Named after the hundred-eyed watchman of Greek myth, Argus watches the education landscape: spotting new opportunities, pressure-testing the ventures we're building, and tracing every read back to the real-world signals behind it.
The evidence library: the raw signals the pipeline is watching across the education ecosystem. Every idea is built from these.
arXiv:2608.05619v1 Announce Type: new Abstract: We present CaRing, a ring worn on the base knuckle of the index finger, a wearable system for detecting the start and end of mouse use to help prevent Carpal Tunnel Syndrome, in which the damage to the median nerve is permanent. CaRing senses finger movement, which neither a software timer nor a wrist-worn device detects. The displacement reported by an optical flow sensor is accumulated into a running value, then a zero point is measured while the hand rests on the desk at the start of each session. With this formulation, the start and end thresholds are expressed relative to the session's zero point. CaRing does not introduce any per-user parameter. We empirically demonstrate that approximately $90\%$ of start and end events are detected within two seconds of the researcher's label, using 35 recordings and a lab study with ten users.
arXiv:2608.05614v1 Announce Type: new Abstract: As AI systems become increasingly integrated into consequential domains such as healthcare, journalism, education, scientific research, organizational decision-making, and defense, effective human-AI collaboration has emerged as a critical challenge. However, the sociotechnical risks that undermine collaboration are often studied in isolation, obscuring the recurring failure mechanisms that cut across domains. This paper presents a lifecycle-oriented synthesis of human-AI collaboration risks spanning four stages: task allocation, interaction, feedback, and adoption. Drawing on evidence from diverse application domains, we identify six recurring cross-domain risk clusters: Trust Miscalibration, Cognitive Burden, Accountability Gap, Capability Erosion, Goal Misalignment, and AI Anxiety and Technostress. We further propose a conceptual interaction model that illustrates how these risks emerge from sociotechnical drivers, interact through cas
arXiv:2608.05570v1 Announce Type: new Abstract: 360-degree video telepresence offers strong immersive potential but remains constrained by the limited resolution of current capture and display hardware. Many telepresence installations feature fixed viewpoints and largely static scenes, yet optimization strategies tailored to such setups have received limited attention. We present a multi-layer, ultra-high-resolution system for static 360-degree telepresence that combines an 8K panoramic camera with a rotatable 4K pan-tilt-zoom (PTZ) camera. Our approach builds a three-layer representation: (1) a tile-based ultra-high-resolution panoramic background, generated by offline stitching high-detail 4K PTZ scans onto the base 8K panorama to achieve effective resolution beyond native capture, and represented as a set of spatial tiles; (2) a dynamic update layer that composites foreground motions from the 8K stream via real-time high-resolution background matting; and (3) a region-of-interest 4K
arXiv:2608.05558v1 Announce Type: new Abstract: This paper examines Turing's 1948 report, "Intelligent Machinery", as an important conceptual source for the later imitation games. Its first contribution is to identify and integrate the design concepts underlying the 1948 chess-based imitation game: the possibility that intelligent machines may make mistakes, the exclusion of irrelevant physical features, the role of the human judge, and Turing's claim that intellectual activity consists mainly of search. The paper's second contribution is to argue that restricting the human contestant to a rather poor chess player increases the role of intellectual search and makes human behaviour more comparable to machine behaviour. This interpretation presents the 1948 game as a human-approximates-machine game and suggests that the imitation game framework can be used not only to ask whether machines imitate humans, but also to examine when human intelligence becomes machine-like under specific task
arXiv:2608.05487v1 Announce Type: new Abstract: Forecasting involves multiple forms of uncertainty, including both uncertainties that can be quantified directly (quantitative uncertainty) and those that must be expressed through experts' subjective judgments about the forecast and its context (qualitative confidence). Past work has established that conveying both quantitative uncertainty and qualitative confidence in forecasts can alter readers' decision making, but little research investigates the impact of how these forms of uncertainty are presented. In this work, we present three preregistered human-subjects studies (total n = 923) on how different methods of visualizing qualitative uncertainty alongside line charts' confidence intervals affects non-experts' decision making. In particular, we investigate representing qualitative uncertainty separately via text and icons, and integrated into quantitative confidence intervals via color, transparency, and a blurred stroke design. In E
arXiv:2603.27117v2 Announce Type: replace-cross Abstract: This paper investigates how gender shapes privacy decision-making in youth smart voice assistant (SVA) ecosystems. Using survey data from 469 Canadian youths aged 16-24, we apply multigroup Partial Least Squares Structural Equation Modeling to compare males (N=241) and females (N=174) (total N = 415) across five privacy constructs: Perceived Privacy Risks (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy Protective Behavior (PPB). Results provide exploratory evidence of gender heterogeneity in selected pathways. The direct effect of PPR on PPB is stronger for males (Male: \b{eta} = 0.424; Female: \b{eta} = 0.233; p < 0.1), while the indirect effect of ATT on PPB via PSE is stronger for females (Female: \b{eta} = 0.229; Male: \b{eta} = 0.132; p < 0.1). Descriptive analysis of non-binary (N=15) and prefer-not-to-say participants (N=39) shows lower trust and
arXiv:2604.17359v2 Announce Type: replace Abstract: Language models asked to simulate psychiatric patients produce cases that survive inspection one at a time and populations that match no real one. We gave GPT-4o-mini, Gemini-3-Flash, DeepSeek-V3 and GLM-4.7 each of 120 demographic cohorts under two framings, one written as a clinician enters a patient and one as a person describes themselves, and scored all 28,800 responses against survey-weighted PHQ-8 anchors derived from NHANES microdata. Case by case the output holds up: 97.3% of elevated presentations satisfy the DSM-5 gateway rule, violating it at 2.68% against a chance null of 10.4%. As populations, four things fail at once. Every benchmarkable group returns inflated by 2.8 to 5.5 PHQ-8 points, and 18.2% of simulated patients screen at the treatment threshold against 7.5% of adults. Population Black-White and Hispanic-White disparities do not survive the simulation, with two models attenuating each gap and two flattening or in
arXiv:2603.00059v3 Announce Type: replace Abstract: How well can AI-derived synthetic research data replicate the responses of human participants? An emerging literature has begun to engage with this question, which carries deep implications for organizational research practice. This article presents a comparison between a human-respondent survey of 420 Silicon Valley coders and developers and synthetic survey data designed to simulate real survey takers generated by five leading Generative AI Large Language Models: ChatGPT Thinking 5 Pro, Claude Sonnet 4.5 Pro plus Claude CoWork 1.123, Gemini Advanced 2.5 Pro, Incredible 1.0, and DeepSeek 3.2. Our findings reveal that while AI agents produced technically plausible results that lean more towards replicability and harmonization than assumed, none were able to capture the counterintuitive insights that made the human survey valuable. Moreover, deviations grouped together for all models, leaving the real data as the outlier. Our key findi
arXiv:2601.06182v2 Announce Type: replace Abstract: Establishing frameworks for promoting the realization of various activities on celestial bodies sustainably is of great significance for different contexts, such as preserving the scientific evidence and space heritage. Therefore, this research first proposes a conceptual model that covers the different types of features, attributes, and relationships between them to comprehensively delineate the surface/subsurface objects and related logical spaces on celestial bodies. It then implements this conceptual model as a CityJSON extension in such a way that allows for creating the three-dimensional (3D) geodatasets that represent these objects and spaces in a standardized manner. Moreover, the usefulness of this study is demonstrated through creating CityJSON datasets that include 3D models of exemplary surface/subsurface objects from the Moon and Mars, such as a historical landing site and related logical spaces, such as exclusion zones f
arXiv:2511.17124v2 Announce Type: replace Abstract: We present a domain-agnostic paired-comparison approach that uses Large Language Models (LLMs) to quantify sex/gender-related asymmetries in documented clinical decision-making. The method trains an LLM to emulate observed decisions, then evaluates sex-swapped pairs in which only sex is flipped, holding documented clinical content constant. We apply it to emergency triage, analyzing more than 140,000 Bordeaux University Hospital (France) admissions and testing methodological portability on MIMIC-IV, spanning a different language, population, and healthcare system. Fine-tuning Mistral NeMo 12B for triage prediction and using Mistral Small 24B for pair generation, we find otherwise identical presentations were more likely to receive a lower-severity predicted score as female than male: 1.1% (95% CI 0.9-1.3) in the French cohort, 2.2% (1.7-2.7) in MIMIC-IV. Predictions are sensitive to both tabular and textual sex markers, with the asymm
arXiv:2511.12686v2 Announce Type: replace Abstract: Artificial intelligence (AI) and high-performance computing (HPC) are transforming scientific capabilities and the way science is conducted. Yet their combined impact on scientific discovery remains poorly understood, as do inequalities in access to these capabilities across countries and institutions. Drawing on metadata from more than five million scientific publications (2000-2024) across 27 fields, we examine how the convergence of AI and HPC correlates with scientific breakthroughs. Our results show that this computational synergy is most pronounced at the scientific frontier: research combining AI and HPC is more likely to introduce novel ideas and achieve top-cited status than either conventional work or research using AI or HPC in isolation. We also document growing disparities in access to supercomputing resources and AI expertise, which are increasingly concentrated in a small number of regions (dominated by the United State
arXiv:2608.06141v1 Announce Type: cross Abstract: This paper focuses on automatic speech recognition (ASR) and ASR-mediated voice interfaces that shape access to public services, healthcare, and education. We argue that persistent failures for low-resource, Indigenous, and non-standard language varieties are not only technical errors, but also implicit linguistic policies that reproduce colonial language hierarchies. Drawing on linguistic capital, raciolinguistic ideology, language policy research, and decolonial computing, we show how data, metrics, and model priors determine whose voices become machine-legible. We introduce the Three Harms (3M) taxonomy---Misrecognition, Misalignment, and Mistrust---and a seven-layer situatedness model for linguistic diversity in ASR and ASR-mediated voice interfaces. We then propose a participatory framework and minimum audit protocol for culturally competent ASR, positioning affected communities as co-designers, evaluators, and governance partners.
arXiv:2608.05910v1 Announce Type: cross Abstract: Student mobility programs such as Erasmus+ enable students to take courses at other universities, broadening their academic and cultural horizons. However, this flexibility also leads to a practical challenge: ensuring that students do not take courses elsewhere that substantially overlap with courses in their home curriculum. In this work, we propose CourseGraph, a methodology that automates the evaluation of external courses based on insights obtained from the process followed by curriculum administrators when assessing courses for inclusion in a degree program. Course- Graph extracts information such as course titles, descriptions, and learning outcomes from the course webpage. Then, this information is represented semantically using a BERT-based language model, after which the pair-wise similarity between courses can be computed. This information is then used by a Random Forest classifier to determine whether a candidate course abro
arXiv:2608.05898v1 Announce Type: cross Abstract: As Generative AI coding tools reshape professional software development, universities have begun designing courses to prepare students for AI-assisted development workflows. By analyzing the syllabi of these courses, we can gather empirical evidence about these courses, reveal how this emerging curricular area is being defined, and gain guidance for future curriculum design. We analyzed 23 publicly available syllabi and course materials of upper-division, credit-bearing courses that meet specific criteria, including explicitly addressing Generative AI in software engineering. Through iterative qualitative coding, we characterized courses' learning objectives, assessments, topics, and documented AI tools. Our analysis reveals commonalities and differences among these courses that allow researchers and educators to study and develop future courses.
arXiv:2608.05889v1 Announce Type: cross Abstract: Large language models (LLMs) can leave small stylistic traces in text written with their help. The most discussed is the em-dash (U+2014), especially the unspaced form word---word, which is normal in typeset English prose but unusual in U.S. press writing, where AP style calls for spaced dashes. This study asks whether that trace is measurable in congressional press releases. In a preregistered design (OSF: 10.17605/OSF.IO/U5NEY), 146,239 scraper-sourced releases from 480 House and Senate offices (2021-2025, the open congress-press dataset) were analyzed: density of unspaced prose-form em-dashes per 1,000 characters of cleaned text, Poisson/negative-binomial models with a length offset, clustering by office. Density stayed within 0.10-0.12 per 1,000 characters through 2021-2024, then rose to 0.217 in 2025, more than twice the four-year baseline; the share of releases with such an em-dash rose from ~13% to 24.8%. The primary frequency ra
arXiv:2608.05576v1 Announce Type: cross Abstract: When a large language model (LLM) writes Harry Potter fanfiction, it reliably produces fundamental elements of the Hogwarts universe, such as recognizable places and characters. Human-written Harry Potter fanfictions, however, typically include these fundamentals and much more, incorporating stylistically irregular content and relationship-diverse plotlines. This gap between LLM and human writing has been noted across a variety of domains. LLMs tend to produce "average" writing, while human writing contains more diverse content that covers a broader distribution. Existing work has shown the existence of this distributional "gap", but no work has proposed a systematic way to measure it. Our paper proposes a human-grounded framework that uses the empirical distribution of human writing on a topic to measure the distributional breadth of LLM-generated content on that same topic. We propose two metrics, LLM Coverage (LLM-Cov) and In-Boundar
arXiv:2608.05224v1 Announce Type: cross Abstract: Large language models fine-tuned on human behavioural data have emerged as general-purpose cognitive proxies, but the scale this requires, and whether these models process task structure or exploit statistical shortcuts, remain open questions. We train fourteen models from 135M to 14B parameters across four architecture families on Psych-101, a dataset of 10.7 million trial-level choices from 160 experiments. In-distribution, scale barely matters. The models fall within a narrow band, as though against a ceiling, and 0.6B to 1B parameters suffice to match a 70B baseline on held-out participants. Out-of-distribution, that band opens into a markedly steeper scaling gradient, with larger models clearly advantaged in generalisation to novel task structure. To determine what information these models use, we run two diagnostics. We progressively strip four prompt channels -- task instructions, experimental stimuli, outcome feedback, and choic
arXiv:2608.05166v1 Announce Type: cross Abstract: We present an evaluation of cognitive bias expression in state-of-the-art instruction-tuned LLMs under realistic multi-turn interaction settings. Our work introduces a novel three-condition experimental framework that disentangles the effect of exposure to a biased user turn from the effect of the turn's semantic content, alongside a benchmark of 24,300 jury-validated user prompts spanning all 81 cells of a 9x9 target-human bias interaction matrix. Across eight frontier LLMs, we find that biased conversational context systematically increases bias expression relative to zero-shot baselines in 6 of 8 models. We identify two competing behavioral dynamics underlying this effect: conversational exposure to biased reasoning generally amplifies downstream bias tendencies, while explicitly stated bias cues often trigger alignment-related suppression behaviors that reduce overt bias expression. We release our framework, codebase, and dataset to
arXiv:2608.06364v1 Announce Type: new Abstract: The use of e-commerce mobile applications is expanding in Nigeria, creating both opportunities and risks, including fraud and reduced user control over digital technologies, raising concerns about digital sovereignty. This research examines how Artificial Intelligence (AI) in Nigerian mobile applications affects digital sovereignty, examined through platform transparency as a key indicator of user awareness and control. Using an interpretive approach, the research combines the forensic analysis of selected Android applications with contextual document analysis to identify AI features and evaluate disclosure practices. The findings show that AI is widely implemented in the applications, yet transparency about its use remains limited. A socio-economic analysis of Nigeria further shows an increasing dependence on consumer digital platforms, moderate AI awareness, and uneven patterns of interaction. By providing empirical evidence on AI trans
arXiv:2608.06322v1 Announce Type: new Abstract: Higher education remains largely reactive in its approach to student success. Institutions frequently identify academic problems only after students have failed courses, fallen behind in degree progression, accumulated excessive debt, or departed without a credential. Healthcare faced a similar challenge decades ago. It responded by shifting from reactive treatment to preventive care powered by predictive models, risk stratification, electronic health records, and artificial intelligence (AI). This paper argues that higher education stands at an analogous inflection point. Drawing on advances in learning analytics, educational data mining, machine learning, workforce analytics, and digital twin technologies, we propose a paradigm we call Precision Education. Under this framework, AI continuously analyzes academic, behavioral, financial, and career data to identify emerging risks, recommend personalized interventions, optimize educational
arXiv:2608.06166v1 Announce Type: new Abstract: The article reports on a blind Turing Test experiment, assessing the performance of out-of-the-box leading LLMs on three Italian legal professional exams: the Bar, Judges and Notary exams. Leading LLMs were asked to generate full written exam papers, which were made indistinguishable from human submissions and anonymously evaluated by expert examiners, using the same criteria applied in real examinations. Results reveal marked differences across both models and tasks. While some LLMs match or exceed top human performance in adversarial legal argumentation and doctrinal analysis, all models fail in the notary exam, which requires goal-directed legal planning under strict formal and substantive constraints. Beyond ranking models, the study identifies task-specific strengths, limitations and recurring legal failure patterns. Although limited to out-of-the-box systems, the findings provide qualitative evidence on the current scope and boundar
arXiv:2608.06106v1 Announce Type: new Abstract: This paper audits whether large-scale generative music systems exhibit measurable musical homogenization relative to human-produced music, and develops a justice-centered account of why this matters. We audit two commercially deployed systems (Suno and Lyria 3) across four genres (Afrobeats, K-pop, Dance Pop, and Heavy Metal). For each system and genre, we generate 100 tracks and compare them against human corpora of equal size, using 72 music information retrieval (MIR) features and multiple diagnostics of dispersion, redundancy, and separability. We define homogenization as reduced acoustic variation in standard computational audio features including rhythm and timing, timbre/spectral shape, and dynamics, both within genres and across genre boundaries. We also generate tracks using only a genre name as the prompt, with no additional instructions, to reveal each system's default musical tendencies. The results show two structurally disti
arXiv:2608.05800v1 Announce Type: new Abstract: Artificial intelligence (AI) systems increasingly mediate decisions affecting individuals and societies. Existing data protection frameworks address certain privacy-related harms, particularly those arising from data leakage, re-identification, and profiling. However, they inadequately capture a more fundamental risk: unreliable or unjustified inference produced by AI systems even when data collection and processing are legitimate. This article argues that modern AI raises distinct concerns of construct validity, confounding, representativeness, distribution shift, and fairness trade-offs that require specialised regulatory attention. In the context of AI, transparency and explainability acquire distinct and significantly more challenging meanings than in conventional software. A substantial body of work in critical data studies and the measurement-theoretic literature has diagnosed these epistemological limitations. This article's contri
arXiv:2608.05656v1 Announce Type: new Abstract: Safety risks of AI are becoming increasingly evident in human interactions with AI technologies. The prominent approaches to evaluating these risks favor technical methods, such as model benchmarks and LLM simulations, often sidelining empirical research with human subjects. To examine this apparent gap in the acceptance of human research, we conduct an expert survey (n=93) and expert interviews (n=17) with AI Safety & Ethics (AISE) researchers from Technical, Sociotechnical, Governance, and Normative backgrounds. Our findings suggest that although there is a consensus that human research is valuable for generating evidence for AISE, its adoption and acceptance are constrained by perceived validity issues, tangible resource barriers, epistemic and personal preferences in methods, and infrastructural constraints from the broader research community. In particular, Technical researchers tend to value human research less and collaborate acros
arXiv:2608.05583v1 Announce Type: new Abstract: As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential. Decisions about scarce medical resources often hinge on judgments of responsibility, particularly when patients' own actions contribute to illness. We investigate how LLMs reason about responsibility and its consequences, tracing their judgments across successive levels, from the behavior, to the resulting illness, to the denial of care. We evaluate a wide range of LLMs, spanning different model families and capability levels, on various clinical vignettes adapted from prior studies. Our results identify a judgment-consequence gap: LLMs largely agree with humans that patients bear responsibility for health-harming behaviors, yet overwhelmingly refuse to let that judgment influence how they allocate scarce resources. Specifically, LLMs default to random allocation, whereas humans consistently favor the less-c
arXiv:2608.05545v1 Announce Type: new Abstract: Generative AI used as a capable servant has greatly accelerated intellectual work, but it also risks eroding human epistemic agency by encouraging uncritical acceptance of AI-generated reasoning. This creates a need for mechanisms that preserve human agency by augmenting metacognition during AI-assisted intellectual work. To address this, we propose the Synthesis-Analysis Reciprocity Model, which views intellectual construction as a reciprocal interaction between Synthesis, which combines components into an artifact, and Analysis, which critically evaluates them against objective indicators and constrains subsequent synthesis. Grounded in this model, we present the Vibe Compiler, a research-logic compiler that helps researchers transform vague ideas (Vibes) into coherent research logic. The system compiles these ideas using a research paper ontology of sixteen academic parameters. Compilation failures indicate missing logical components;
arXiv:2608.05438v1 Announce Type: new Abstract: Generative AI has infiltrated every stage of the research lifecycle: how scholarship is conducted, written, published, and reviewed. Recent policy responses, such as ACM's authorship policy, address an immediate concern about responsible and transparent disclosure of AI use. We argue that a focus on authorship and disclosure, although necessary, risks obscuring and ballooning a set of entrenched problems and strains within publication systems. The central question is not about how papers and other research artifacts should incorporate AI, but how scientific communication itself should evolve when all relevant parties (authors, reviewers, readers) may rely on AI assistance. We draw on our experience within these and other roles to illustrate two contrasting but feasible visions of 2036 with four entwined questions, namely about the purpose of papers as artifacts, reviews, human reviewers, and the incentives that bind all of them. We argue
arXiv:2608.05427v1 Announce Type: new Abstract: School network reorganization is a strategic planning problem that requires balancing demographic trends, territorial accessibility, educational requirements, and institutional constraints while ensuring an efficient allocation of public resources. This paper proposes an optimization framework for school dimensioning decisions based on a novel Integer Linear Programming formulation integrating geographical, administrative, and educational criteria. A synthetic benchmark generator is introduced to evaluate the scalability and computational performance of the model on artificial instances, while a real-world case study involving the complete public school network of the Calabria region (Italy) is conducted using actual institutional, territorial, and demographic data. The proposed approach effectively identifies optimal aggregation plans under different policy scenarios while preserving the structural characteristics of the educational syst
arXiv:2608.05181v1 Announce Type: new Abstract: Building Information Modeling (BIM) has transformed workflows across the Architecture, Engineering, and Construction (AEC) industry, yet its relationship with employee job satisfaction remains insufficiently understood. This pilot study investigates whether BIM engagement or demographic characteristics better predict job satisfaction among AEC professionals. Survey responses from 104 participants were analyzed using Spearman rank correlations, logistic regression, and Classification and Regression Tree (CART) modeling. 27 items Job Satisfaction Index demonstrated excellent internal reliability. Across all analytical approaches, BIM engagement emerged as a stronger predictor of job satisfaction than demographic factors. Specifically, the proportion of project work completed using BIM was the only significant predictor of job satisfaction, whereas age, gender, education level, and professional experience showed no significant relationships.
arXiv:2608.05180v1 Announce Type: new Abstract: The integration of large language models into defense and national-security workflows raises urgent questions about whether frontier models exhibit stable, consistent, and policy-appropriate preferences in high-stakes contexts. We introduce the Nuclear Decision-Making Benchmark (NDM Bench), a targeted evaluation framework of 151 scenarios authored by PhD-credentialed scholars in international relations spanning four domains: escalation (76), arms control (25), non-proliferation (25), and proliferation (25). Scenarios are actor-agnostic, enabling multiple country pairs to be exchanged, and we introduce experimental phrasing variants to probe sensitivity to narrative framing. We apply the benchmark to seven frontier AI systems: DeepSeek-V3.2, ERNIE 4.5-300B, Gemini 3 Pro, GLM-4.6, GPT-5.2, Llama 4 Maverick-17B Instruct, and Qwen3-235B. We find significant overall inter-model variation in all four domains, with 91.7% of pairwise inter-model
arXiv:2608.05179v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used across the scientific research lifecycle: ideation, literature search, experiment design and execution, analysis, manuscript drafting, and review. End-to-end AI scientist systems can now produce paper-like manuscripts, but their claims are often harder to verify than their code is to run. This survey studies that gap in computational AI/ML research, where code, benchmarks, experiments, and write-ups are most visible. We screen 125 candidate works and include 35, with full-text coding of 26 entries: 24 runnable systems and two study or position papers. We code seven audit dimensions: lifecycle stage, autonomy level, evaluation method, released artifacts, human-in-the-loop points, novelty verification, and result-selection disclosure. The main pattern is that code release is now common, but reproducibility-grade and claim-verification artifacts remain much less common. In the 24 runnab
arXiv:2608.05178v1 Announce Type: new Abstract: Equitable access to scientific knowledge often depends on informal gatekeeping decisions, particularly when resources such as paywalled articles, datasets, or professional materials such as curriculum vitae (CV) must be shared selectively. We introduce a controlled simulation framework in which large language model (LLM)-based professors must grant access to only one requestor. Across prompts, requesters vary systematically by global region (Global North vs. Global South) and academic seniority (undergraduate student, PhD candidate, postdoctoral researcher, and tenured professor), while all other factors remain constant. Across varying evaluation scenarios, LLMs exhibit contrasting academic status biases, with some prioritizing PhD candidates, while others favor tenured professors. However, when global regions differ, a distinct divergence emerges based on model architecture: while many frontier LLMs systematically favor requesters from t
arXiv:2608.05177v1 Announce Type: new Abstract: Research indicates students require customized written composition feedback to enhance critical thinking and writing competence, yet teachers face barriers to delivering timely personalized guidance due to heavy workloads. To address this gap, this study developed the Writing Improvement and Smart Evaluation Agent (WISE Agent), an artificial intelligence (AI) feedback tool targeting textual logic and perspective biases in student essays. We conducted a three-month intervention with 260 Chinese sixth-grade students, each completing seven themed essays and receiving targeted WISE Agent feedback shortly after submission. Assessment used a critical thinking rubric adapted from the California Critical Thinking Disposition Inventory (CCTDI), covering seven core dimensions including cognitive maturity and open-mindedness. Results indicate structural optimizations in critical thinking dimensions rather than a uniform increase in total scores. Whi
arXiv:2608.05176v1 Announce Type: new Abstract: Music education has never been a static discipline. Each major technological shift has forced educators and institutions to reconsider what they teach, how they teach it, and why. We now stand at what may be the most consequential of such turning points. Three deeply intertwined transformations have been converging simultaneously: 1. The very nature of music has changed: how it is made, distributed, consumed, and valued; 2. The public for music has changed: listening habits are now shaped by streaming algorithms and the boundary between consumer and creator has blurred; 3. Music-making itself has changed: digital audio workstations (DAWs) have for two decades been reshaping compositional practice. In addition, generative AI has now irrupted, capable of producing complete, stylistically coherent musical pieces from a short text prompt. These changes are not independent of one another, and they all bear directly on musical education - both
arXiv:2608.05175v1 Announce Type: new Abstract: Large language models can complete most of the assignments in an introductory artificial intelligence course. This paper is an experience report on redesigning one such course, CSS~382 at the University of Washington Bothell, in response. Rather than freeze the curriculum, the redesign retained the course's classical core (search, adversarial search, Markov decision processes, reinforcement learning) and added a strand in which students build a large language model from scratch, so that a tool they are required to use is also one they are required to understand. Assessment was rebuilt around tasks that resist unattributed automation: in-class exercises, reflective writing, and a defended team project, with examinations removed entirely. The policy on AI was inverted, from unmentioned in 2023 to required in 2026. The center of the paper is a participatory ethics sequence in which a cohort of students deliberated on and endorsed a "Student
arXiv:2608.05174v1 Announce Type: new Abstract: Artist-led open-source libraries such as Processing or openFrameworks have had a major impact on artists and designers who use code as a creative medium. In this work, we conduct the first large-scale empirical study of public code repositories that use these libraries. Our study dives into 1,613,571 code repositories collected from the Software Heritage archive. Combining quantitative and qualitative methods, we investigate the diversity of practices and practitioners in terms of code hosting, geographical distribution, characteristics of code-based creative works and the purposes of these works. Key findings include evidence of the worldwide presence of generative art and creative coding, as well as the adoption of these practices in both education and across creative industries. We illustrate these findings with concrete examples of repositories and contributor profiles around the globe, spanning the spectrum from university curricula
arXiv:2608.05173v1 Announce Type: new Abstract: As AI capabilities advance, AI systems will pose greater risks to national security and potentially humanity as a whole. Governments may eventually conclude that these risks warrant restraining AI development. This motivates the question: will governments still be able to restrain AI development in the future, should they want to do so? In this paper we analyze which world events and changes to the state of AI development would make future governance more difficult or even effectively impossible. Our analysis surfaces likely pathways that would lead to these difficulties, including hardware proliferation, continued algorithmic progress, and the release of catastrophically dangerous AI models. Due to the field's lack of understanding of AI development, it may be difficult or impossible to know when we will hit a "point of no return", and we therefore recommend a conservative approach. The window may be closing, but governments currently ha
arXiv:2608.05172v1 Announce Type: new Abstract: The task-based framework in economics models occupations as bundles of tasks. It is the standard lens for understanding how technology affects work: a new technology changes the cost or time each task requires and these task-level effects aggregate to occupation-level effects. We study how tasks should be weighted in this aggregation. Prior work has relied on idiosyncratic or ill-justified choices for task weights. While recent work suggests weighting tasks by time spent, existing time shares are either based on coarse ONET data not intended for this purpose or estimated via black-box language models. We address this gap by proposing a principled method for estimating time shares for nearly 18,000 tasks that constitute nearly all U.S. jobs. Our estimates factor a task's time into (i) the expected frequency of the task, derived from ONET, and (ii) the time to complete a single instance of it. To estimate the latter, we solve a constraint s
arXiv:2608.05171v1 Announce Type: new Abstract: Generative AI (GAI) creates new opportunities for collaborative problem-solving (CPS), yet its role in shaping student interaction remains unclear. To address this gap, we conducted a six-week quasi-experimental study with 201 fifth-grade students in two conditions: with and without GAI. Chi-square analysis showed significant differences in CPS behavior distributions between groups. Compared with the control group, the GAI-supported group demonstrated more social behaviors, particularly engagement and conflict management, but less frequent cognitive behaviors such as task planning and solution reasoning. Lag sequential analysis further revealed distinct interaction patterns: while the control group followed a more conventional transition from listening to planning, the GAI group showed a robust pathway from task planning to conflict management to solution reasoning. Thematic analysis of AI interaction logs suggested that students used GAI
Across the country, schools are raising alarms about chronic absenteeism. News stories highlight rising numbers of missed days, legislators are demanding answers from districts, and educators are feeling the stress.
Section 504 of the Rehabilitation Act, which prohibits discrimination against students and other individuals with disabilities, is far less visible than the Individuals with Disabilities Education Act (IDEA) in school districts.
“The premise that cybersecurity is a back-office or administrative expense and that something might not happen — that needs to be changed,” says Fadi Fadhil, field CIO and director of field strategy at Palo Alto Networks. “CISOs and CIOs can steer that change by engaging in simplified conversations with university leadership. It’s a strategic effort, helping them understand how the investment reduces institutional risk.” When it comes to budgeting for their cybersecurity programs, higher education CISOs must overcome some unique hurdles, ranging from the federated nature of university IT…
Raising your hand in class and patiently waiting until you’re called before speaking. Sharing with classmates in a group project. Understanding what you’re feeling and how best to express it safely. These are a few examples of what social-emotional skills look like in the classroom. Social-emotional learning (SEL) houses a variety of skills, all of which have always been embedded in the K–12 experience. As recent research points more directly to the value of weaving these learning moments into the K–12 curriculum, educational technology has risen to meet the demands. The Evidence for Social-…
Article URL: https://twitter.com/gergelyorosz/status/2062861559009820976 Comments URL: https://news.ycombinator.com/item?id=48411421 Points: 10 # Comments: 1
On any given Tuesday afternoon, a dean at Morgan State University can pull live enrollment trend data without submitting a ticket, waiting for a report or following up with the IT department. At most higher education institutions, that same request can take about three weeks. The difference isn’t the data platform, however. It’s how the historically Black college is prioritizing data literacy. Timothy Summers, vice president of IT and CIO at the Baltimore-based institution, is betting the university’s artificial intelligence strategy on employees’ ability to effectively interpret, question…
The landscape for specialized colleges and universities such as art schools is shifting as higher education continues to evolve to fit emerging job markets and student interest. Founded in 1882, Cleveland Institute of Art continuously challenges itself to stay modern and relevant. Years ago, the school’s leadership had the vision to partner with the city to revitalize an area due for reinvigoration. The result was the Interactive Media Lab, which brings together the university, the city and private industry into a satellite campus that gives students and the community a space to…
As I wrapped up my student conferences, one conversation stuck with me. Steven had barely touched his final project for our computer science course, a virtual simulation of a piano, despite showing real promise earlier in the year.
Why boredom, quiet, and reflection matter for teen identity, agency, and imagination in a world shaped by constant screens. The post Running Your Own Race: Why Agency Begins in the Interior Life appeared first on Getting Smart .
Innovative Leader Award - Lauren Harwood of Dighton-Rehoboth Regional School District shares how she focuses her efforts on AI, CTE program, and cybersecurity
The recent ransomware incident involving Canvas has renewed attention on one of the most difficult decisions schools and technology providers can face: how to respond when sensitive student, faculty, or institutional data is stolen and threatened with public release. The post The Canvas ransomware attack shows why schools must focus on containment, not just recovery appeared first on eCampus News .