An instrument for spotting the next edtech opportunity — generated ideas, each traced 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:2606.25626v1 Announce Type: cross Abstract: We present a general answer set programming based hybrid quantitative-qualitative method for computing constrained branching trajectory modes for moving objects in real-world settings. The method performs constrained traversal of an environment graph, enumerating geometrically admissible motion behaviours as stable models, each constituting a distinct trajectory mode characterised by both domain-dependent and independent factors such as derived event sequence, map topology, and domain norms. The hybrid trajectory computation method is generally applicable across motion characteristics typically encountered in diverse dynamic domains with moving objects, e.g., autonomous driving. We demonstrate applicability and highlight how computed trajectories are traceable to their underlying stable model, thereby affording verifiable interpretability that purely learned approaches cannot provide. We also perform an empirical evaluation with Argover
arXiv:2606.25337v1 Announce Type: cross Abstract: AI copilots can substantially boost human performance through shared control, but excessive assistance can induce over-reliance and skill atrophy. This paper studies how an embodied AI agent can act as a coach that accelerates human motor-skill development. We argue that effective coaching requires strategic scaffolding and stepping back that are aligned with the learner's capability, allowing productive failures that drive learning. We formalize the interactive AI coaching process as a non-cooperative dynamic game in which the learner optimizes task performance while the coach targets the learner's independent competence. Building on this formalism, we develop a reinforcement learning framework combining adaptive shared control with probabilistic models of the coach's causal influence on skill evolution, enabling tractable training of coaching policies. A comprehensive user study (N=33) on first-person-view drone racing shows significa
arXiv:2606.25177v1 Announce Type: cross Abstract: Cognitive workload monitoring is important for adaptive rehabilitation and assistive interfaces, where task difficulty, pacing, and feedback should be adjusted according to the user's cognitive state to avoid overload and under-challenge. Emerging extended reality and robot-assisted rehabilitation environments provide controllable training tasks, but they require unobtrusive sensing methods that can capture rapid ocular dynamics during interaction. Existing eye-movement-based cognitive workload recognition methods mainly rely on frame-based eye trackers, which often suffer from limited temporal resolution and degraded robustness under rapid eye movements. In contrast, event cameras provide microsecond-level temporal resolution, high dynamic range and low latency, making them suitable for capturing fine-grained ocular dynamics. Many previous studies rely on free-viewing or similar paradigms, where gaze locations can vary across tasks. As
arXiv:2606.25162v1 Announce Type: cross Abstract: Augmented Reality (AR) can improve collocated human-robot collaboration by making robot state and intent visible and enabling intuitive control, yet large, visually diverse environments like the outdoors challenge both interaction and content legibility, especially at long distances and beyond visual line of sight. We present fARfetch, an AR-HRC system that integrates (i) shared semantic environment mapping across an AR headset and robot that visualizes detected landmarks in AR to support landmark-grounded go-to commands, (ii) a context-aware world-in-miniature representation of the shared environment for fine-grained path authoring, and (iii) vision-language-model driven AR view management that jointly adapts virtual content color, size, and orientation to maintain legibility in large visually diverse environments. We implement fARfetch with a Meta Quest 3 headset and Unitree Go2 quadruped robot, and conduct a within-subjects user stud
arXiv:2606.25116v1 Announce Type: cross Abstract: Respiratory acoustic foundation models (FMs) are benchmarked exclusively on smartphone recordings, yet clinical deployment increasingly targets body-coupled (BC) wearables whose sensors attenuate high-frequency content through tissue and bone, leaving FM reliability uncharacterised. We introduce BCoughBench, evaluating five FMs (OPERA-CT/CE/GT, HeAR, M2D+Resp) on nine classification tasks (AUROC, sensitivity at 95% specificity, Expected Calibration Error) and three age regression tasks (MAE vs. a mean-predictor baseline) across five EBEN-simulated BC sensor conditions on five labeled cough datasets. Mean AUROC declines from 0.785 (smartphone) to 0.689-0.723, degrading most under temple vibration pickup ($\Delta$ = -0.096) and least under the soft in-ear ($\Delta$ = -0.062). No FM meets the clinical sensitivity threshold (Se@Sp95 $\geq$ 0.20) on most disease tasks under any BC sensor. Sex classification on the CIDRZ cohort collapses (AUR
arXiv:2606.25108v1 Announce Type: cross Abstract: Autonomous AI systems are transitioning from advisory to autonomous roles for medication prescriptions. Recent United States bill H.R. 238 and Utah's prescription-renewal pilot both authorize AI to prescribe medications in an agentic capacity. While some regulatory guidelines suggest aggregate model performance metrics for clearance, they do not require i) calibrated per-prediction confidence for action-gated thresholds, ii) differentiated communication of uncertainty arising from model ignorance (epistemic) versus genuine clinical ambiguity (aleatoric), and iii) inferential transparency at the moment of decision that allows for liability allocation. Here, we present a regulatory and technical argument (tested with a survey of 136 U.S. prescribing clinicians) positioning these as minimum architectural requirements for safe autonomous prescribing. Our results suggest prescribing clinicians i) would not permit autonomous prescribing witho
arXiv:2606.25941v1 Announce Type: new Abstract: Increasing demand for precise and reliable control in complex scenarios has led to the development of increasingly sophisticated controllers, including data-driven approaches employing closed box models and mathematically rigorous yet complex designs. This complexity highlights the needs for explainable control that can provide human-understandable insights into controller behavior. In this paper, an explainable control framework (XCF) along with supporting algorithms and user interface are proposed to explain how controllers determine their control actions and their underlying working mechanism. The novel contributions of this work are threefold: First, the XCF is designed to provide model-agnostic explanations for controllers in closed-loop systems and can optionally refine local explanations by system response dynamics. Second, a novel explanation method, hierarchical fuzzy model-agnostic explanation for control systems (HFMAE-C), is p
arXiv:2606.25809v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate personalized guidance in wellbeing contexts such as physical activity, stress management, and mental health support, enabling fluent and context-aware interaction but relying on largely implicit mechanisms that shape how recommendations are expressed and adapted. We argue that this reliance on implicit adaptation through prompting and alignment limits control over guidance, responsibility framing, and user influence, which is particularly problematic in wellbeing settings where recommendations affect users' actions and long-term outcomes. We propose a system-level perspective in which conversational behavior is structured through explicit interaction constraints, including guidance strategies, explanation styles, degrees of directness, and mechanisms for user control. Building on prior work on tangible recommendations, we show how these constraints address key challenges in we
arXiv:2606.25681v1 Announce Type: new Abstract: Motion-to-photon latency is inherent in immersive virtual reality (VR) systems and can arise from multiple sensorimotor loops, including view-contingent latency between head movement and display update and interaction latency between hand movement and the virtual effector. Although prior work shows that interaction latency can impair VR performance, it remains unclear whether common spatial, temporal, and efficiency measures reveal the same latency-related disruption. This study addressed this question by experimentally imposing delays between the physical and virtual hands during manual pointing in VR. Participants pointed to targets on a horizontal surface in VR and in the physical environment as an unmediated baseline. In VR, pointing was performed with a virtual hand avatar controlled by a motion capture pipeline, and additional delays (0-500 ms) were imposed between the participant's hand movement and the rendered movement of the vir
arXiv:2606.25489v1 Announce Type: new Abstract: Large language models (LLMs) increasingly show step-by-step reasoning rationales alongside their answers, turning reasoning from an internal model capability into a user-facing interface feature. Yet it is unclear whether such rationales help users judge when trust is warranted or merely persuade through fluent reasoning. We address this gap through the lens of auditable trust calibration: user-facing rationales should help people inspect whether an answer is warranted by evidence. We test this framing in factual verification through two linked studies. Study 1, an online experiment (N=68), manipulated rationale presentation format (instant, delayed, on demand), rationale correctness (correct, incorrect), and certainty framing (none, certain, uncertain). Study 2, a controlled eye-tracking study (N=54), examined how no-, correct-, and incorrect-rationale conditions were associated with users' trust, decision-making, and eye-movement patter
arXiv:2606.25287v1 Announce Type: new Abstract: Contemporary digital and AI-mediated environments are reshaping the cognitive ecologies within which human reasoning develops. As everyday activity becomes embedded in datafied infrastructures, cognitive habits adapt to conditions of immediacy, fragmentation, externalisation, and algorithmic filtering. This paper introduces the Digital Pirah\~a Condition, a cultural ecological model explaining how these environments cultivate adaptive but shallow cognitive patterns, epistemic flattening, reduced recursive capacity, and heightened reliance on external scaffolds. While functional within digital systems, these adaptations create an ecological mismatch with the recursive, integrative reasoning required in academic and institutional activity systems. The paper argues that this mismatch is an ecological outcome rather than a psychological deficit, and that addressing it requires intentional cognitive niche construction within educational instit
arXiv:2606.25271v1 Announce Type: new Abstract: Augmented Reality (AR) can be a positive therapeutic approach to support mental health and emotion regulation. Although AR techniques for therapeutic support exist, there is no user-centered, expert-informed understanding of how real-time AR designs can support people in emotional distress without disengaging them from their ongoing activities. This lack of reusable design resources hinders the adoption of AR for mental health support. This paper addresses this gap by introducing a co-designed collection of AR interventions describing how this technique can support real-time emotion regulation. The repository was created following a two-phase participatory design process. Phase 1 recruited 40 anxiety-prone individuals and used the Nominal Group Technique to list ideas on how AR affordances could support emotion regulation. Phase 2 recruited 10 mental health professionals to organize these ideas into thematic clusters and assess their clin
arXiv:2606.25247v1 Announce Type: new Abstract: Neural swipe decoders are typically tied to the keyboard they were trained on, requiring a new corpus and training run for each layout. In this report, we document our approach toward training models that can function on any contiguous mobile keyboard layout. At each point along the swipe, our encoder predicts whether the user is indicating a character and where on the keyboard that character lies. The keyboard layout is supplied at inference time and used to map the spatial and temporal prediction to a logit at each key, rather than being learned during training. Training neural models requires substantial data, but public swipe data is limited, particularly for non-QWERTY layouts. We release swipe.futo.org, the largest MIT-licensed swipe corpus we are aware of, containing over 1M donated swipes from more than 12k donor sessions. To generalize beyond the English QWERTY layout, we apply geometric augmentations to both the swipe trajectory
arXiv:2606.25202v1 Announce Type: new Abstract: Human-robot collaboration (HRC) often requires robot intentions and internal states to be conveyed to users for task efficiency and safety. Recently, augmented reality (AR) situated analytics provide such real-time robot feedback in HRC contexts. However, AR situated analytics can obstruct important real-world elements, posing safety and usability risks, especially when content is dynamically positioned relative to movements of mobile robots in a warehouse HRC scenario. In this paper, we introduce the Augmented Reality Technique Of Obstruction Deterrence while Aiding Robotic Teaming for Users (ARTOO-DARTU), an AR system tailored specifically for warehouse HRC that enables real-time robot situated analytics and control while preserving visibility of the real world through an obstruction detection and mitigation pipeline (ODM) that is uniquely suited for AR-HRC. To evaluate ARTOO-DARTU, we developed Pocket MonstARs, a controlled gamified ab
arXiv:2606.25149v1 Announce Type: new Abstract: The last few years have seen a significant rise in interest in highly autonomous and proactive systems, fueled by advances in AI. Systems that anticipate user needs, take initiative, and act without explicit user input. Such systems span a wide range of applications, from smart lighting that adapts to user activity to assistive robots that plan actions in advance to intelligent thermostats that learn routines and adjust environments proactively. Despite this breadth, the concept of proactivity remains loosely defined and inconsistently applied across research and practice. Current usage of the term often conflates fundamentally different system behaviors. For instance, simple reminders or recommendation systems are frequently labeled as proactive, even though underlying mechanisms and intentions differ significantly. This conceptual ambiguity limits our ability to systematically design, compare, and evaluate proactive systems. Moreover, e
arXiv:2606.18936v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to laboratory planning and autonomous discovery. This progress creates an urgent need for safety benchmarks that evaluate not only scientific competence, but also whether models recognize and avoid risks in high-stakes scientific contexts. Existing AI4Science safety datasets cover several disciplines and task formats, leaving the underlying risk dimensions underspecified. We introduce \textbf{SciRisk-Bench}, a benchmark designed to evaluate AI4Science safety from two complementary perspectives: explicit risk dimensions and scientific disciplines. SciRisk-Bench covers 7 disciplines, 31 subdisciplines and 10 risk dimensions. In the experimental section, we evaluate both mainstream LLMs and science-oriented LLMs across risk dimensions, disciplines, and sub-disciplines, enabling
arXiv:2606.08998v2 Announce Type: replace-cross Abstract: Agentic AI systems can behave differently across runs: the same request may produce a different plan, a different tool call, a different code edit, or a different final answer. Such variability arises from several layers that are often conflated. At the core of many current agents is a foundation model, a large pretrained model adaptable to many downstream tasks, embedded in an orchestration loop that plans, calls tools, observes results, and updates state. One explicit intrinsic source of variability in such systems is token generation: the model computes scores over possible next tokens, the scores are converted into probabilities, and a decoder may sample tokens using a pseudo-random number generator. A small sampled token difference can then propagate upward into a different tool call, code path, search query, or agent state. Other sources of variability are extrinsic to token sampling, including changing environments, live
arXiv:2605.29129v2 Announce Type: replace-cross Abstract: Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback. These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt. We define Agentic Technical Debt as the accumulated liability created when prompts, memory, tool schemas, orchestration graphs, control policies, and observability routines are patched together faster than they can be validated, standardized, and governed. We define Stochastic Tax as the recurring operating burden of keeping probabilistic agent behavior within acceptable bounds. The distinction matters: debt is a stock of design and governance liability, while the tax is a flow of operating cost that arises because stochastic agents act through tools and workflows. We outline how managers can make both visible through
arXiv:2605.26309v3 Announce Type: replace-cross Abstract: The rapid proliferation of visual content raises fundamental questions about how different visual formats and features shape perceived credibility. Drawing on processing fluency theory, this research examines how visuals shape credibility judgments. We focus on three popular formats-photos, infographics, and data visualizations-comparing them to text-only posts, and test how two visual features, aesthetic appeal and production quality, influence credibility through processing fluency as a mediating mechanism. Through a preregistered experiment with 1200 US participants, we found that visual posts are generally perceived as more credible than text-only posts but this credibility advantage only applies to photos and infographics, not to data visualizations. Aesthetic appeal increases perceived credibility, partially mediated by processing fluency, while production quality had no significant effect on credibility across formats. Th
arXiv:2601.13317v2 Announce Type: replace-cross Abstract: Climate discourse online shapes public understanding of climate change and informs political and policy debate, yet it unfolds across structurally different environments: paid advertising platforms host targeted, institutionally produced messaging, while public social media reflects largely organic, user-driven discussion. We present a comparative analysis of climate discourse across paid advertisements on Meta (previously Facebook) and public posts on Bluesky from July 2024 to September 2025. To support it, we develop an interpretable thematic discovery pipeline that clusters texts by semantic similarity and uses large language models (LLMs) to label clusters with concise, human-interpretable themes, requiring no predefined topic inventory or seed set. Using these themes, we find the two environments diverge systematically: paid advertising centers on strategic promotion of specific solutions in a formal, forward-looking regist
arXiv:2510.20025v2 Announce Type: replace-cross Abstract: Monitoring systems increasingly fuse dynamic behavioral data with outcome-based surveillance, raising a basic question: when does behavioral data carry predictive information that outcome history lacks? We study this using epidemic forecasting on mobility networks, asking whether mobility networks provide independent predictive signal beyond local outcome-based surveillance. We formalize this as a substitution-complementarity problem over directed, weighted mobility networks. Using a Frisch-Waugh-Lovell variance decomposition, our analytical framework derives domain-agnostic conditions under which network-topology features retain incremental explanatory power beyond autoregressive outcome histories. We instantiate the framework using town-level COVID-19 forecasting in Massachusetts (April 2020-April 2021), constructing mobility networks among 300+ towns from smartphone-derived origin-destination aggregates to extract centrality
arXiv:2506.04435v2 Announce Type: replace-cross Abstract: Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting Computer Science departments and their DBLP coauthorship connections. We introduce algorithms for combining name- and perception-based demographic labels by maximizing alignment with self-reported demographics from a survey of faculty from our census. We find that women and individuals with minoritized race identities are less central in the computer science coauthorship network, implying worse access to and ability to spread information. Centrality is also highly correlated with prestige, such that faculty in top-ranked departments are at
arXiv:2605.16237v3 Announce Type: replace Abstract: Clearly-defined rules are often assumed to be straightforward to automate and evaluate. We challenge this assumption through an in-depth study of Major League Baseball's (MLB) seven-year experimentation with the Automated Ball-Strike System (ABS). ABS is envisioned to call balls and strikes accurately: a seemingly straightforward use of technology to objectively determine the distance between a pitch and the strike zone. Although the strike zone is an area clearly defined in the rulebook, it took MLB seven years to figure out how to automate calling balls and strikes with ABS, showing how even seemingly straightforward rules require a complex translation process to operationalize via technological systems. In this paper, we trace the design decisions that led to the current implementation of ABS. Our case study reveals that "distance" exists even between a clear rule and its technological implementation. Using analytic frameworks from
arXiv:2601.09117v3 Announce Type: replace Abstract: Generative AI systems increasingly enable the production of highly realistic synthetic media. Civitai, a popular community-driven platform for AI-generated content, operates a monetized feature called Bounties, which allows users to commission the generation of content in exchange for payment. To examine how this mechanism is used and what content it incentivizes, we conduct a longitudinal analysis of all publicly available bounty requests collected over a 14-month period following the platform's launch. We find that the bounty marketplace is dominated by tools that let users steer AI models toward content they were not trained to generate. At the same time, requests for content that is "Not Safe For Work" are widespread and have increased steadily over time, now comprising a majority of all bounties. Participation in bounty creation is uneven, with 20% of requesters accounting for roughly half of requests. Requests for "deepfake" - m
arXiv:2606.25787v1 Announce Type: cross Abstract: When a large language model (LLM) answers a question about a company, it grounds the answer in retrieved web sources, and those sources decide what the model says. Most analysis of AI brand visibility looks at the answer text. This study looks one step earlier, at the citations. We merge three Rankfor.AI datasets covering 128 brands across 12 home markets and 13 languages, and analyse 167,551 URL-grounded citations (189,974 total attribution rows). We classify each citation by domain and source type and measure where AI gets its brand information, by language and by market. Four patterns hold. First, AI grounds brand answers overwhelmingly in third-party sources: 85.7% of citations point to sites the brand does not own, against 14.3% owned. Second, the source base is concentrated and long-tailed: 80% of citations come from about 18% of domains, fitting a Zipf law (alpha = 0.86, R^2 = 0.983). Third, one reference site dominates almost ev
arXiv:2606.25320v1 Announce Type: cross Abstract: Since the 1990s, advancements in big data and information technology have increasingly driven data-centric research in the field of Library and Information Science (LIS). To assess the influence of this data-driven research paradigm on the LIS discipline, this study conducts a fine-grained analysis to uncover the evolutionary trends of research methods within the domain. Using academic papers from LIS published between 1990 and 2022, four key categories of data-driven method entities are automatically extracted: algorithms and models, data resources, software and tools, and metrics. Based on these entities, the study examines the evolution of LIS research methods from three dimensions: the characteristics of research method entities over time, their evolution within different research topics, and the evolutionary features of research method entities across various research methods. The findings highlight data resources as a pivotal driv
arXiv:2606.25120v1 Announce Type: cross Abstract: Aviation software certification has operationalised three structural requirements for governed software systems since 1992: structured governance linkage between governing specifications and operational evidence, context-bounded validity that triggers revalidation when operational context changes, and an objective evidence architecture that defines what proof means and what makes it sufficient. These requirements appear in DO-178C and DO-330 and are enforced through FAA and EASA certification. No existing framework requires these structural properties as intrinsic properties of individual AI governance documents. A system prompt, an AGENTS.md file, a governance policy, or a task envelope can be deployed without satisfying any of the three requirements aviation has enforced for three decades. Aviation is the most technically rigorous instance: its standard-setting bodies have acknowledged that their frameworks break down for AI systems,
arXiv:2606.25076v1 Announce Type: cross Abstract: Following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, this article shifts attention from forecast output to the working practices that make prediction systems possible. We argue that machine learning and recent digital technologies will reshape the forecasting value chain: how models are coded and developed, how observations and Earth-system data are exploited, how data and computing are managed, how systems are verified, and how information is created, evaluated and turned into services. We discuss six non-exhaustive areas in which agentic software engineering, open and compressed data, shared verification workflows, interactive computing and generative methods may make modelling, evaluation and service creation faster, more interactive and more widely accessible. These changes will require weather and climate centres to adapt their infrastructures, da
arXiv:2606.24973v1 Announce Type: cross Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning 328 questions across five subjects and including handwritten work. We test whether off-the-shelf large language models agree with examiners as closely as the two examiners agree with each other. We find that models overwhelmingly agree well with the examiner consensus across subjects, with the top performing models agreeing more closely with examiners than examiners agree with each other. Models achieve high scores for subjective tasks like English essay marking, as well as handling complex and messy handwritten Maths paper scripts. Agreement is uniform near the examiner line, and not massively discriminated by model size, providing cost-effective automated marking solutions.
arXiv:2606.24896v1 Announce Type: cross Abstract: LLM agent memory is now treated as a first-class architectural component in five major surveys published between January and April 2026. None of these surveys treats project governance, capital structure, or license posture as architectural variables. We argue they are. In a constructed sample of 105 production-relevant open-source data-infrastructure and AI-tooling projects, we catalogue 38 license-and-sustainability events between 2018 and May 2026. About a quarter of the sample (24 percent) experienced at least one adverse event. The conditional rates split sharply by structure: 46 percent for single-vendor venture-backed projects, 2.5 percent for foundation-governed projects funded outside the venture cycle. The headline differential -- roughly nineteen-fold -- is invariant to the most contested coding choice in the catalogue; we show the sensitivity table in Section 7. A small subset of foundation-governed projects with venture-bac
arXiv:2606.24890v1 Announce Type: cross Abstract: Can a small group of volunteers shape how AI systems discuss animal welfare, just by editing Wikipedia? We show that they can. Wikipedia appears in nearly every major language model training dataset and is weighted more heavily than web-crawled text. The Pro-Animal Wikipedians (PAW), a group of advocates who add sourced animal welfare content to relevant articles, have made 125 edits across 115 pages. Using gradient-based data attribution (Bergson; MAGIC), we traced how these edits influence language model behavior. TrackStar retrieval attribution on Llama 3.1 8B found that PAW-edited sections made up 68 percent of the highest-attributed documents for animal welfare queries (p < 0.0001) but only 52 percent for unrelated queries about the same companies (p = 0.53): the model links PAW content specifically to animal welfare topics, not to the entities in general. MAGIC counterfactual influence estimation on Llama-3.2-1B, run across five r
arXiv:2606.25668v1 Announce Type: new Abstract: Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings - including criminal pretrial release, clinical triage, student support, and more - it is often assumed that improved predictive accuracy is the priority consideration in determining better downstream outcomes upon the deployment of ADS. In practice, real-world case studies reveal that this is far from the case: introducing individual predictions into decision-making modifies organizational workflows, assessment, and decision-making processes in ways that require a complete re-consideration of our approach to the design, evaluation, and deployment of ADS. As a result, this Perspective develops an integrated framework for studying ADS in social systems, shifting current priorities from a purely prediction-based paradigm towards an intervention-oriented view that a
arXiv:2606.25484v1 Announce Type: new Abstract: Existing approaches to e-scooter mobility hub planning lack city-type-specific causal evidence. Demand models are typically correlational, built on proprietary trip data, and do not distinguish how driver profiles vary across urban typologies. This paper presents a three-phase agentic AI framework that constructs a Causal Template Library from public GBFS data across 29 German cities, encoding which environmental features causally drive hotspot demand for each combination of city type (large, university, industrial, hilly) and cluster type (core, peripheral). A large language model (LLM) orchestrated causal discovery pipeline adapts algorithm selection to local data conditions across 57 city-cluster units. The library reveals systematic variation. Core demand is driven by activity access and transit proximity, while peripheral demand responds to built form, with city-type-specific patterns supporting transferable siting templates. A plann
arXiv:2606.25308v1 Announce Type: new Abstract: Behavioral detectors provide valuable insights into learner motivation and self-regulation. Among these, delayed start, a new session-level detector, has shown great promise as a valid behavioral measure that generalizes well across systems. In this paper, we examine cross-subject predictive validity of delayed start behavior. Using iReady data from 711 grade 7 students, we find delayed starts during Math practice are predictive of standardized test performance in both Math ($\beta$=.07 SD, p=.02) and English ($\beta$=.10 SD, p=<.001). Additionally, using mixture modeling and sensitivity analyses, we use a data-driven strategy to operationalize the identification of delayed starters in practice. We identify two underlying sub-groups of interest: "early starters" (<5 minute average delay, 20% of students) and "chronic delayers" (>13 minutes average delay, 20% of students). Relative to students in neither sub-group, early starters experienc
Math is a fundamental part of K-12 education, but students often face significant challenges in mastering increasingly challenging math concepts.
I've worked in edtech for almost 10 years now in B2B, B2C, and nonprofit contexts. I've seen real product market fit, and a lot of poor product market fit. Edtech has been one of the largest tech disappointments of the internet era. The internet has transformed everything about how people learn. I always joke that Youtube is actually the best edtech product. And now, I guess chatGPT and other LLMs. But these products have a lot of problems, specifically around accuracy, pedagogy and lack of assessment. (Research shows low-stakes assessment is when the moment of learning often happens.) Within the "Ed tech space", a lot of products have failed in my view. The best product I built was free online science simulations (virtual labs). I've worked on products that were financially successful but its debatable if they helped helped users learn much. Edtech companies that sell to parents are making a product for parents. The goal is often to make parents feel good about the choices they are ma
In my classroom, students increasingly ask for relevant content. Students want to know how what they are learning in school relates to the world beyond the classroom. They want to be engaged in their learning.
As school districts navigate a rapidly evolving digital landscape, IT and academic leaders face a growing list of challenges--from hybrid learning demands and complex device ecosystems to rising cybersecurity threats and accessibility expectations.
Reading is competing for attention in a world built for scrolling. A recent University of Florida study found that the share of Americans who read for pleasure on an average day dropped from 28 percent in 2003 to just 16 percent in 2023.
From data privacy and staff readiness to classroom fit and long-term cost, here are the questions schools should ask before investing in AI.
Schools are finding new, data-driven ways to re-approach gifted and talented programs -- with a focus on inclusivity.
Our students are coming of age in a world that demands global competency. From economic interdependence to the accelerating effects of climate change and mass migration, students need to develop the knowledge and skills to engage and succeed in this diverse and interconnected world.
"As kids get older, it's still important for adults to monitor the level of content and what is being offered to them."
EXTON, PA – May 6, 2026 – Red Rover, the fastest-growing provider of modern human capital management solutions for K-12 ... Read more
Libraries are more than a quiet corner of school where students can pick up a book now and then--they are vibrant learning environments that support classroom curriculum, spark curiosity and creativity, and enhance vital literary skills
District communication is most powerful when it reflects what families already see and experience daily.
When I first stepped into my role overseeing student data for the Campbell County School District, it was clear we were working against a system that no longer served us.
Data has become one of the most important strategic assets in education. Yet across institutions, publishers, and edtech companies, it often remains fragmented, inconsistently governed, and difficult to use with confidence.
Hey everyone! I know this forum is 'notorious' for having more experienced and skilled coders but if I figured this might be relevant for some of you: Coding The Future is a program where we match people who are passionate about computer science to teach students interested in learning. If this sounds like an opportunity that you'd like to participate in please fill out this form so we can best match you with a tutee. Tutoring sessions will be 30 minutes weekly virtually. All tutoring is done for free, so if you are interested in becoming a tutor you will get community service. We provide tutors with the resources to be an effective teacher, and regularly check in with our tutors and tutees to make sure the process is going smoothly. Please note that dedicated tutors may be offered leadership roles, and if you are interested in taking on more leadership within the program, for example becoming a local director of programming or curriculum developer, please let us know. You can also mai
McHenry, Ill., Feb. 19, 2026 – Building on its September 2025 introduction into the public library market, Follett Content today ... Read more