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:2607.21570v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for medical education, but most existing systems focus on localized interactions such as question answering or single-turn feedback, rather than organizing an entire clinical case into a decision-centered learning trajectory. We introduce \textit{MedGame}, a framework that transforms static clinical cases into structured, executable storytelling games. MedGame uses a dual-engine design: a Medical Narrative Designer synthesizes case-grounded clinical storylines with states and decision nodes, while a Story Director converts them into dependency-aware multimodal orchestration plans rendered by our released interactive platform. We construct MedGame Bench, a 5,000-case benchmark and evaluation protocol for Medical Narrative Generation and Story Direction. Experiments show that task-specific fine-tuning substantially improves open-source LLMs on MedGame Bench and narrows the gap with commercial mode
arXiv:2607.21019v1 Announce Type: cross Abstract: Traditional approaches to wearable health signal analysis, such as smartwatches, are constrained by rigid analytical frameworks and limited personalisation. The emergence of LLM agents creates a new opportunity for Personal Health Agentic Analysis, where health insights can be generated adaptively and in context. However, currently there is no open-source locally deployable platform capable of processing personal health data in real time while preserving privacy. We present HiMe, a locally deployable, privacy-first agent platform that is fully compatible with real-time health data ecosystems across a wide range of wearable devices. HiMe is guided by three design principles. The database is treated as a first-class component. Effectiveness and efficiency are jointly optimised to achieve a low-cost Pareto-optimal balance. Data are processed in real time while the user is modelled over the long term. Together, these principles make it prac
arXiv:2607.20903v1 Announce Type: cross Abstract: We examine whether richer visual representations yield more human-aligned measures of urban engagement, using 61 first-person city-walk videos from YouTube segmented into over 50,000 ten-second clips and represented across four modalities: spatiotemporal video features, temporally averaged images (TAIs), audio embeddings, and text-based semantic descriptions. Spearman correlation analysis reveals the expected ordering along the temporal-richness continuum, with video features showing the strongest continuous alignment. However, this ordering breaks down under binary classification of high- versus low-engagement moments (the paradigm most commonly used to train perceptual scoring models), where TAIs consistently match or outperform video across most classifiers and quantile thresholds. An independent two-alternative forced-choice study on Amazon Mechanical Turk confirms that this parity reflects human judgment: participants identified en
arXiv:2607.20796v1 Announce Type: cross Abstract: This paper examines the question of whether artificial intelligence (AI) systems can be creative, approached from the dual perspective of a researcher trained in electrical engineering, pattern recognition, machine learning, and neural networks, who has also spent most of his life engaged in the arts as actor, stage and film director, writer, composer, and visual artist, and in philosophy. Drawing on Margaret Boden's foundational framework, both her three properties of creativity (novelty, surprise, and value) and her three types of creative processes (combinatorial, exploratory, and transformational), the paper argues that AI systems are structurally incapable of creativity in its strongest sense. While they exhibit genuine capability in the domain of combinatorial creativity, they are significantly bounded in exploratory creativity, and fundamentally incapable of transformational creativity. The paper further argues that the most impo
arXiv:2607.20706v1 Announce Type: cross Abstract: The growing need for secure and convenient authentication methods has led to the increasing popularity of biometric solutions. In addition to traditional and popular methods, such as fingerprint or iris scanning, voice-based approaches are also employed. User identity verification based on voice is conducted using Automatic Speaker Verification (ASV) systems. Despite their many advantages, these systems are sensitive to various types of attacks and acoustic noises, which can reduce verification accuracy. This work examines the potential to improve the performance of ASV systems by using hybrid feature sets that combine different signal representations, starting with widely-used Mel-Frequency Cepstral Coefficients (MFCC), through Constant Q Cepstral Coefficients (CQCC) and ending with the innovative RAB descriptor. Experiments were conducted on recordings from the Google Speech Commands dataset under two scenarios: in clean conditions an
arXiv:2607.20574v1 Announce Type: cross Abstract: Broken Object-Level Authorization (BOLA), also known as Insecure Direct Object Reference (IDOR), has topped the OWASP API Security ranking since 2019 and is the root cause of some of the largest exposures of applicant data in recruitment technology. The defining feature of this flaw class is that a malicious request is byte-for-byte indistinguishable from a legitimate one, which is precisely why web application firewalls and single identity scanners fail to catch it. We present AuthProbe, an open-source, black-box scanner that detects BOLA and IDOR in HTTP APIs by driving its tests from an OpenAPI specification and by acting under two or more identities that the operator controls. AuthProbe discovers, for each identity, the objects that identity legitimately owns, then attempts to read one identity's objects while authenticated as another and confirms a leak by comparing the response against a ground-truth fetch by the true owner. It al
arXiv:2607.20520v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formulations as interchangeable and conflates reasoning errors with interface failures. This paper investigates representation robustness in LLM-based mathematical problem solving by systematically varying surface representations of the same underlying problems, including story problems, word-equations, symbolic equations, and isomorphic paraphrases. Using a curated dataset of mathematically equivalent problems, we evaluate five contemporary LLMs under a direct answer generation condition. We find substantial representational sensitivity: models frequently change correctness across equivalent formulations, with nontrivial flip rates across story, symbolic, and word-equation variants. We also observe systematic regressions under isomorphic reformulations, showing that even subtle paraphrase-le
arXiv:2607.20461v1 Announce Type: cross Abstract: Present implementations of artificial intelligence (AI) ethics do not adequately take feelings, or affect, into account. If AI should be aligned with human ethics, it seems reasonable to thoroughly investigate the possibility of AI behaviour that mirrors virtuous human ethical conduct, where feelings play a role in the actions, judgements or statements one makes. Furthermore, while prominent theories of normative ethics are often discussed in terms of their differences and shortcomings, Virtue, Consequentialist, and Kantian Deontological ethics all share a common feature of considering human feeling to some degree while the popular descriptive ethics theory, Moral Foundations Theory, positions feelings as central to many of its foundations. Therefore, in the present paper, a data set of moral valence is proposed, consisting of 500 annotations by six human participants for both action/judgement and consequence moral valence, ranging from
arXiv:2607.20428v1 Announce Type: cross Abstract: This study evaluated a retrieval-augmented, multi-agent large language model (LLM)-driven, human-in-the-loop framework for detecting cutaneous immune-related adverse events (cirAEs) from clinical notes. Compared with unassisted manual review, the LLM-assisted workflow improved accuracy (F1 = 0.88 vs 0.77), inter-rater agreement measured by Cohen's kappa (kappa = 0.82 vs 0.50), and reduced average review time by approximately half. This framework pilots how LLMs can be applied to identify immune-related toxicities across organ systems and, more broadly, enable accurate, scalable, and transparent adverse event data extraction.
arXiv:2607.21527v1 Announce Type: new Abstract: Passive sensing for wellbeing uses smartphones and wearables to continuously collect human behavioral data and applies ML/AI models to infer psychological states and behaviors (e.g., depression, cognitive load). These systems are increasingly adopted in high-stakes settings (e.g., hospitals, universities), yet fairness research remains limited---primarily to post-hoc, identity-based comparisons of model performance. However, passive sensing combines heterogeneous sensing infrastructures, indirect behavioral inference, and longitudinal deployment---characteristics that, while not exclusive to the domain, are jointly pronounced here and raise two underexplored questions: (1) what additional sources of inequity arise from these characteristics, and (2) how do such inequities propagate beyond algorithmic audits across the system lifecycle? To address this gap, we conducted semi-structured interviews with 14 researchers and practitioners acros
arXiv:2607.21513v1 Announce Type: new Abstract: Large language models can make conversational product advisors fluent but opaque. If they hide the logic behind a ranking and the evidence for a recommendation inside natural-language replies, they challenge users' ability to understand, trust, and steer the results. One response is to build transparency into the advisor. We report a formative, moderated think-aloud usability study of one such system: a chatbot for laptop search with constrained natural-language generation, an on-demand ranking explanation, and a comparison feature. Seven participants completed three laptop-search tasks and reported post-task usability measures. We coded their sessions into severity-rated usability problems. Ease and satisfaction during the tasks were high, but two findings stand out. First, transparency by design did not guarantee understanding: several participants valued the ranking explanation in principle, yet it caused the most severe problem. Secon
arXiv:2607.21502v1 Announce Type: new Abstract: Political legislation affects the well-being and livelihoods of constituents. In the U.S. Congress a representative's voting record on bills and legislation is public. These bills have themes associated with them, such as veterans' affairs, coastal monitoring, agricultural appropriations, etc. A bill on veterans' affairs may affect a constituency differently if they have a high percentage of veterans. In this work, we demonstrate how congressional vote outcomes can be merged with typical geographic information systems (GIS) data to help compare a legislator's votes with the geographies of their constituencies to measure the association between district features and legislators' decisions. We retrieved and tagged bills from the 118th U.S. House of Representatives (Jan. 2023 - Jan. 2025) by manually assigning each bill a set of themes. We then retrieved spatial data at the congressional district level related to each theme. We built a backe
arXiv:2607.21468v1 Announce Type: new Abstract: People often use handwritten notes and sketches to externalize ideas for ideation. To integrate large language models (LLMs) into this practice, we propose Thinkink. Prompts can be handwritten text or drawn sketches with LLM-generated responses visualized as ink-like text and sketches spatially integrated into a shared canvas. A semantic tree streamlines ink interpretation, and a lightweight UI provides explicit control using a state machine. The tool was designed using a three-stage process. A formative study (N=12) examined current practices with conventional and digital inking methods. The results informed a technical probe for a diagnostic study (N=6) identifying usability and human-LLM interaction challenges. This motivated the design of Thinkink, with a final study (N=10) examining how people incorporate it into their ideation practices. We contribute design implications and a tool for ink-native LLM interaction where the user and L
arXiv:2607.21394v1 Announce Type: new Abstract: Creative writing increasingly integrates AI assistance, yet current tools miss in-situ moments when writers draw inspiration from real-world experiences. We envision Context-aware Reality-Fiction Transformation (CRAFT), an approach for AI glasses that translates daily experiences into fiction narratives. We explored its desirability, feasibility, and potential viability through three studies. Interviews with nine writers yielded desires and three design goals: 1) augmenting in-situ perception to bridge reality-fiction gaps, 2) promoting authenticity grounded in real-world experiences while maintaining fictionalization, and 3) preserving creative agency, enjoyment, and life-art boundaries. Co-design workshops with 16 writers and researchers operationalized these goals into concrete interaction mechanisms using a technology probe. We then conducted supported field trials with eight writers across 24 sessions using a refined probe, revealing
arXiv:2607.21289v1 Announce Type: new Abstract: In experiential learning environments, Deaf and hard of hearing (DHH) students often experience ``split attention,'' dividing their focus among tasks, instructors, and access providers. Augmented reality (AR) has been proposed as a means to centralize communication access within the student's field of view; however, little is known about how such systems affect the instructors, interpreters, and captioners who support access in these settings. We present a formative, expert-based evaluation of ARRAE, an AR-mediated communication access ecosystem, examining the experiences of an instructor, an American Sign Language (ASL) interpreter, and a real-time captioner in a simulated laboratory environment. Comparing in-person, traditional remote, and AR-mediated access, our findings suggest that AR reconfigures accessibility labor and interactional practices by redistributing communication, attention, and awareness across participants. While AR-me
arXiv:2607.21257v1 Announce Type: new Abstract: As large language models (LLMs) become integrated into programming education, learner-facing systems increasingly differ in how that assistance is bounded, enacted, and controlled. These governance decisions are often described implicitly, making it difficult to compare systems in educationally meaningful ways. To address this gap, we conduct a scoping review and qualitative synthesis of 90 peer-reviewed LLM-based programming support systems in CS education. We analyze assistance governance through three dimensions, which we refer to collectively as PEA: Policy, capturing what forms of help are allowed or restricted; Enforcement, capturing how those boundaries are operationalized through interaction and system behavior; and Authority, capturing who can configure, adapt, or override them during use. Our findings show that systems often share similar pedagogical goals, but implement those goals through varied enforcement mechanisms. At the
arXiv:2607.20835v1 Announce Type: new Abstract: Audio description (AD) makes film and television accessible to blind and low-vision (BLV) audiences by narrating characters' actions. However, in scenes with lots of dialogue, AD often omits important actions because it is constrained not to overlap with speech. It is not yet known how to convey characters' actions during dialogue. We present Sonic Stage, a system that transforms dialogue videos into interactive spatial soundscapes, enabling BLV audiences to intuitively understand characters' actions and movements through immersive auditory cues. Sonic Stage conveys essential visual information during dialogue through three auditory techniques: (1) spatialized dialogue to represent spatial layout, (2) diegetic sound to convey character actions, and (3) interactive descriptions to provide context-specific visual details. Evaluation with 12 BLV viewers showed that Sonic Stage significantly improved video comprehension, spatial presence, and
arXiv:2607.20775v1 Announce Type: new Abstract: We present Flint, an intermediate language that enables authors to create high-quality visualizations from concise, semantics-driven specifications without explicitly configuring low-level parameters such as scales, axes, and formatting. Unlike prior systems that infer default configurations from surface-level data representations, often producing brittle choices, Flint introduces a hierarchical data semantic model that allows users to specify the meanings of data fields structurally and helps the compiler derive appropriate visualization configurations. From a concise specification, the system generates and optimizes library-agnostic visualization configurations and translates them into complete, executable specifications for multiple target grammars, including Vega-Lite, Apache ECharts, and Chart.js. We demonstrate that Flint simplifies the authoring process without compromising on visual quality, and it is an effective intermediate lan
arXiv:2607.20773v1 Announce Type: new Abstract: Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exchanges. Researchers studying HCI and UI use moderated usability sessions, interviews, surveys, transcript analysis, and static prototypes. However, static prototypes provide limited opportunities to study interaction with live AI systems or systematically control how an LLM behaves across participants and scenarios. Conversation transcripts reveal little about how users formulate, revise, and hesitate over prompts before submission. We designed the Human--AI Research Platform (HARP) for researchers, designers, and anyone who has ever wondered, `What if AI did this?' HARP places participants in controlled mock scenarios with live, configurable AI agents. Researchers can control agent prompts, model parameters, response characteristics, and experimental conditions; trigger surveys at predefined moments;
arXiv:2605.21624v2 Announce Type: replace-cross Abstract: Delay and Disruption Tolerant Networks (DTN) are critical for reliable communications in challenged network environments, particularly for space systems where end-to-end connectivity cannot be guaranteed. We present an open-source, full-stack implementation of the Bundle protocol for communicating with the International Space Station (ISS), with complete security features including Bundle Authentication Block (BAB), Payload Integrity Block (PIB), and Payload Confidentiality Block (PCB) using HMAC-SHA256 and AES-256-CBC encryption. The system includes bundle fragmentation and reassembly, priority-based queuing, custody transfer with ACK/NAK mechanisms, and automatic retransmission. Our system also includes a frontend facilitated by a modern responsive web interface. We consider this work highly relevant in the context of computer networking because: i) it demonstrates a full stack, open-source, freely available implementation of
arXiv:2602.07589v3 Announce Type: replace-cross Abstract: Quantum computing is increasingly practised through programming, yet most educational offerings emphasize algorithmic or framework-level use rather than software engineering concerns such as testing, abstraction, tooling, and lifecycle management. This paper reports on the design and first offering of a cross-listed undergraduate--graduate course that frames quantum computing through a software engineering lens, focusing on early-stage competence relevant to software engineering practice. The course integrates foundational quantum concepts with software engineering perspectives, emphasizing executable artifacts, empirical reasoning, and trade-offs arising from probabilistic behaviour, noise, and evolving toolchains. Evidence is drawn from instructor observations, supplemented by anonymous student feedback, a background survey, and inspection of student work. Instructor observations and inspected work artifacts indicated that stu
arXiv:2510.15236v2 Announce Type: replace-cross Abstract: Contemporary AGI evaluations report multidomain capability profiles, yet they typically assign symmetric weights and rely on snapshot scores. This creates two problems: (i) equal weighting treats all domains as equally important when human intelligence research suggests otherwise, and (ii) snapshot testing can't distinguish durable capabilities from brittle performances that collapse under delay or stress. I argue that general intelligence -- in humans and potentially in machines -- is better understood as a homeostatic property cluster: a set of abilities plus the mechanisms that keep those abilities co-present under perturbation. On this view, AGI evaluation should weight domains by their causal centrality (their contribution to cluster stability) and require evidence of persistence across sessions. I propose two battery-compatible extensions: a centrality-prior score that imports CHC-derived weights with transparent sensitivi
arXiv:2606.00048v2 Announce Type: replace Abstract: Prior work establishes that instruction-tuned LLMs exhibit left-of-center political bias, but measures it exclusively through abstract questionnaires. We show this does not generalize to concrete policy decisions. We introduce a dual-instrument methodology grounded in Swiss direct democracy: the Smartvote questionnaire (75 policy questions), administered to 66 LLMs and compared to 184 elected members of the Swiss National Council, and 48 real federal referenda (Volksabstimmungen) put to 9 flagship LLMs in four national languages and three information conditions, compared to actual outcomes and party recommendations (Parolen). The instruments disagree. (1) The left-to-right agreement gradient that dominates Smartvote (replicating prior work; Cohen's d = 3.64) shifts to center-peaked on referenda, where models align most with centrist Die Mitte and FDP rather than leftist SP and Gruene (Wilcoxon p = 0.008). (2) For some models the langu
arXiv:2411.00631v2 Announce Type: replace Abstract: This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available through the lens of Critical Digital Pedagogy. Following PRISMA-ScR guidelines, we collected 10 studies from academic databases focusing on both learner and teacher agency in GenAI-enabled environments. We conducted an AI-supported hybrid thematic analysis that revealed three key themes: Control in Digital Spaces, Variable Engagement and Access, and Changing Notions of Agency. The findings suggest that while GenAI may enhance learner agency through personalization and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts. This review highlights gaps in the current research on GenAI's impact on agency. These findings have implications for educational policy and practice, suggesting the need for frameworks that promote equitable access while pr
arXiv:2607.21306v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems. While guidance from AI assistants can scaffold thinking and foster learning, such benefits depend on how they help--for instance, intervening too early or too frequently may hinder true learning and cognitive engagement. Yet how AI systems navigate intervention decisions during problem-solving remains poorly understood. Here, we introduce Int-Bench, a simulation-based benchmark for evaluating LLM interventions during learning. Int-Bench simulates a "student" solving a problem while a "teacher" monitors the student's reasoning and decides whether, when, and how to intervene. Across three domains--code debugging, mathematics, and brain teasers--we evaluate LLM teachers on the frequency and timing of interventions, as well as their impact on both immediate task success and generalization to new problems. We also compare
arXiv:2607.21178v1 Announce Type: cross Abstract: Single transferable vote (STV) is a multi-winner preferential proportional electoral system. The margin is the smallest number of ballots that need to be manipulated to alter the set of winners. If we can compute the margin of an STV election, or a reasonable lower bound on the margin, we can use recent advances in auditing research to conduct a risk-limiting audit of the election's winners. Knowledge of the margin also provides insight into whether uncovered mistakes, or a known error rate in ballot interpretation, could have influenced the outcome. This paper presents substantial improvements on an existing algorithm for computing lower bounds on the margin of an STV election. These improvements allow us to compute higher lower bounds for real STV elections, making mismatch-based risk-limiting audits more practical.
arXiv:2607.21082v1 Announce Type: cross Abstract: Existing methods for risk-limiting audits typically focus on certifying individual contests. In parliamentary elections, however, the politically relevant outcome is often whether a party has won enough seats to form government, not whether every reported seat outcome is correct. Extending on the work of Mohanty et al. (2019), we formulate the certification of a parliamentary majority as a partial conjunction testing problem: it is enough to verify that the reported winning party truly won at least a majority of its reported seats. Building on the SHANGRLA auditing framework, we construct a sequential audit statistic for the majority outcome by combining seat-level statistics. We then propose adaptive sampling strategies that allocate auditing effort across seats, including variants that learn to avoid spending excessive effort on seats that appear unlikely to have been truly won. Using simulations based on synthetic and real data, from
arXiv:2607.21063v1 Announce Type: cross Abstract: Almost every large language model that reaches a broad audience is quantized: trained in full precision, then compressed for efficiency. This step is assumed harmless and its safety is rarely re-checked. We find its principal side effect is increased bias that standard safety evaluation misses. Holding the model, its training, and the prompts fixed, a quantized model still refuses harmful requests, still avoids over-refusing benign prompts, and still selects the unbiased multiple-choice answer. Yet asked an open-ended question, the same model volunteers stereotypes in all eight languages we probe, in roughly one in four open-ended answers under an independent judge (~24% to ~27% across the compression ladder): it passes every standard check and still reaches users measurably more biased. The selective gap is a robust finding; whether open-ended bias further increases with compression is less certain, sensitive to the judge that scores i
arXiv:2607.20923v1 Announce Type: cross Abstract: Large language models (LLMs) have rapidly and significantly entered scientific workflows, but it remains unclear how their diffusion is associated with changes in scientists' strategies in research directions and team building. We link PubMed Central full text with OpenAlex publication and collaboration histories for 775,323 scientists and analyze CRediT contribution statements from 137,120 multi-author papers. After 2022, scientists increasingly published across more intellectually distant fields and entered fields in which they had not previously worked. These increases in interdisciplinarity and exploration were especially pronounced among established scientists and scientists from non-English-speaking low- and middle-income countries. Authors with stronger AI-writing signals were already more interdisciplinary and exploratory before the widespread adoption of LLMs, and the gap widened further after 2022 compared with authors with we
arXiv:2607.20542v1 Announce Type: cross Abstract: A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicines. This problem is complicated by limited high-quality data, which restricts the applicability of traditional data-driven techniques. We propose a novel decision-aware machine learning framework for essential medicines allocation, which additionally leverages multi-task learning to ensure sample efficiency and catalytic priors to ensure equitable allocation. In collaboration with the Sierra Leone national government, we performed a staggered, nationwide deployment of our system as a decision support tool. Our econometric evaluation finds an estimated 19% increase in consumption of allocated products in treated districts, demonstrating its efficacy at improving access to essential medicines. Our tool was subsequently scaled nationwide, covering an estimated 2
arXiv:2607.20446v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly used by students to generate natural language responses and program code, there is growing interest in whether LLMs themselves can be used to distinguish AI-generated work from human-authored submissions. In this paper, we investigate the extent to which LLMs can detect their own generated content across multiple educational task types, including programming exercises, reflective writing, and short-answer questions. Using authentic student responses and multiple variants of LLM-generated answers, we evaluate detection performance under different prompting strategies and output formats. Our study addresses three research questions: (1) how accurately LLMs can identify their own outputs across task domains, (2) how detection effectiveness is influenced by factors such as prompt design, response length, and task type, and (3) what characteristics of LLM-generated responses contribute to succ
arXiv:2607.21549v1 Announce Type: new Abstract: Technology-facilitated abuse (TFA), the use of digital technologies to stalk, harass, monitor or threaten others, has become a pervasive form of interpersonal harm. As victims turn to online sources for guidance, responses can shape how they assess risks, interpret abuse, and choose protective actions. We present a large-scale evaluation of online support for TFA victims across three channels: web search, peer-support forums, and conversational AI systems. Drawing on a decade of victim narratives from r/Stalking, we use qualitative coding and supervised classifiers to construct a dataset of TFA queries spanning 11 categories of technology misuse. We simulate these queries across the three channels and evaluate responses using a unified framework spanning technical, social, and safety dimensions. The framework assesses relevance, accuracy, actionability, persuasiveness, and understandability, alongside platform risks and support characteri
arXiv:2607.21534v1 Announce Type: new Abstract: The spread of generative AI (GenAI) in higher education has raised concerns that students offload cognitive effort to AI, earning high grades without learning. If this "GenAI substitution hypothesis" is true, grades should rise disproportionately in GenAI-susceptible courses--those relying more on assessments like take-home problem sets and essays rather than in-class exams. Substitution could also affect student satisfaction, measured here as self-reported understanding and interest in the subject, which prior research links to assessments. We test the substitution hypothesis using syllabus and administrative data from a large U.S. university (2015-2025; 156,135 students; 87,936 course offerings). We measure courses' GenAI susceptibility using a human-validated LLM pipeline to extract assessment types from syllabi, and use a differences-in-differences design comparing outcomes across courses before and after ChatGPT's release, while mode
arXiv:2607.21462v1 Announce Type: new Abstract: As AI governance moves from benchmark scores toward auditable oversight, a central question is how reviewers can tell whether an LLM-generated audit report is actually supported by evidence. This paper studies that question in passage-anchored policy audits, where a report must interpret a given policy passage and cite evidence for its claims. We introduce a controlled evaluation framework that holds the passage, rubric, and auditor model fixed while changing only the evidence interface supplied to the auditor. Across 60 AGORA policy cases, we generate 600 structured reports under ten evidence conditions, including passage-based evidence, internal model evidence, a hybrid package, and a shuffled control that preserves evidence format while breaking case relevance. Five human reviewers evaluate the primary interfaces for correctness, passage grounding, diagnostic usefulness, and evidence misuse. The results show that internal evidence chan
arXiv:2607.21317v1 Announce Type: new Abstract: Artificial intelligence is being deployed across the Global South at a pace matching or exceeding the Global North, yet AI governance has not kept pace, and the gap is far wider in the South. Drawing on a decade of AI audit practice across Latin America, Sub-Saharan Africa, and Asia Pacific (the only fully published second-party audit of a deployed system in the region, Robot Laura in Brazil; two completed but unreleased national audits, of a child-welfare risk model and a public-employment matching algorithm; thirteen Responsible AI Assessments; and a regional landscape analysis), this paper documents patterns in the sparse field of Global South AI evaluation and explains why it so rarely occurs. We count fewer than twenty published second- and third-party audits of deployed systems across the region over the past decade, against hundreds of documented public-sector algorithms and multibillion-dollar national AI investments. We find four
arXiv:2607.20695v1 Announce Type: new Abstract: Human judgment is fundamentally prone to error. A promise of AI is that it will rid decisions of bias and ensure a fairer and safer world for all. Yet research unequivocally demonstrates that LLMs exhibit consequential sociocognitive biases. We alert readers that bias in AI (a) is covert and ironically a feature of alignment goals, (b) is not merely a mirror, but an amplifier of human bias, (c) intensifies across model generations, and (d) even transmits bias to humans. Given the potentially seismic and ubiquitous influence of AI on decision making, we propose countermeasures that are diagnostic, regulatory and operational.
Article URL: https://restofworld.org/2026/edtech-funding-collapse-k12-startups-ai-workforce/ Comments URL: https://news.ycombinator.com/item?id=47887985 Points: 2 # Comments: 0
Flint offers personalized learning, using AI, across a range of subjects.
Article URL: http://startupworks.co/blog/2015/01/23/will-2015-be-year-medical-and-children-education-apps/ Comments URL: https://news.ycombinator.com/item?id=8934536 Points: 2 # Comments: 0
By proactively handling negative individuals, school leaders can create a psychologically safe learning environment for everyone.
Along with Ambros Therapeutics’ merger with Werewolf Therapeutics, the combined company will raise $150 million in a private placement. Ambros is in Phase 3 testing with a drug developed for treating a type of pain that does not respond to currently available medicines, including opioids. The post Ambros Therapeutics’ Path for Pain Drug Goes Through Reverse Merger With Werewolf appeared first on MedCity News .
Three departments walk into a meeting with three different enrollment numbers — all of them technically correct. Most institutions are surrounded by data: reports, dashboards and transactional systems pulling from every corner of campus. And yet, when a leader needs to make a fast, confident decision, the data doesn’t quite deliver. It can be fragmented and inconsistent. I think about data strategy as everybody’s opportunity and no one area’s problem. Academic affairs, enrollment, student success and finance — each of these teams is using data in smart ways to solve local problems. What a CIO…
For nearly three decades, E-Rate has made connectivity and network infrastructure accessible for even the smallest and most rural schools. Now, the Federal Communications Commission is taking another look at the program and deciding whether it should continue in its current form. The reason? Leaders have raised questions about whether screen time on E-Rate–funded networks can be tied to educational purposes, especially in light of school board concerns about screen time. Our team at CDW has been talking to K–12 districts about E-Rate for years, and lately we've been hearing that school…
Today, 3D modeling and printing technology can make a dramatic impact — even shortening some operations by 30-90 minutes. Looking ahead, that reduction will only grow as the technology is further integrated and becomes more advanced. The post The Pulse of Innovation: How 3D Models Can Prepare Surgeons for the Operating Room appeared first on MedCity News .
Gaps in reimbursement oversight and recovery will only widen, unless plans modernize how they detect, investigate, and recoup improper payments. The post Modernizing Payment Integrity in an Era of Systemic Fraud appeared first on MedCity News .
A new report from InsideTrack, a national student success coaching nonprofit, reveals that a five-year coaching initiative across 39 Historically Black Colleges and Universities (HBCUs) helped stopped-out students re-enroll at 2.5 times the national average while keeping actively enrolled students on track at a fall-to-spring retention rate of 83.8 percent. The post Student coaching can help thousands of HBCU students re-enroll and graduate appeared first on eCampus News .
The best AI-powered tutors guide students towards genuine learning
arXiv:2605.23057v2 Announce Type: replace-cross Abstract: RequestRouter is a lightweight request-boundary controller for reducing the latency and energy cost of single-GPU large language model inference. Rather than serving all requests with one static configuration, the system uses cheap request-level features to select one fixed inference mode per request, including FP16, quantized inference, speculative decoding, prefix caching, continuous batching, and hybrid modes such as GPTQ plus prefix caching and INT8 plus continuous batching. We evaluate RequestRouter using an 8B instruction-tuned language model served through vLLM on NVIDIA A100 GPUs. Across the full-scale A100 evaluation--26,500 fixed-mode evaluations followed by 3,500 online-controller evaluations, for 30,000 measured inference executions in total--the controller achieves a 2.10x mean latency speedup over FP16 and a 0.48x energy ratio on deployment-style workloads. A smaller matched evaluation with repeated measurements pr
arXiv:2604.06501v2 Announce Type: replace-cross Abstract: Analogical reasoning is a hallmark of human intelligence, enabling us to solve new problems by transferring knowledge from one situation to another. Yet, developing artificial intelligence systems capable of robust human-like analogical reasoning has proven difficult. In this work, we train transformers using Meta-Learning for Compositionality (MLC) on an analogical reasoning task (letter-string analogies) and assess their generalization capabilities. We find that letter-string analogies become learnable when guiding the models to attend to the most informative problem elements, induced by including copy tasks in the training data. Furthermore, generalization to new alphabets improves when models are trained with more heterogeneous datasets. For the best training run, our 3-layer encoder-decoder model performs on par with frontier models on our letter-string analogy datasets. The MLC approach also enables some generalization to
arXiv:2603.21875v2 Announce Type: replace-cross Abstract: Speech deepfake source verification systems aims to determine whether two synthetic speech utterances originate from the same source generator, often assuming that the resulting source embeddings are independent of speaker traits. However, this assumption remains unverified. In this paper, we first investigate the impact of speaker factors on source verification. We propose a speaker-disentangled metric learning (SDML) framework incorporating two novel loss functions. The first leverages Chebyshev polynomial to mitigate gradient instability during disentanglement optimization. The second projects source and speaker embeddings into hyperbolic space, leveraging Riemannian metric distances to reduce speaker information and learn more discriminative source features. Experimental results on MLAAD benchmark, evaluated under four newly proposed protocols designed for source-speaker disentanglement scenarios, demonstrate the effectivene