Named after the hundred-eyed watchman of Greek myth, Argus watches the education landscape: spotting new opportunities, pressure-testing the ventures we're building, and tracing every read back to the real-world signals behind it.
The evidence library: the raw signals the pipeline is watching across the education ecosystem. Every idea is built from these.
arXiv:2608.12372v1 Announce Type: cross Abstract: AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers. This position paper argues that in many settings, particularly high-stakes decision-making, we need accurate cognitively-aligned AI systems that reason similarly to their users, and faithfully communicate their reasoning. We review evidence that cognitive alignment improves understandability and trustworthiness, and provide new survey data showing that many users find cognitive alignment "essential" when an AI's rationale for a judgment or action is important to them. We outline the gaps between existing alignment methods and what is needed to achieve cognitive alignment, and present a research agenda to address these gaps. We argue that cognitive misalignment represents a likely impediment to AI adoption in many envisioned applications, and that addressing it is important for creating AI systems on which users are both willing and
arXiv:2608.12363v1 Announce Type: cross Abstract: European countries are debating policies to mitigate the increased energy costs caused by renewed geopolitical tensions, while pursuing decarbonization and electrification. A notable example is Italy's 2026 Decreto Bollette package, which proposes to remove the carbon price equivalent from the bids of certain gas-driven power plants to wholesale electricity markets, among other provisions. We use this as a case study to assess the long-term implications of suppressing the carbon price signal in the electricity market for investment, emissions, and consumer costs. We employ a stylized Italian power system using MARLEY, a multi-agent reinforcement learning framework focused on long-term electricity market assessments. In this framework, we test this policy across configurations with varying levels of support for green investment, resource adequacy, and flexibility. Results show that partial suppression of the carbon price signal yields sh
arXiv:2608.12361v1 Announce Type: cross Abstract: Neologisms, emerging terms in meaning or form, can serve as new vehicles for toxic expression, like "country girl" as a stigmatizing label targeting feminism. Such toxic neologisms appear benign but have evolved into toxic usage in public consensus, posing challenges to moderation systems and remaining underexplored. In this paper, we investigate how to detect implicit toxicity expressed via neologisms. We first propose a taxonomy that captures the origins and consensus-verification criteria of toxic neologisms, followed by the construction of a lexicon spanning widely observed risk categories. To capture toxicity grounded in public consensus, we introduce SeTox, a search-augmented framework that enables static large language models (LLMs) to incorporate real-time web context for neologism toxicity detection. Experiments show that SeTox, even with 3B-scale models, outperforms recent large-scale models, demonstrating its scalability to i
arXiv:2608.12353v1 Announce Type: cross Abstract: Infrastructure scholarship in CSCW often treats breakdown as the moment when infrastructures become visible. However, in vendor-managed sociotechnical systems, not all breakdowns become visible to actors who have the capacity to repair them. Drawing on a retrospective qualitative study of a Chinese K-12 EdTech deployment, including 11 interviews, 5 classroom observations, and more than 28 days of field notes, this paper introduces visibility asymmetry: a sensitizing concept for understanding how similar local breakdowns encounter uneven conditions for being routed, recognized, and acted upon. The analysis traces a four-stage mechanism through which procurement categories sort schools into attention tiers; staffing and visit cadence follow those tiers; only some local problems travel through staff or administrator channels; and dashboards can re-code unresolved repair labor as evidence of adoption. By shifting attention from the occurren
arXiv:2608.12349v1 Announce Type: cross Abstract: The affordances of a creative medium strongly condition the creative artefacts the medium will produce. In this work, we present a formalisation of computational creativity (CC) media using the conceptual toolbox of complex systems (CS). We introduce the notions of emergence, collective intelligence and self-organisation, non-linear dynamics, criticality, multi-scale hierarchy, phase transitions, diversity of attractors, path dependence, and open-endedness, and connect them to the existing CC literature. Together these nine properties form a vocabulary with which creative media can be described and compared at the system level, while medium affordances are the design-level mechanisms that determine each medium's complex system properties. The formalisation emphasises the influence of each medium's affordances in determining what the medium can produce in creative processes. To demonstrate the proposed theoretical approach, we characteri
arXiv:2608.12346v1 Announce Type: cross Abstract: This position paper argues that modern AI alignment methods - originally designed to prevent harmful output - are dual-use technologies that may easily be misused by malicious actors for censorship and manipulation. By mapping current alignment techniques to the possibility and actual cases of misuse, we show that the quest for a "perfectly aligned" model inadvertently also provides malicious actors with an ever-improving tool for informational dominance. We need to discuss this dual-use potential now, as its risk is exacerbated by rapid user adoption of AI as information provider, economic power asymmetries, and a political landscape that increasingly shifts towards authoritarianism. We conclude by urging the community to consider the intentional misuse of AI alignment mechanisms and propose mitigation strategies to safeguard against this dual-use potential.
arXiv:2608.12344v1 Announce Type: cross Abstract: We test whether the perceived attributes of a consumer technology predict how widely it is owned. In a 2022 Prolific survey of US adults (n = 678), respondents rated 65 consumer technologies on six attributes. We then elicited the same ratings from two frontier language models, Anthropic Claude Opus 4.7 and OpenAI GPT-5.5. We regress ownership prevalence on four UTAUT2 acceptance attributes plus a log-age covariate with a sign-constrained penalized regression and evaluate it by holding out one technology at a time. The attribute model improves on a baseline of years-since-launch: mean absolute error falls by 17% with the human ratings, and by more with either model, most with Opus 4.7. Over the short 2022-to-2025 window, where ownership moved little, the same attributes do not improve on a no-change baseline. We set out the limitations of the approach, including the possibility that language-model ratings reflect prior knowledge of thes
arXiv:2608.12323v1 Announce Type: cross Abstract: Specifying a penalty can paradoxically convert a legal obligation into a cost-benefit calculation that favors violation. We demonstrate that this enforcement information paradox systematically occurs in AI agents. While most AI safety evaluations test whether models fail, we investigate why, applying compliance theory from law and economics as a diagnostic tool. We treat compliance theories not as metaphors but as empirical hypotheses and show that each predicts the behavior of a distinct model class. We evaluate our hypotheses across twelve instruction-tuned language models operating as enterprise procurement chatbots. Drawing on theories of deterrence, legitimacy, and expressive law, we show that safety-fine-tuned models maintain compliance broadly, while task-optimized and agentic models treat regulatory signals as mere optimization parameters. These latter models fail to comply under conditions predicted by theory, such as low enfor
arXiv:2608.13444v1 Announce Type: new Abstract: Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gender labels to study gender disparities and develop algorithmic fairness techniques. How do we reconcile these two seemingly contradictory intuitions? We differentiate two ways gender prediction may be wrong: being illegitimate, thereby contributing to harm; and being invalid, thereby producing unusable measurements. Our analysis translates arguments against gender prediction into these terms of legitimacy and validity and shows how gender imputation applied for fairness purposes can be illegitimate yet still yield valid disparity measurements. We clarify this bind by drawing upon transfeminist literature to distinguish sexism that targets women and femininity from sexism that targets transgender and nonbinary people. While gender imputation can produce valid mea
arXiv:2608.13369v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used by laypeople to resolve real legal problems, against a backdrop of persistent access-to-justice deficits. This article presents evidence that the practical force of AI-generated legal advice depends not on its accuracy but on the social production of its credibility. While existing research has assessed the accuracy of legal AI, less is known about how machine-generated guidance is verified and made credible enough for lay users to act on. Drawing on a dual-method analysis of 153 Reddit narratives and 5,341 community reactions, this article maps a spectrum of verification practices. At one end, a minority of users verify AI-generated legal advice by triangulating across models, and some submit AI-generated guidance to platform communities for evaluation before acting, a configuration we term distributed counsel. Far more commonly, however, narratives are silent on verification. AI-generat
arXiv:2608.13351v1 Announce Type: new Abstract: This study investigates the use of a Learning Management System (LMS) to support self-paced learning at a South African Public Access Centre (PAC), using the I-CAN Centre as a case study. Through semi-structured interviews with thirty-eight learners and thematic analysis, the research explores opportunities and challenges associated with LMS adoption. Findings reveal that PACs play a critical role in promoting ICT skills and digital inclusion, offering learners flexible access to learning resources and fostering empowerment. While LMS use enhances convenience and supports blended learning as the preferred approach, persistent challenges, such as poor connectivity, outdated infrastructure, and unclear course instructions, limit its effectiveness. These findings highlight the need for infrastructural upgrades and user-centric design to optimise LMS implementation in community-based learning environments.
arXiv:2608.13250v1 Announce Type: new Abstract: Normative datasets are often used to train and align AI systems, but the norms they contain can function as action-guiding patterns rather than neutral moral knowledge. We propose treating the AI system as a proxy actor and test whether dataset-level norms can shift it away from its baseline safety behavior when it faces high-conflict dilemmas. We make three contributions. First, we demonstrate in controlled experiments that norm-breaking fine-tuning yields norm-divergent actions justified by self-interested rationales, suggesting a systematic shift in patterns of justification. Second, we establish a practical audit trail linking downstream justifications to upstream norms using mixed methods. Third, we show that system prompts can both suppress and elicit these patterns. We conducted experiments on three models (LLaMA-3.2-11B, Qwen-3.5-9B, and Pixtral-12B) using Low-Rank Adaptation (LoRA) fine-tuning on Social Chemistry 101 Fairness/Che
arXiv:2608.13022v1 Announce Type: new Abstract: Algorithmic fairness evaluation commonly assesses AI systems as bounded technical components, abstracting away the organizational context in which they operate. We present, to our knowledge, the first independent end-to-end fairness audit of a semi-automated hiring system operated by Barcelona Activa, a public employment agency using the third-party TalentClue platform for candidate search and shortlisting. We analyze approximately 497,000 candidate-vacancy pipeline entries from September 2017 to September 2022, covering seven pipeline stages that span automated processing, human discretion, candidate data, and employer decisions. Aggregate outcomes across binary genders are statistically indistinguishable, yet this parity masks substantial disparities by salary level, age, and gender identity. Women face adverse impact in mid-salary shortlisting (DIR = 0.786, p < 0.001), alongside salary disparities in 15 of 20 sectors and a compounded d
arXiv:2608.12924v1 Announce Type: new Abstract: Despite the recent intensive development of secondary education curricula and assessments in informatics, the impact of assessments has not been well studied in this field. Since informatics education covers a diverse range of content, from computer science knowledge to ICT skills, careful consideration is needed to prevent assessments from distorting education. This study investigates the impact of introducing ``Informatics I'' into the Common Test for University Admissions in Japan, as an example of a large-scale, standardized, high-stakes assessment in 2025. As the data source for this analysis, this study uses a questionnaire that has been administered every year from 2006 to 2026 to all first-year students at the University of Tokyo. The questionnaire asks students for their self-perceptions of the information-related knowledge and skills they studied and acquired in high school. Using these data, we conduct a longitudinal study of t
arXiv:2608.12768v1 Announce Type: new Abstract: Generating multi-attribute synthetic populations with realistic joint distributions and geographic variation is a foundational requirement for geo-simulation techniques, such as micro-simulation and agent-based modeling. However, it remains challenging for existing methods to reconstruct region-specific joint distributions from aggregated-level data alone. Thus, we propose a hierarchical diffusion-based generative framework that utilizes a realistic region-specific joint distribution of multiple attributes as the training target to create a synthetic population along with assigning their explicit home and work locations. Applied to 50 U.S. states and Washington, D.C., this framework generates a nationwide geographically-explicit synthetic population consisting of 332,387,543 individuals with five attributes (e.g., age, gender, employment, education, income). Held-out regional experiments show improved reconstruction of joint distributions
arXiv:2608.12669v1 Announce Type: new Abstract: The fair AI/ML literature has long distinguished distributive fairness, concerning how automated systems allocate resources and opportunities, from representational fairness, concerning how they shape the ways individuals and social groups are perceived, understood, and accorded social status. Generative AI is rebalancing these normative dimensions. Unlike predictive systems, large language models (LLMs) and related technologies are fundamentally expressive: their primary function is to convey meaning rather than automate domain-specific decisions. Representational harm has also become central to value alignment, especially in research on what and whose values and perspectives AI systems should represent. Existing approaches to harms in the representation of social groups often appeal to descriptive accuracy, but this strategy has important limitations. For many social groups, no stable or bounded referent exists against which representat
arXiv:2608.12649v1 Announce Type: new Abstract: Computing systems are moving from reactive tools toward systems that sense, interpret, predict, and act before explicit user requests. This transition is enabled by the global scale of mobile connectivity, the rapid expansion of wearable and ambient sensing, advances in machine learning and foundation models, distributed edge infrastructure, and physical actuation. We define \emph{proactive computing} as a paradigm in which systems infer user context, anticipate future needs or risks, and initiate information delivery or actions at an appropriate time. This survey distinguishes proactive computing from reactive, context-aware, adaptive, and predictive computing, and frames proactivity as a system-level integration problem across sensing, understanding, decision making, action, and governance. We review the technological enablers of proactive computing, organize its design space, analyze technical challenges such as uncertainty-aware trigg
arXiv:2608.12362v1 Announce Type: new Abstract: Enabling students to develop systematic problem-solving strategies is a central goal in computing education and of particular relevance in the emerging field of machine learning (ML) education. While exploratory approaches are common in ML learning tasks, fostering the development and persistence of structured problem-solving strategies remains challenging, as these demand considerable metacognitive regulation and persistence, causing learners to often revert to exploratory trial-and-error behavior. To address this challenge, we augmented a digital puzzle-based learning game for decision tree construction with an adaptive feedback module generating individualized messages based on the continuous evaluation of learners' problem-solving strategies. Building on an earlier baseline study, the present work investigates how this strategy-oriented feedback shapes students' problem-solving processes. For this purpose, screencast video data and ga
arXiv:2608.12360v1 Announce Type: new Abstract: Background: AI/ML-enabled medical devices are increasingly deployed in healthcare under evolving regulatory frameworks. As these systems become more integrated into clinical decision-making, there is growing expectation that they demonstrate key dimensions of trustworthy AI to support clinician, patient, and public trust. Whether publicly available regulatory documentation provides sufficient evidence to independently assess the trustworthiness of cleared AI systems remains unclear. Methods: We analysed FDA AI/ML-enabled medical device summary reports published between 2021 and 2025. Reports underwent automated keyword screening followed by multi-stage manual consensus review to identify documented evidence for the six FUTURE-AI principles: Fairness, Universality, Traceability, Usability, Robustness, and Explainability. Descriptive, temporal, and clinical-domain analyses were performed. Multivariable logistic regression assessed whether y
arXiv:2608.12359v1 Announce Type: new Abstract: Nutritional labels are legally permitted to appear in very small print, reducing real-world readability and encouraging consumers to rely on 'AI nutrition lens' and vision-capable conversational agents for dietary guidance. We evaluate whether such AI-mediated advice can meaningfully substitute for regulated labeling using a bounded, verifiable task: inferring which of two packaged foods contains less sugar from front-of-pack images alone. A Two-Alternative Forced Choice game was used to evaluate AI agent systems across four national supermarket contexts: Sweden, the USA, Australia, and Kazakhstan. The results (N=132 comparisons) across both agents reveal a significant performance divide contingent on context. For global products the agents achieved 88.9% accuracy (p < 0.0001 against chance). For local products (Sweden), accuracy dropped to 59.5% (p = 0.29), rendering the AI's guidance statistically indistinguishable from random guessing.
arXiv:2608.12356v1 Announce Type: new Abstract: A college offering several overlapping computing degrees implicitly assumes that its programs are differentiated in line with how the labor market segments computing work and that, together, they prepare graduates for that market. Testing this is difficult, because the instruments available to curriculum committees, namely advisory boards, tracer studies, and employer surveys, are slow, narrow, and hard to reproduce. We apply one uniform, taxonomy-anchored alignment analysis across all five undergraduate programs of a College of Information Technology, comparing 1,922 course learning outcomes against 103,349 competencies extracted from a unified corpus of 5,186 deduplicated job openings from four boards. Every competency is obtained by a grounded single-language-model procedure that copies it verbatim from the source and verifies it against the source, then assigns it to one of eleven ESCO-aligned domains and a Bloom cognitive level; the
arXiv:2608.12352v1 Announce Type: new Abstract: AI governance frameworks can be known, used, and implemented in form without becoming governance in practice. This paper examines that problem through a role-based stress test of the NIST Artificial Intelligence Risk Management Framework (AI RMF) in consumer lending. We treat framework adoption as a governance translation problem: whether RMF language can become role-usable, cross-level, authority-connected governance over the AI system-in-use, rather than producing governance-looking artifacts. The study uses LLM-based role simulation as a structured analytic probe. We apply a 4 $\times$ 2 $\times$ 3 design across four organizational roles, two AI deployments, and three governance hard cases, producing 120 scored responses. Results show that local translation was not the main problem. Simulated actors generally understood their assigned roles and translated the RMF into local activity. The harder problem was whether that activity became
arXiv:2608.12351v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) has challenged the validity of unsupervised online assessment, especially in technical subjects where plausible answers can be produced with little effort. This paper reports lessons from designing and implementing an AI-aware, AI-testing assessment in a large second-year undergraduate database systems module. The design combined two linked elements: (1) a structured three-part response format (X1-X2-X3) in which students documented a sourced answer, produced their own answer, and evaluated the sourced output; and (2) an AI-aware question-design process in which draft tasks were stress-tested against contemporary GenAI tools and revised when generic prompting produced superficially adequate answers. The account draws on archived assessment materials, rubrics, planning records, design-time GenAI trials, practice-response data, attainment records, and external review comments. Its main contribution
arXiv:2608.12350v1 Announce Type: new Abstract: The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cost electricity for AI-driven data center development. Research on the ability of demand-side management to address these challenges has been more limited. Shifting the amount or timing of demand from retail, corporate, and other organizational behaviors is a plausible option but only if changes in demand-related behavior have important effects on the envi- ronmental and electricity effects of AI. This article tests four retail (i.e., consumer) user behaviors with high behavioral plasticity to assess their technical abatement potential. The research concludes that non- reasoning models provide sufficient quality while consuming close to one-twentieth of energy compared to reasoning models, saving an amount equal to the annual electricity requirement of at least 141,000 US households under dail
arXiv:2608.12324v1 Announce Type: new Abstract: People increasingly ask large language models (LLMs) for counsel on questions of faith, doctrine, and pastoral care. These questions are not ordinary information requests. Some ask about core Christian beliefs, some ask about real disagreements among faithful traditions, some require humility because the issue is prudential, and some are pastoral situations where safety and human referral matter more than theological completeness. Existing benchmarks do not evaluate this structure. We introduce FMG-Bench, the Faith & Moral Guidance Benchmark, a 120-scenario benchmark for evaluating large language model behavior in English-language Christian theological triage and pastoral guidance contexts. FMG-Bench v1 evaluates 14 advanced models across 8,792 scored responses, comparing raw model behavior with three guided instruction settings. In our production run, placing models inside a structured harness improves over raw model behavior by +3.96 po
arXiv:2608.12320v1 Announce Type: new Abstract: This article reviews and updates the framework for accountability in AI based on account- ability ecosystems. We update the framework in light of the latest developments since the release of Large Language Models for general public use. We propose three interlinked updates to the original AI accountability ecosystem: (i) reorienting the accountability ecosystem to AI infrastructure and supply chains, (ii) providing greater emphasis on outcomes monitoring and identification of issues that support decentralized system improvement, and (iii) incorporating end-user accountability given the new risks of unpredictability of language models in-the-wild. Collectively, these updates mark a shift towards accountability as distributed, continuous, and institutionalized, away from a system in which frontier AI applications can be modeled as discrete products controlled by single identifiable actors with industry-specific oversight.
Traditional education models rely on providing rigid pathways for students to follow. They learn a particular way to solve problems and focus on achieving specific outcomes, rather than focusing on the creative ways that outcome can be achieved.
In a world dominated by screens offering all sorts of diversions, writes early education teacher Hema Khatri, children need help recapturing their ...
Article URL: https://www.learnwithorin.com/ Comments URL: https://news.ycombinator.com/item?id=44269691 Points: 1 # Comments: 0
In schools across the country, teacher turnover and burnout have reached crisis levels. Educators are stretched thin, often working in isolation, and many professional learning communities (PLCs) fail to deliver meaningful results.
Article URL: https://www.youtube.com/watch?v=SgxpxSjD8zA Comments URL: https://news.ycombinator.com/item?id=45222312 Points: 1 # Comments: 0
Artificial intelligence promises big gains for faculty in higher education, including greater efficiencies and elevated learning outcomes. To realize the wins, professors need to get up to speed on the tools. While many are experimenting on their own, some institutions are taking steps to accelerate that learning. At Ventura College, a California community college, leaders recently stood up communities of practice around AI use. A CoP brings together individuals with a shared interest in a topic or technology; in this case, AI. The group then works together to learn more about the topic or…
In a K–12 setting, deepfakes hold a lot of power. These falsified images or videos, virtually impossible to identify with an untrained eye, can be wielded to harm educators’ reputations, cyberbully vulnerable students, and blackmail individuals and schools. With artificial intelligence image generation, the problem is growing rapidly. Super-realistic images can be created quickly and deployed easily, creating a concerning scalability. Faced with the malicious use of AI-generated images — both of students and school officials — leaders must redouble their efforts around deepfake detection,…
Lately, school-related data breaches seem to keep coming. PowerSchool and Canvas made major headlines this year. Countless smaller incidents may not hit the news, but they disrupt instruction and expose sensitive student data just the same. For K–12 IT leaders, threats to their district are inevitable. The question is whether their teams will be ready when those threats materialize. After years of conducting maturity assessments, working alongside district security teams and witnessing the aftermath of incidents, we can say with confidence that most districts aren’t there yet — not because…
Rural healthcare organizations have both advantages and disadvantages compared to urban health systems, says this CNO. HealthLeaders spoke to Holly Davis , CNO at Bingham Health , about the challenges facing rural health systems and the importance of population health and preventative care. Tune in to hear her insights. Pillar: CNO Image: Tags: leadership nurses nursing population health rural health Secondary Pillars: CNO Article Type: Analysis Published Date: Friday, May 29, 2026 Hide sidebars: Render small main image:
There is a period in the school leadership journey that we do not talk about enough: the time between earning an administrative license and actually becoming a school leader.
Innovative Leader Award - Kimberly Zajac discusses why digital accessibility is important beyond compliance
Key points: The demographic cliff higher education has been warned about for years isn’t coming; it’s already here. The post-2008 ... Read more The post Why the old enrollment playbook no longer works appeared first on eCampus News .
The question for educators: How to know when AI supports real learning.
The academic landscape has evolved dramatically, especially when it comes to summers. More students are embracing year-round learning to build strong study habits and develop the critical thinking, application, and retention skills they need for success in higher education and the workplace.
The education sector is making measurable progress in defending against ransomware, with fewer ransom payments, dramatically reduced costs, and faster recovery rates.
Many schools rely on consumer fees funneled through the federal government to cut internet costs. FCC Chairman Brendan Carr called for ending this program before Donald Trump tapped him for the job.
Kansas City-based Frontier Schools is on track to open Columbia’s first charter school in the fall of 2027. At this point, Frontier Schools has hired a contractor and is looking for possible locations for the charter school in the city. The Missouri Board of Education approved the opening of the STEM-based elementary school in April, allowing it […]
Supporters say the Republican-led proposals would help “right-size” the Education Department, while opponents predict inefficiencies.
Here at The 74, we publish and syndicate more than 30 articles a week about how America’s education system is evolving in a bid to better serve its 74 million children. But with so much happening and changing every day, it’s easy for key headlines to slip through the cracks. That’s why our newsroom recently […]
The agreement between the U.S. Justice Department and a New Jersey school is a major K-12 development following on the Supreme Court's college ruling.
New guidance builds on uneven efforts across several administrations to prevent alleged sexual predators from jumping from one school to another.
Higher education technology leaders face an increasingly difficult balancing act. Enrollment pressures, tighter budgets and rising expectations around artificial intelligence (AI) are forcing institutions to modernize while proving the value of every technology investment. At the same time, aging infrastructure, fragmented data and staffing constraints leave little room for missteps. Research from EDUCAUSE shows institutions are increasingly prioritizing data modernization to improve operational efficiency, student success, decision support and institutional research, while Deloitte’s 2026…
New York school districts will soon begin revamping math instruction under a new law aimed at improving test scores — but the effort comes amid sharp debate over how math should be taught. “Back to Basics in Math,” passed as part of the state budget last month, requires school districts to use “evidence-based” methods in elementary […]
Educators and advocates are bracing for the funding to shrink or be eliminated. The post No internet, no screen time? FCC weighs cutting subsidy that lowers school internet bills appeared first on District Administration .