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:2609.02364v1 Announce Type: new Abstract: Existing Human-Robot Interaction (HRI) literature has focused on identifying and structuring errors, failures, conflicts, and knowledge issues (called in this work as contradictions) in domain-specific dialogue-based interactions. However, there is still lack of a formal computational framework to represent and define these contradictions, interoperable and usable across HRI and human-agent interaction (HAI) domains. Thus, this research project aims to capture, represent, and evaluate the notion of (1) dialogue-based collaborative interaction and (2) related contradictions in a foundational ontology. METHONTOLOGY, a systematic approach to build domain-independent ontologies was applied. In the conceptualisation stage of the presented ontology, concepts and models from Activity Theory were used. Preliminary results presented in this short article are: (i) Natural language definitions of dialogues and related contradictions in HRI, (ii) Set
arXiv:2609.02149v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evolving from conversational assistants into agents capable of operating external digital environments. Graphical user interface (GUI) agents play an important role in this transition, as many real-world workflows remain accessible only through user-facing software interfaces. However, despite recent progress on general computer-use benchmarks, domain-specific professional standard operating procedures (SOPs) remain challenging for GUI agents because they often involve implicit domain knowledge, software-specific conventions, and task-level verification requirements. We introduce OmegaUse-SOP, a human-in-the-loop SOP Engineering system for transforming human demonstrations of professional computer use into reusable SOP skills for GUI agents. Analogous to prompt engineering, SOP Engineering iteratively refines demonstrations, execution rules, and domain knowledge to convert professional SOPs in
arXiv:2609.01981v1 Announce Type: new Abstract: Mastering musical performance requires precise multisensory coordination, yet learners encounter a kinesthetic mismatch, which is a discrepancy between the internal perception of an action and the actual physiological state of the body. While multisensory Body Transformation Experiences (BTE) provide tools to bridge this gap, existing designs often focus on external correction rather than internal alignment. To address this, we propose the Somatic Alignment Mindset (SAM), a conceptual lens that integrates Taoist philosophy to shift the focus of HCI design from prescriptive feedback toward holistic embodied unity. By positioning technology as a reflective medium, SAM operationalizes the principles of Adaptation, Assessment, and Awareness to reconcile somatic discrepancies and foster deep, self-aligned musical mastery.
arXiv:2609.01976v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly embedded in organizational work, yet their errors often pass human review. Prior research locates such failures in users' capability to review LLM output or their engagement in doing so. We develop an alternative, retrieval-based account of human oversight and posit that error detection is more effective when oversight-relevant information is accessible to users at the moment of review. Across two randomized lab-in-the-field experiments with 640 customer-facing employees, we show that self-generated explanations improve error detection and strengthen recall of verification-relevant reasoning, while cues that reactivate such reasoning help sustain detection under repeated LLM use. Theoretically, we identify information retrievability as a distinct precondition for effective oversight and specify generative encoding and cue-supported reactivation as mechanisms that build and sustain it. Practica
arXiv:2609.01974v1 Announce Type: new Abstract: While breathing is essential to living and for sound production in some instruments, for pianists, it is often a hidden and automatic process, making it difficult to analyze or refine. A critical gap exists between data and awareness: while sensors record precise physical metrics, they fail to capture the performer's somatic experience. Conversely, the high cognitive load of performance makes it nearly impossible for musicians to recall their internal states with temporal precision. To address this, we present a system, Breathing Mirror, and associated methodology designed to externalize the pianist's internal somatic experience through three analytical lenses: a Baseline View (synchronized signals), a First-Person View (subjective recall), and an Interpersonal View (collaborative reflection). Through a four-week longitudinal study with a skilled amateur pianist (35 years of experience), we evaluated the system's effectiveness by recordin
arXiv:2609.01813v1 Announce Type: new Abstract: Interactive editors usually assume that users already know what to change. Yet an important interaction state comes earlier: a user may recognize that an artifact is not working without knowing what intervention to request. We call this the articulation gap. We introduce PROS (Proactive Refinement Of Scientific Posters), which separates epistemic initiative from behavioral authority: the system can surface source-grounded candidate problems, while users decide which become repair goals and whether resulting changes are committed. Accepted issues hand off to native-object PPTX editing with validation and reversible preview. We also introduce PROS-Bench, a source-linked collection of 120 papers and 320 editable PPTX posters, including a 120-poster matched primary core and a separate conference representation challenge. On the primary core, PROS achieves a mean VLM-rated stage-balanced diagnosis quality score of 67.2 on a 0-100 scale and 87.
arXiv:2609.01698v1 Announce Type: new Abstract: Squeezing is one of the most natural forms of hand manipulation, inherently involving fine-grained, temporally evolving, per-finger flexion. In VR content creation, squeezing plays a unique role in enabling particular visual effects such as localized deformations and dynamic behaviors, e.g., bursting a Coke can or juicing a fruit, thereby expanding the expressive possibilities of VR content. However, existing techniques, such as 3D Gaussian splatting-based methods and diffusion-based video generation models, are limited in their ability to simulate fine-grained virtual squeezing effects. We introduce VirSqueezer, a framework designed to generate both localized deformations (primary effects) and complex squeezing dynamics, such as rupture and overflow (secondary effects). VirSqueezer captures squeezing control signals using a SenseGlove and provides the user with inferred resistance force feedback during the squeezing process. By estimatin
arXiv:2605.30930v2 Announce Type: replace-cross Abstract: As large language models (LLMs) increasingly act as collaborative partners, human--AI alignment is often evaluated through explicit task success, accuracy, or reward optimization. Yet many collaborative settings depend on tacit understanding: whether an agent can align with a human's evaluative stance or representational priors without clear objectives, communication, or feedback. To study this capacity, we develop a spectrum-placement task inspired by the social party game Wavelength, in which humans and agents independently place concepts along subjective spectra. We operationalize the Tacit Understanding Index (TUX) as a pairwise behavioral measure of similarity between human and agent judgments, and evaluate it with 241 human participants and 200 profile-conditioned LLM agents across four models. We find that nearest human--agent pairs in trait space achieve significantly higher TUX, suggesting that tacit alignment is associ
arXiv:2602.10118v2 Announce Type: replace-cross Abstract: Peer review is central to scientific quality, yet reliance on simple heuristics, namely lazy thinking and non-specific critiques, has threatened review quality. Prior work frames lazy thinking detection as single-label classification and stops at detection, yet review segments often exhibit multiple co-occurring issues, and reviewers benefit more from actionable, guideline-aware feedback than from labels alone. We further show that off-the-shelf LLMs prompted for feedback frequently rewrite the entire review or address the authors rather than the reviewer, motivating an inference-time approach. We introduce an LLM-driven framework that decomposes reviews into argumentative segments, identifies issues violating ACL Rolling Review (ARR) guidelines, and generates targeted feedback using issue-specific templates refined by a novel iterative, reranking-based generation algorithm. In a controlled rewriting study, our feedback reduces
arXiv:2601.22396v3 Announce Type: replace-cross Abstract: Despite the growing utility of Large Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accurately reflect world and moral value systems across different cultural conditionings remains uncertain. This paper investigates the alignment of synthetic, culturally-grounded personas with established frameworks, specifically the World Values Survey (WVS), the Inglehart-Welzel Cultural Map, and Moral Foundations Theory. We conceptualize and produce LLM-generated personas based on a set of interpretable WVS-derived variables, and we examine the generated personas through three complementary lenses: positioning on the Inglehart-Welzel map, which unveils their interpretation reflecting stable differences across cultural conditionings; demographic-level consistency with the World Values Survey, where response distributions broadly track human group patterns; and moral profiles derived from a Mo
arXiv:2507.21815v2 Announce Type: replace-cross Abstract: Millions of individuals' well-being are challenged by the harms of substance use. Harm reduction as a public health strategy provides non-judgemental, evidence-based information intended to improve health outcomes and reduce associated safety risks. Some large language models (LLMs) have demonstrated a high level of medical reasoning, promising to address the information needs of people who use drugs (PWUD). However, their performance in relevant tasks remains largely unexplored. We introduce HarmReduction, a benchmark designed to evaluate LLMs' accuracy and safety risks in harm reduction information provision. The benchmark dataset (HR-Basic) has 2,160 question-answer-evidence pairs. The scope covers three tasks: checking safety boundaries, providing quantitative values, and inferring polysubstance use risks. We build the Instruction and RAG schemes to evaluate model behaviours based on their inherent knowledge and the integrat
arXiv:2312.06315v2 Announce Type: replace-cross Abstract: Warning: This paper contains content that may be offensive or upsetting. There has been a significant increase in the usage of large language models (LLMs) in various applications, both in their original form and through fine-tuned adaptations. As a result, LLMs have gained popularity and are being widely adopted by a large user community. However, one of the concerns with LLMs is the potential generation of socially biased content. The existing evaluation methods have many constraints, and their results exhibit a limited degree of interpretability. In this work, we propose a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs (e.g., GPT-4 \cite{openai2023gpt4}) to assess bias in models. We also introduce prompts called Bias Attack Instructions, which are specifically designed for evaluating model bias. To enhance the credibility and interpretability of bias evaluation, our framework not only prov
arXiv:2606.02347v2 Announce Type: replace Abstract: Algorithm registers are public-facing databases that display basic information about algorithms employed in public administration. While several such registers exist across Europe and globally, their capacity to deliver meaningful transparency remains contested. In Germany, the landscape is notably fragmented: no federal-level register exists, yet at least five state- and federal-level initiatives publish information about AI systems with varying scopes and objectives. A recent conceptual proposal by Alina Lorenz (2025), outlines technical and governance requirements for a national AI transparency register in Germany. We repurpose this proposal as an audit instrument, extracting structured checklists from the transparency goals and subgoals it formulates. The resulting checklists, translated from German into English, is made publicly available to support practitioners auditing existing registers or designing new ones. We apply this fr
arXiv:2604.12289v2 Announce Type: replace Abstract: Online hate speech is associated with harms ranging from deteriorating mental health to violence, yet how consistently platforms moderate hate, and whether enforcement is feasible at scale, remain poorly understood. We audit hate speech moderation on Twitter (now X) using 540,000 tweets annotated by trained native speakers, representative of a full day on the platform. Five months after posting, 80% of hateful tweets, including violent ones, remained online. Removal was only marginally more likely than for non-hateful tweets, far below scams or adult content, and insensitive to severity and reach. Automated detection could not reliably classify hate but ranked it highly, enabling human triage. Simulating this workflow, current staffing curbed little exposure, yet substantial reductions proved financially feasible, far below applicable regulatory fines. Persistent hate reflects resource allocation, not technical limits.
arXiv:2609.02700v1 Announce Type: cross Abstract: Remaining control over their private data is one of the key challenges in this century for users. We know from prior work that users are often neither in a position to fully grasp the content of the usually complicated texts, nor are they motivated to spend the time necessary to do so. We report on the progress made by the PIONEER project on a privacy support tool that combines knowledge transfer and persuasive elements to increase users' privacy awareness and motivation; thus empowering them to more privacy sovereignty. Throughout the research and design process, we consider user group specifics that may result in different requirements, e.g., for children, adolescents, parents, or elderly people. We further target sustainable behavior change by addressing different states of change, precisely: spark initial motivation, facilitate the creation of new habits, and encourage habituation of these habits in the long term (volition). Finally
arXiv:2609.02646v1 Announce Type: cross Abstract: Planning a large data center is difficult because a facility big enough to matter changes the electricity prices it will pay. Those prices are set by market clearing, a constrained optimization problem solved anew in every operating condition. However, simulating the market tells a planner how a candidate plan performs but not how to improve it. Here we treat market clearing as a differentiable optimization layer: each forward pass solves the market, and reverse-mode automatic differentiation propagates the planning cost back through the cleared prices to the plan. After validating these gradients against finite differences, we apply them to a concrete problem: allocating 50 MW of data-center load across six candidate buses in two synthetic networks, under a fixed cost per active site, evaluated over 36 operating states. Judged against exhaustive enumeration of all site combinations, gradient optimization recovers the continuous allocat
arXiv:2609.02620v1 Announce Type: cross Abstract: Generative AI is changing how cultural artifacts are created and circulated, and with it our understanding of creativity itself. Researchers disagree about whether these tools enrich or impoverish culture, and we argue that much of that disagreement comes from conflating two distinct components of creativity: novelty, a property of single artifacts, and diversity, a property of populations. We argue further that creativity in the context of generative AI is best understood as a property of hybrid collectives, or populations of interacting people and algorithms, rather than of individuals. AI-assisted ideation reliably raises the novelty of individual output while narrowing diversity in the aggregate, but this is not an inevitable consequence of putting machines in the loop. Because humans and models search in complementary ways, mixed groups can outperform and out-diversify groups of either kind alone, and machine-discovered solutions c
arXiv:2609.02526v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to predict the responses of human participants in survey panels. Towards that goal, persona prompting has recently emerged as a technique to inform and align large pretrained language models. Persona prompting refers to the practice of using short textual descriptions of 'personas' in prompts to steer the LLM's generations. Personas describe individuals through different attributes such as their socio-demographics, attitudes, or behaviors, with the aim of aligning LLMs to produce responses that correlate with the corresponding human responses. Yet, recent work has produced mixed and partly conflicting results of persona prompting without clear patterns of success and failure. Among the few consistent findings is that the selection of persona attributes matters, and that using more attributes does not necessarily lead to better performance. It remains unclear how different attribute sele
arXiv:2609.02495v1 Announce Type: cross Abstract: Differential privacy (DP) has emerged in the computer science literature as a measure of the impact on an individual's privacy resulting from the publication of a statistical output such as a frequency table. This paper provides an introduction to DP for official statisticians and discuss its relevance, benefits, and challenges from a National Statistical Organisation (NSO) perspective. We motivate our study by examining how privacy is evolving in the era of big data and how this might prompt a shift from traditional statistical disclosure techniques used in official statistics--which are generally applied on a cell-by-cell or table-by-table basis--to formal privacy methods, like DP, which are applied from a perspective encompassing the totality of the outputs generated from a given dataset. We identify an important interplay between DP's holistic privacy risk measure and the difficulty for NSOs in implementing DP, showing that DP's maj
arXiv:2609.02379v1 Announce Type: cross Abstract: While existing work on LLM authorship attribution (AA) has made progress, available benchmarks remain limited, often focusing on English, controlled settings, or relatively outdated models, with the few multilingual studies considering only relatively short texts. We introduce MultiGhostBench, a multilingual benchmark comprising 928 books generated by five recent LLMs across six languages and three scripts, with an average length of approximately 59K words per book. The benchmark supports evaluation under domain, author, and language shifts. Evaluation of representative AA methods shows that no single method consistently performs best across settings, and performance generally degrades under distribution shifts. Transformer-based detectors can retain generator-related information across languages, although transfer effectiveness varies by language pair, whereas statistical and fingerprint-based detectors are more language-dependent. We
arXiv:2609.02122v1 Announce Type: cross Abstract: As large language model (LLM) agents shift from tools to participants in human groups, a fundamental question for collective behavior is how their growing presence reshapes consensus formation. Here we study mixed human-AI groups in a collaborative description game, in which shared conventions emerge through repeated rounds of random pairwise communication. Varying the proportions of LLM agents, we identify three distinct regimes of consensus formation: low agent proportions facilitate human-led consensus, intermediate proportions disrupt convergence, and high proportions restore strong consensus while shifting it toward agent-led conventions. Crucially, these regimes differ not only in the strength of convergence, but also in the semantic grounding and communicative form of the resulting consensus: human-led consensus is more concrete, holistic, and grounded in shared real-world analogies, whereas agent-led consensus is more abstract,
arXiv:2609.01846v1 Announce Type: cross Abstract: Recorded lecture videos, often enhanced with search and summarization features, are a standard study resource. However, students cannot easily ask course specific questions or verify answers against an instructor's lecture. We report a semester-long deployment of VideoPoints platform with a retrieval-augmented chatbot that answers from course lecture materials and returns timestamped citations. The chatbot retrieves only from the active course, uses chapter summaries to guide transcript ranking, and returns clickable timestamped citations. Students used it for quick lookups and exam review. Across 833 messages, 70.5% included citations, none crossed a course boundary, and when no lecture evidence matched, the chatbot usually declined rather than answering. Among the users, citations were the most consistently useful feature, while practice-question generation was the strongest unmet request. We also evaluated the design on the real-worl
arXiv:2609.01685v1 Announce Type: cross Abstract: With the development of artificial intelligence (AI), the landscape of meta-ethics, which has largely centred on human ethics, faces pressures that may significantly reconfigure it. In particular, if future AI systems were to exhibit sufficiently integrated capacities for moral reasoning, moral intentionality, and moral reflection, novel meta-ethical questions would arise concerning what I call "AI's own ethics", as distinct from ethical principles merely imposed on AI by human designers. This paper offers a conditional and methodological framework for identifying the questions that would emerge if such AI systems were to arise. On that basis, the paper distinguishes four domains of meta-ethical inquiry in the era of AI: questions about the nature of human ethics from the human perspective; questions about the nature of AI's own ethics from the human perspective; questions about the nature of human ethics from the AI perspective; and qu
arXiv:2609.01680v1 Announce Type: cross Abstract: This paper compares rule-based and learning-based pricing mechanisms for peer-to-peer (P2P) electricity trading in residential photovoltaic communities. The rule-based benchmarks comprise bill-sharing as an ex post allocation mechanism, the mid-market rate, and supply-demand-ratio pricing. The reinforcement-learning (RL) formulation is implemented through a Deep Q-Network and evaluated under multiplier-based and learnable SDR-shaped pricing, with a fixed-parameter SDR variant as a non-learning control. Performance is assessed through community savings together with complementary financial and operational indicators. In the base PV-only configuration, the rule-based benchmarks outperform the best RL policy. With battery energy storage, evaluated for the RL policies only, community savings under the best RL policy increase from EUR 734.23 to EUR 978.52. Across the learning-based modes and in both configurations, SDR-shaped pricing outperf
arXiv:2609.01625v1 Announce Type: cross Abstract: Content moderation is a central form of digital governance, yet people disagree over what content should be removed from shared online spaces. While platforms aggregate human judgments to build moderation systems, it remains unclear how this process shapes which users are protected from content they perceive as toxic. We address this gap by combining large-scale judgment data with counterfactual simulations that trace how the demographic composition of moderator pools shapes the distribution of protection across users. Applying this framework to removal judgments from 16,221 U.S. respondents evaluating 102,463 comments from Twitter, Reddit, and 4chan, we find demographic heterogeneities in moderation demand. We further reveal a consistent pattern of in-group protection: reductions in perceived toxicity accrue disproportionately to users who share the demographic identities of the moderator pool. Crucially, moderator pools that mirror th
arXiv:2609.02455v1 Announce Type: new Abstract: Digital rights increasingly exist in law but remain difficult to exercise through the information systems that mediate them. Using disciplined conceptual synthesis and problematization, this critical-conceptual IS paper explains the gap through the interaction of legal heterogeneity, conflicting organizational and commercial incentives, fragmented architectures, and asymmetrical control over rights-relevant acts. It then theorizes a human-compatible rights layer: a governed sociotechnical capability for standardized, machine-readable, bidirectional, and jurisdictionally plural communication of requests, consent, refusal, withdrawal, objection, records, and support. Comparing California-style opt-out signals, the EU's mixed lawful-basis regime, and P3P, DNT, GPC, and ADPC, the paper derives seven normative requirements, develops rights by architecture as a bounded emancipatory policy argument, and treats a proposed GDPR provision on automa
arXiv:2609.02453v1 Announce Type: new Abstract: Machine learning (ML) education faces two persistent and connected obstacles: many educational tools present ML as an opaque black box, which leaves learners with a superficial understanding, and this same opacity prevents users from forming the calibrated trust that appropriate reliance on AI systems requires. We present ICE-T, a didactic framework that integrates three mutually reinforcing facets: intermodal transfer grounded in Bruner's enactive, iconic, and symbolic modes of representation, computational thinking operationalized through the Use-Modify-Create progression, and explanatory thinking supported by a process model. Connecting the framework to the empirical literature on algorithm aversion, AI literacy, and mental model formation, and to systematic reviews of the K-12 ML activity landscape, we argue that the three facets supply the cognitive mechanisms that the trust calibration literature identifies as drivers of appropriate
arXiv:2609.02296v1 Announce Type: new Abstract: A new politics of artificial intelligence and work is taking shape across party systems, but comparative politics has yet to map it. Using 1,514,950 parliamentary speeches from 33 parliaments (2023-2026), we show this politics follows a different logic than political economy expects. Research anticipates that technological disruption generates demands for compensation; instead, compensation accounts for just 2.3% of response-frame mentions, while enablement and investment dominate (55.2%), regulation and restriction follow (21.8%), and training (20.6%) appears at similar rates across families. Parties disagree instead over what AI means for work and how far this technology should be restrained. The mainstream and radical right support unrestricted enablement; the left is critical but divided on remedy. Social democrats stay adoption-oriented; greens split evenly. The radical left is the clearest force for restriction. The AI conflict thus
arXiv:2609.02232v1 Announce Type: new Abstract: Automated student-attention estimation can support learning analytics, but aggregate predictive metrics can conceal demographic disparities. This study evaluates fairness-aware multimodal temporal models on DIPSER, a naturalistic classroom dataset combining facial images, wearable-sensor measurements, attention annotations, and automatically inferred demographic metadata. Three baselines are compared across 10 training seeds: a Visual GRU, a Sensor GRU, and a Residual Fusion Transformer. The multimodal model achieves the best mean test performance (MAE 0.283, RMSE 0.363) and the lowest worst-group error among the evaluated baselines, although its gain over the Visual GRU is modest. Gender- and age-targeted MAE-gap regularization reduces disparities on validation data, but these gains do not consistently transfer to held-out subjects or repeated subject-level splits. On an NVIDIA A100-SXM4-40GB GPU, the warm end-to-end pipeline averages 50
arXiv:2609.02055v1 Announce Type: new Abstract: Privacy policies may contain internal contradictions in which commitments are undermined by practices documented elsewhere in the same policy. We operationalize this phenomenon, privacy washing, through a four-stage pipeline: statement extraction, compatibility filtering and natural language inference screening, multi-model judge verification, and thematic analysis, with contradictions confirmed by majority vote of a three-model LLM panel. Applied to two corpora of website privacy policies, 123 collected in 2026 (OPPT) and 115 collected in 2015 (OPP-115), the pipeline finds the same category patterns recurring across the 11-year gap, with third-party sharing contradictions the majority of confirmed cases in each primary run, consistent with structural factors in policy composition rather than necessarily intentional deception. At least one panel-confirmed contradiction appears in 12.2% of OPPT companies (15/123; 9.8% excluding legacy pair
arXiv:2609.01902v1 Announce Type: new Abstract: Assessments of cultural alignment have become an important part of the development and improvement of large language models (LLMs). However, the majority of the evaluations treat culture as a single snapshot, investigating only whether a model represents a society accurately at the current time. Research in cultural psychology shows that cultural values change at different rates and directions over time. Therefore, a "culturally aware" model should capture not only where a culture is today but also how it has changed over time. We examine this missing dimension of cultural awareness using more than two decades of the World Values Survey data. We compare the cultural trajectories of 40 countries with the trajectories produced by four state-of-the-art (SOTA) LLMs on the Inglehart-Welzel cultural map. Our findings show that while models generally place countries close to their most recent surveyed positions, these representations tend to lag
arXiv:2609.01773v1 Announce Type: new Abstract: Open geospatial standards let data, services, and systems work across platforms. But openness is not just a property of specifications. It also depends on the institutions that produce them and the infrastructures in which they operate. Standards may function as digital public goods and, once embedded in public systems, as digital public infrastructure. Those roles can diverge. This paper asks when open geospatial standards remain public infrastructure rather than becoming channels of enclosure. It makes two claims. First, standards-setting governance matters: institutional design shapes whether open specifications retain their public-good character. Second, geospatial coordination is shifting toward proprietary location stacks, APIs, and cloud platforms, where roadmaps, pricing, and service terms increasingly set the rules. Sovereign-cloud and sovereign-AI strategies relocate this power rather than remove it. These dynamics reinforce eac
K–12 school districts are packed with digital tools, and it’s up to IT leaders to manage the governance, data and risk associated with them. But no single department can do this alone and without all of the facts they need to make an informed decision. At ISTELive 2026 in Orlando, Fla., technology experts explained how cybersecurity, data privacy, accessibility and governance work together and why building a safe, intentional, student-centered digital environment depends on breaking down the silos between IT and the rest of the district. Cybersecurity Maturity Assessments Can Help Guide…
Earlier this year, U.S. senators convened to grill experts on how social media, smartphones and other technologies are affecting children’s mental health and learning. That conversation has since helped fuel a new wave of legislative action, with nearly a dozen states now considering screen-time restrictions for students. It’s an important debate. But from where I […]
A lawsuit alleges the U.S. Department of Education and the Office of Management and Budget are withholding the funds unlawfully.
The preliminary proposal comes as the Missouri school district has seen enrollment decline sharply by 58.5% in a 34-year period.
As the debate about screen time and digital tools in the classroom continues in school districts around the country, speakers at ISTELive 26 in Orlando, Fla., presented research noting that a balance between digital tools and foundational learning is the best path to effective learning. “This is not pro-tech versus anti-tech,” said Amanda Bollinger, associate administrator in the teaching and learning department at Jordan School District and a member of the Utah State Board of Education. “It’s just current reality. How do we balance those two things?” Cari Warnock, education…
This story was originally reported by Nadra Nittle of The 19th. Meet Nadra and read more of their reporting on gender, politics and policy. To understand why five California families took their fight against segregated schools to court in the 1940s, picture the buildings reserved for their children’s learning. At that time in rural Orange […]
District leaders are under increasing pressure to improve science achievement while balancing competing priorities, staffing challenges, instructional demands, and accountability expectations.
Human Intelligence Labs: New Infrastructure for Learning in the Age of AI Elizabeth Redden Thu, 07/02/2026 - 03:00 AM Colleges need to invest in creating welcoming, AI-free learning spaces. Byline(s) Karen Spira
How Will We Look Back on This Moment in Higher Ed? sara.custer@in… Thu, 07/02/2026 - 03:00 AM Between historical revisionism and higher ed reform, this week speaks to the tensions in our country and marks a historical moment for the sector, one we’re still trying to understand. Byline(s) Sara Custer
New Presidents: Aspen Institute, Spelman, U at Buffalo, SUNY Brockport and More gianna.jakubowski Thu, 07/02/2026 - 03:00 AM Byline(s) Gianna Jakubowski
6 HBCUs Launch Course-Sharing Partnership Joshua.Bay Thu, 07/02/2026 - 03:00 AM The new initiative lets students take classes across institutions without transferring or losing progress toward a degree. Byline(s) Joshua Bay
New AI Agents Pose ‘Existential Threat’ to How Grants Are Awarded sara.custer@in… Thu, 07/02/2026 - 03:00 AM The rapid development of technology is outpacing any attempts to reform assessment systems, researchers warn. Byline(s) Seher Asaf for Times Higher Education
Your Kids in Your College? Readers Respond. Sara Brady Thu, 07/02/2026 - 03:00 AM From the can’t-get-far-enough-away to the happy-at-home, readers’ experiences vary. Byline(s) Matt Reed
Virginia Wesleyan Officially Changes Name to Batten University Katherine Knott Thu, 07/02/2026 - 03:00 AM Byline(s) Katherine Knott
Mississippi Facing Financial Aid Shortfall Johanna Alonso Thu, 07/02/2026 - 03:00 AM Byline(s) Johanna Alonso
Colleges Reflect on 250 Years of American History, Warts and All kathryn.palmer… Thu, 07/02/2026 - 03:00 AM Higher education institutions are commemorating the nation’s founding by providing a forum for grappling with the uncomfortable and nuanced aspects of the American past. Byline(s) Kathryn Palmer
DA Launches Criminal Investigation of N.M. Highlands University Ryan Quinn Thu, 07/02/2026 - 03:00 AM Byline(s) Ryan Quinn
One analysis estimates that the policy could cost the 28-institution system $15 million a year in lost tuition and fee revenue.