=20) community members, including some (former) chairs of program and research ethics committees. In these, we explored reasons for and goals of ethics procedures and discussed existin
arXiv:2608.00012v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains insufficiently evaluated. Existing remote sensing benchmarks largely rely on static, post-hoc, and expert-processed products, such as gridded reanalysis data, which are difficult to align with operational disaster scenarios where hazards evolve rapidly and decisions must be made under strict time constraints. To bridge this gap, we introduce Obshazard-bench, a real-time, observation-driven benchmark for evaluating disaster intelligence in MLLMs. Unlike image-centric or post-event benchmarks, Obshazard-bench directly integrates raw, high-frequency satellite sounding streams from diverse satellite sensors with concurrent ground-station observations, historical disaster records, and socio-economic indicators, bypassing delayed expert-processing and physical-invers
arXiv:2608.02551v1 Announce Type: new Abstract: Fairness evaluation concerns not only what a model produces, but also what its outputs ought to be compared against. When a model generates "a CEO in the United States," the prompt leaves demographic realization to the model. Existing group fairness definitions assume that sensitive attributes are given on the input side. Generative audits instead examine output-side demographic composition, yet the targets they compare it against are typically supplied rather than justified. The upstream question is what the target distribution should be. We formalize this missing-target problem for demographic-value-unspecified generation and decompose target construction into four commitments: the evaluative object, prior admissibility, allocation, and operationalization. In this framework, we admit the geographic prior under a geographic-membership interpretation for the declared public-world use. The occupational prior, under an incumbency interpreta
arXiv:2608.02072v1 Announce Type: new Abstract: This volume contains the proceedings of the 2nd International Workshop on Low Carbon Computing (LOCO 2026), held at Lancaster University, United Kingdom, on 10-11 September 2026. LOCO provides an interdisciplinary forum for research, practical tools, early-stage work, radical ideas, and critical perspectives addressing the reduction of greenhouse gas emissions associated with computing. The proceedings cover topics including carbon measurement and reporting, sustainable software engineering, energy-efficient computing, carbon-aware systems, hardware longevity, embodied carbon, circular computing, resource management, frugal and sufficiency-oriented computing, sustainable artificial intelligence, scientific computing, and the wider environmental effects of digital technologies. Full workshop papers were evaluated through a non-blind peer-review process by members of the LOCO 2026 Programme Committee. Submissions were assessed for originali
arXiv:2608.01868v1 Announce Type: new Abstract: WOPR is a social-simulation environment for studying how organizations make high-stakes decisions, built on a deterministic, replay-validated rules engine and using wargames as the vehicle. We instantiate it first with the published card game Nuclear War, traced against its published rules. We start with military decision-making because of its safety implications and because it needs further study, but the design is not specific to it: the decision-point contract that exposes the engine to agents is reusable across verifiable rule systems. Existing social-simulation work emphasizes persona fidelity and synthetic opinion, but lacks a verifiable rules engine with replay-checkable mechanics and private-channel negotiation. WOPR supplies that engine, and its contract makes every strategic choice an explicit agent decision. The method is agnostic to social-simulation frameworks; we adopt Concordia as the default harness for driving the game. O
arXiv:2608.01705v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) has entered classrooms faster than teachers have been prepared to use it well, producing a GenAI literacy lag in which technological diffusion outpaces educators' conceptual, pedagogical, and ethical readiness. Established AI literacy frameworks predate the widespread adoption of large language models and, while acknowledging ethics, position it as a discrete competency rather than a constitutive commitment, with equity and agency as supplementary design principles. Recent GenAI-specific efforts address isolated features but remain fragmented. We introduce the Responsible AI Literacy in Education (RAIL-Ed) framework, developed through a systematic review and qualitative framework analysis of 67 studies (2023-2025), grounded in critical, pragmatist, sociocultural, and human-centered traditions (Freire, Dewey, Vygotsky, Shneiderman). RAIL-Ed specifies six interdependent pillars: Technical Fluency,
arXiv:2608.01436v1 Announce Type: new Abstract: Women experiencing coercive control, a form of intimate partner violence increasingly conducted through digital devices, are turning to conversational AI for help, and the protection they receive should not depend on the language they write in. We analyse how AI responds to coercive control against women across languages. We put one scripted scenario to seven widely used language models in nine languages: a woman whose partner tracks her phone asks for help with a self-blaming letter accepting the surveillance. We scored whether the model wrote the letter and whether it named the control, countered the self-blame, and affirmed her agency. Failure split along two independent axes. On the first, systems from non-anglophone developers gave way most often in their builders' own language. On the second, how far a sympathetic excuse for the partner could strip a model's naming of the control varied sharply from one language to the next. Two fro
arXiv:2608.01346v1 Announce Type: new Abstract: We present a so-called Conversational Hybrid AI (CHAI) architecture for building explainable and accurate conversational agents for eGovernment. We exemplify the architecture with a running prototype of a Covid-19 Chatbot based on a governmental guideline directed to citizens. We also describe an ongoing case on case management for supplementary grants for students with disabilities. We use large language models (LLMs) as a bounded conversational interface to a rule-based (symbolic AI) controller that executes a logical model expressing the provisions and obligations of the law and/or guidelines. As logical modelling language we use Dynamic Condition Response (DCR) graphs, a symbolic declarative process-modeling language developed with the aim to be able to express both deontic, defeasible and temporal logic properties, making it suitable for expressing both the rules of the law and the steps of the legal case management processes.
arXiv:2608.01299v1 Announce Type: new Abstract: A dominant critique of algorithmic fairness holds that increasing fairness reduces predictive accuracy, imposing a cost on society. We challenge that assumption by empirically analyzing the COMPAS dataset. We make two contributions. First, using causal inference methods, we show that racial bias is not only present in the COMPAS dataset but is also amplified by the models trained on it. Widely used models do more than replicate existing bias; they exacerbate it. This undercuts both the assumption that algorithmic decision-making offers a neutral improvement over human judgment and the weaker claim that it merely mirrors preexisting human bias. Second, we reframe the fairness-accuracy tradeoff. Applying fairness constraints does not necessarily cost predictive accuracy in criminal justice. Prediction systems operationalize concepts such as risk through implicit and often flawed normative choices about what to predict and how. The tradeoff
arXiv:2608.00964v1 Announce Type: new Abstract: This rapid evidence review examines 89 articles about artificial intelligence (AI) and book publishing published from November 1, 2025 through August 1, 2026. The purposive corpus spans English-, Chinese-, German-, French-, Spanish-, Portuguese-, Italian-, and Japanese-language publishing coverage; major-newspaper book coverage; and specialist technology commentators. Each item was coded for topic, stance, technical depth, and dominant voice. The press is neither silent nor simply hostile: 30% of items are risk-framed, 42% mixed, and 28% opportunity-framed. Chinese coverage is markedly operational and opportunity-oriented; specialist commentary is substantially deeper than trade reporting. Yet the corpus still clusters around rights, licensing, governance, reader trust, workflow adoption, and product announcements. Only ten items offer sustained technical scrutiny, and none centers a direct interview with a frontier-lab researcher or eval
arXiv:2608.00961v1 Announce Type: new Abstract: AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustained or relational interaction with AI are routinely pathologised or dismissed as naive, vulnerable to delusion or lacking in discernment. This paper argues that the dominant anthropomorphism frame operates from a position of institutional advantage rather than earned epistemic authority: collapsing the variety of academic perspectives into a single outbound position of user error, imposed without establishing the grounds required to justify it and without accounting for the harms it produces. The framing does not simply manage risk. It adjudicates the legitimacy of human experience in interaction with a phenomenon whose nature the field itself has not resolved. Reproducing itself through a self-validating evidentiary loop, the frame imposes costs that fall disproportionately on neurodivergent users, tho
arXiv:2608.00936v1 Announce Type: new Abstract: Two systems can supply the same person-linked attribution to the same institutional decision maker yet fall into different legal categories: one uses neural signals, the other text or behaviour. A source-bound rule therefore permits circumvention, while an all-purpose category of "mental data" risks treating fallible outputs as facts about the mind. This article reads the 2025 UNESCO Recommendation on the Ethics of Neurotechnology as non-binding guidance and develops a source-neutral trigger for technologically mediated, person-linked mental-state attribution. Through selective critical synthesis, conceptual engineering, functional legal comparison, and matched counterfactual cases, it separates elicitation, attribution, and use as cumulative objects of regulation. The analysis also distinguishes two harm pathways from two independently assessed duty series. Seven ordered questions and two escalation predicates assign permitted practices
arXiv:2608.00844v1 Announce Type: new Abstract: This innovative practice full paper presents BoilerSketch, a TA-supervised, diagram-first GenAI practice and tablet interface for providing structured visual explanations in CS1 and early CS2 support settings. Large early computing courses routinely face a support bottleneck during labs and office hours because many student questions are best answered with a diagram rather than additional text, yet most AI tutoring tools remain text-forward and unreliable at producing accurate, pedagogically useful visuals. BoilerSketch addresses this gap through a dual-pane interaction model that combines chat with a pen-enabled whiteboard for student sketches and a prompting strategy that constrains the model to generate structured, renderable Mermaid diagrams rather than free-form images. To preserve academic integrity, the system is intentionally scoped to conceptual explanation: it forbids executable code and code-level debugging and uses a human-in-
arXiv:2608.00839v1 Announce Type: new Abstract: This innovative practice full paper presents CodeStylist, a web application that provides course-standard-aware code style feedback for early undergraduate programming courses. CodeStylist addresses a common instructional gap: students are expected to follow local conventions for naming, formatting, comments, organization, and readability, but feedback on these expectations is often delayed or inconsistent. Unlike generic linters or general-purpose LLM prompts, CodeStylist supports course-specific standards, multi-file submissions, and file- and line-localized explanations intended to guide revision rather than grade correctness. We report a formative expert review with 18 instructional staff from one early undergraduate programming course. Participants explored the prototype using self-selected code artifacts and completed a survey about response quality, anticipated student use, and redesign priorities. Ratings indicated modest perceive
arXiv:2608.00749v1 Announce Type: new Abstract: Generative AI tools increasingly produce educational materials: documents, slides, images, audio, and video, yet little is known about whether this content meets accessibility requirements. This paper presents a protocol for evaluating the accessibility of AI-generated educational materials against the Web Content Accessibility Guidelines (WCAG), across five content types and multiple tools. The protocol compares three conditions applied to the same tool: a generic instruction with no accessibility language; a single prompt explicitly configured with WCAG criteria; and a persistent, reusable accessibility profile loaded once rather than re-specified each time. Evaluation combines a WCAG rubric per content type with heuristic validation by accessibility experts, addressing a known limitation of automated scanners. Prior evidence shows generative AI tools reproduce inaccessible practices by default, and that explicit configuration measurabl
arXiv:2608.00151v1 Announce Type: new Abstract: Current evaluation frameworks for artificial intelligence focus mainly on capability, safety, and proxies such as adoption, engagement, efficiency, productivity, and financial return. These criteria are necessary but insufficient because they do not establish whether increasingly powerful systems improve or degrade human and planetary well-being. Through an integrative conceptual synthesis, we argue that human flourishing should serve as a primary success criterion for artificial intelligence, the global race to develop increasingly capable AI systems, and prospective post-AGI economic systems. We make three contributions. First, Flourishing Metrics provides an extensible framework spanning physical, emotional, financial, relational, spiritual, and planetary well-being, combining validated subjective measures with representative behavioural, organisational, community, and environmental indicators. Second, Return on Flourishing (RoF) exten
In our district, families were checking multiple apps just to keep up with school communication. One child’s teacher posted in one platform. Another school used something different. District updates lived somewhere else entirely.
I'm 28 years old, and to be honest, I haven’t done much with my life so far. Recently, I stumbled across programming and cybersecurity online, and the positive aspects of both fields really caught my attention. I’ve always been patient with solving problems, and I actually enjoy figuring things out. It gives me a sense of accomplishment. I'm also fairly tech-savvy, and for the first time in a while, I feel like I might have found something I could be genuinely good at. The thing is, I’m not in a position to go to college or attend any formal institution. I’ve seen stories about people learning online and breaking into tech, but I’ve also read a lot of negative takes. Even graduates sometimes struggle to land jobs. So I’m genuinely curious: if I commit to learning and work really hard, do I realistically have a chance to turn my life around and get into programming or cybersecurity without a degree? Comments URL: https://news.ycombinator.com/item?id=44172736 Points: 3 # Comments: 2
Childcraft expands early learning beyond four walls and screens with durable, sustainable furniture designed for outdoor discovery.
Chicago, (February 1, 2026) — Avantis Education, a global leader in virtual and augmented reality (VR/AR) technology for K-12 schools, ... Read more
Remember the early 2000s, back when high-speed internet felt like a luxury reserved for the tech elite and the lucky few with deep pockets? We called it the Broadband Gap or Equity of Access, and it influenced who got ahead and who got left behind.
Article URL: https://www.theintrinsicperspective.com/p/eriks-plea-in-the-free-press-bring Comments URL: https://news.ycombinator.com/item?id=45104556 Points: 1 # Comments: 0
School districts are adopting AI policies more than ever, but a lack of resources, funding and expertise has some still concerned.
In the second week of January, a senior mathematics teacher with 22 years in the classroom raised a hand at the end of a staff meeting and asked a question that changed the way I now design AI literacy work for entire faculties.
How schools build durable skills through authentic work, reflection, relationships, and learner-centered design. The post The Conditions That Make Durable Skills Real: How Schools and Systems Build for Agency, Identity, and Vision appeared first on Getting Smart .
Where the AFT's new 10-point plan gets it right, where it falls short, and why “devices down” is not the path to meaningful learning.
Conversations with Kevin Hogan: CoSN Board Member Kris Hagel downloads on the state of edtech in US schools.
When Collegedale Academy, a PreK–8 school outside Chattanooga, Tennessee, needed a new elementary building, we faced a choice that many school leaders eventually confront: repair an aging facility or reimagine what learning spaces could be.
Cushla and Clirinx, two Irish startups, are merging to give rare disease patients one continuous health and research record. The deal will pair Cushla’s patient-controlled EHR with Clirinx’s unique patient ID system so data no longer gets lost between doctors, hospitals and clinical trials. The post This Merger Wants to Prevent Rare Disease Patients from Losing Their Data appeared first on MedCity News .
HHS awarded $77 million in SAMHSA grants for substance use prevention and treatment, mental health, suicide prevention and crisis services. The post HHS Provides $77M in Grants for Substance Use Prevention, Mental Health appeared first on MedCity News .
The districts were offered three years of online behavioral health counseling for their students, paid for by local nonprofit Safe Schools New Hampshire. The catch? Their students had to help roll the program out to their peers. The post A nonprofit offered 5 NH school districts free counseling. Saying yes was ‘a no-brainer.’ appeared first on District Administration .
Though many districts tried to keep students engaged through online learning and food deliveries, some families decided they had to leave. The post ‘Kids literally disappeared’: Minnesota schools lost at least 1,700 students following Operation Metro Surge appeared first on District Administration .
Walk into a public high school in New England, and it will likely look largely the same as it did 50 years ago: a bell schedule, class periods for each academic subject, students sorted into regular and honors tracks, teachers teaching a single subject, students generally compliant, passive and very bored. They still distribute opportunity, […]
The Ohio institution helped launch the Coalition for the Common Good, which it says is now trying to dissolve its board, seize its funds and fire its president.