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:2606.27378v1 Announce Type: new Abstract: We introduce an axiomatic evaluation framework for latent thought representations in LLMs, comprising metrics that are independent of downstream benchmark scores and reveal representational failures that benchmark accuracy masks. Existing evaluations conflate representation quality with model capacity. Therefore, failures cannot be attributed to the representation rather than to the model that processes it. We formalize four functional axioms (Causality, Minimality, Separability, and Stability) and define a quantitative measure for each, computed directly on the representation independently of downstream accuracy. We audit open-weight LLMs across 23 reasoning tasks (e.g., Spatial Reasoning, Factual QA). We find that no candidate satisfies all four axioms simultaneously, that the representations distinguish task type reliably but cannot distinguish between two questions within the same task, and that the representations encode little infor
arXiv:2308.05201v4 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics. However, predicting their precise impact a priori is challenging, given AI's simultaneous effects on both demand and supply, as well as the strategic responses of market participants. Leveraging an extensive dataset from a leading online labor platform, we document a pronounced displacement effect and an overall contraction in submarkets where required skills closely align with core LLM functionalities. Although demand and supply both decline, the reduction in supply is comparatively smaller, thereby intensifying competition among freelancers. Notably, further analysis shows that this heightened competition is especially pronounced in programming-intensive submarkets. This pattern is attributed to skill-transition effects: by lo
arXiv:2605.04306v2 Announce Type: replace Abstract: Understanding high-dimensional data requires projecting it into lower-dimensional spaces, but any single projection inevitably loses information or introduces distortions. Tours address this limitation through animation of 2D projection sequences, yet existing tools present tradeoffs in the freedom and steerability of projection traversal, providing little to no ability to move between expert-guided paths and unrestrained exploration. We present dtour, a tour interface that combines static projection previews, reversible scrubbing along continuous geodesic projection paths, manual projection manipulation, and a wandering grand tour, all within a single progressive exploration interface. dtour scales to millions of points via GPU-accelerated rendering, runs in any modern browser, and integrates with both Python and JavaScript ecosystems. We demonstrate dtour on text, image, and single-cell data for two usage scenarios: gradually reveal
arXiv:2604.03401v4 Announce Type: replace Abstract: Understanding student engagement usually requires time-consuming manual observation or invasive recording that raises privacy concerns. We present a privacy-preserving pipeline that analyzes classroom videos to extract insights about student attention, without storing any identifiable footage. Our system runs on a single GPU, using OpenPose for skeletal extraction and Gaze-LLE for visual attention estimation. Original video frames are deleted immediately after pose extraction, thus only geometric coordinates (stored as JSON) are retained, ensuring compliance with FERPA. The extracted pose and gaze data is processed by QwQ-32B-Reasoning, which performs zero-shot analysis of student behavior across lecture segments. Instructors access results through a web dashboard featuring attention heatmaps and behavioral summaries. Our preliminary findings suggest that LLMs may show promise for multimodal behavior understanding, although they still
arXiv:2504.04703v2 Announce Type: replace Abstract: Artificial intelligence-augmented technology represents a considerable opportunity for improving healthcare delivery. Significant progress has been made to demonstrate the value of complex models to enhance clinicians` efficiency in decision-making. However, the clinical adoption of such models is scarce due to multifaceted implementation issues, with the explainability of AI models being among them. One of the substantially documented areas of concern is the unclear AI explainability that negatively influences clinicians` considerations for accepting the complex model. With a usability study engaging 20 U.S.-based clinicians and following the qualitative reflexive thematic analysis, this study develops and presents a concrete framework and an operational definition of explainability. The framework can inform the required customizations and feature developments in AI tools to support clinicians` preferences and enhance their acceptanc
arXiv:2501.10551v5 Announce Type: replace Abstract: As large language models (LLMs) advance and become widespread, students increasingly turn to systems like ChatGPT for assistance with writing tasks. Educators are concerned with students' usage of ChatGPT beyond cheating; using ChatGPT may reduce their critical engagement with writing, hindering students' learning processes. The negative or positive impact of using LLM-powered tools for writing will depend on how students use them; however, how students use ChatGPT remains largely unknown, resulting in a limited understanding of its impact on learning. To better understand how students use these tools, we conducted an online study $(n=70)$ where students were given an essay-writing task using a custom platform we developed to capture the queries they made to ChatGPT. To characterize their ChatGPT usage, we categorized each of the queries students made to ChatGPT. We then analyzed the relationship between ChatGPT usage and a variety of
arXiv:2405.13890v4 Announce Type: replace Abstract: This paper was a Workshop Paper. See the full paper which will be presented at CHI 2026: arXiv:2501.10551; As large language models (LLMs) become more powerful and ubiquitous, systems like ChatGPT are increasingly used by students to help them with writing tasks. To better understand how these tools are used, we investigate how students might use an LLM for essay writing, for example, to study the queries asked to ChatGPT and the responses that ChatGPT gives. To that end, we plan to conduct a user study that will record the user writing process and present them with the opportunity to use ChatGPT as an AI assistant. This study's findings will help us understand how these tools are used and how practitioners -- such as educators and essay readers -- should consider writing education and evaluation based on essay writing.
arXiv:2606.28083v1 Announce Type: cross Abstract: Micro-expression recognition is challenging due to subtle and short-lived facial muscle movements. Existing methods rely heavily on apex-onset frames, overlook fine-grained inter-frame dynamics, and separately model spatial and temporal information, limiting generalization across datasets. To address these challenges, we propose STAG, a dynamic ROI-AU-coupled spatial-temporal network that jointly models motion flow and adaptive facial connectivity. The framework extracts optical flow from discriminative frames using magnitude-based selection and temporal attention. A dual-branch architecture combines an enhanced graph attention network for structured spatial reasoning with a transformer encoder for temporal modeling. A bidirectional cross-attention module enables mutual refinement of spatial and temporal features, while AU-guided dynamic connectivity adapts facial region interactions according to muscle activation patterns. The transfor
arXiv:2606.27897v1 Announce Type: cross Abstract: We present a design rationale, embedding model, and interactive visual-analysis system for exploring large wristwatch collections through heterogeneous visual and semantic attributes. The system addresses a common limitation of catalog and e-commerce interfaces: users can filter by metadata, but they receive little support for open-ended exploration of visual similarity, stylistic alternatives, and mixed aesthetic-functional criteria. We therefore represent watches with separate attribute graphs for dial color and dial design, while using watch type as an explicit semantic organizer. Dials are segmented with a U-Net, watch types are predicted with a Vision Transformer, colors are represented through a shared CIELAB reference palette, and dial structure is described with a gradient-based image descriptor. We extend UMAP by combining attribute-specific neighborhood graphs in a unified probabilistic objective and by adding a class-aware la
arXiv:2606.27619v1 Announce Type: cross Abstract: Dyslexic learners increasingly use artificial intelligence (AI) tools to support reading, writing, organisation, and study-related tasks. However, their lived experiences with these tools remain largely underexamined. This paper proposes DysLexLens, a low-resource LLM framework, designed to analyse dyslexic learners experience with AI through online forum discussions. DysLexLens is designed as an end-to-end, evidence-traceable architecture which transforms noisy social media posts into a dictionary-driven corpora, provides knowledge-graph (KG)-based question reasoning, generates verifiable query responses, and enables response evaluation through quantitative and human-grounded assessment. DysLexLens has four key features. First, it employs a dictionary-driven filtering method to construct a more focused Reddit corpus on dyslexia and AI, filtering out noisy and weakly related posts to improve the relevance of data collected from low-reso
arXiv:2606.28241v1 Announce Type: new Abstract: Background: Conversational AI chatbots designed for mental health may offer an accessible, scalable avenue for supporting psychological well-being, yet prior evaluations have largely focused on clinical symptom reduction rather than broader indicators of day-to-day functioning, and have rarely monitored for potential harms such as inflated self-perception. Objective: We examined within-person change in psychological functioning indicators among real-world users of Ash, a purpose-built conversational AI for mental health support, over the first four weeks of use, and whether these changes were associated with engagement metrics. Methods: In this single-arm observational cohort study, new users (n = 1,284) completed in-app single-item measures of psychological functioning (life satisfaction, relationship satisfaction, sleep quality, behavioral activation), working alliance, and grandiosity (inflated self-perception), at baseline and Week 4.
arXiv:2606.28090v1 Announce Type: new Abstract: As Large Language Models (LLMs) become increasingly integrated into daily routines, understanding how users interact with these systems is crucial for effective human-AI collaboration. This work investigates keystroke dynamics as a behavioral measure of user mental effort and perceived output usefulness in human-LLM interaction. We conducted a user study (N = 36) to examine how task difficulty (easy vs. hard) and device type (desktop vs. mobile) influence typing behavior and workload (NASA-TLX) during interactions. Our results indicate that hard tasks led to significantly more keystrokes, slower typing, increased pauses, and higher self-reported workload. Device type had weaker effects, with mobile use slightly reducing input length and typing speed. While keystrokes captured differences in cognitive effort, they did not predict perceived LLM output usefulness. These findings highlight the potential of keystroke dynamics as real-time indi
arXiv:2606.28081v1 Announce Type: new Abstract: Spatialized document layouts are widely used for exploratory analysis of text corpora, but interpreting the spatial organization of documents and the relationships between regions remains challenging. Existing approaches primarily summarize document content or explain how layouts are generated, providing limited support for understanding spatial relationships within the layout itself. We present CAPE, a context-aware explanation framework that generates natural-language explanations grounded in both document semantics and layout-derived spatial context. CAPE identifies salient spatial patterns (e.g., clusters, subgroups, outliers, and bridging documents) and constructs multi-level contextual representations to guide LLM-based explanation generation. It supports both AI-guided overview and user-driven exploration, with explanations available at multiple levels of detail. We demonstrate CAPE on news and scholarly document layouts and evalua
arXiv:2606.27738v1 Announce Type: new Abstract: Text-to-3D generation lowers the barrier to 3D content creation, but text alone is a weak interface for specifying spatial intent: where parts should be placed, how they relate, and how an object should be organized in 3D. We present HandMade, a workflow that combines VR 3D sketching and language for open-domain 3D asset generation. HandMade treats coarse, part-labeled 3D sketches not as incomplete geometry to reconstruct directly, but as spatial prompts for existing generative models. It converts segmented VR strokes into multi-view part guidance and structured prompts, allowing users to specify object layout and part relationships through 3D sketching while using language for identity, material, style, and local details. A technical evaluation shows that HandMade better preserves user-authored spatial scaffolds than text-only and sketch-based baselines on 20 varied examples. A user study with eight participants characterizes how users m
arXiv:2606.18142v3 Announce Type: replace-cross Abstract: AI agents are moving from advisors to actors, booking travel, planning menus, and running procurement on behalf of users. Existing benchmarks for AI and animal welfare evaluate model text responses to question-answer prompts, leaving open whether the welfare reasoning surfaced in those responses transfers to agentic deployment where the model must take actions with tools. We introduce TAC (Travel Agent Compassion), the first agentic benchmark measuring whether AI agents avoid options involving animal exploitation when acting on behalf of users. TAC presents an AI agent with twelve hand-authored travel booking scenarios across six categories of animal exploitation, augmented to forty-eight samples to control for price, rating, and position confounds. We evaluate seven frontier models from four labs. Every model scores below the chance level of sixty-four percent, with the best performer (Claude Opus 4.7) at fifty-three percent. A
arXiv:2604.18193v3 Announce Type: replace-cross Abstract: With the increasing deployment of robots in public spaces, encounters between robots and incidentally copresent persons (InCoPs) are becoming more frequent. However, InCoPs remain largely underexplored in the literature, particularly from a cross-cultural perspective. Therefore, the present study investigates differences in InCoPs' existence acceptance (EA) of autonomous cleaning robots in public spaces among Japanese and German participants. Online survey results revealed that Germans showed significantly higher EA. Social Norms and Trust were the strongest positive EA predictors across cultures. More specifically, for Germans, EA was directly influenced by Usefulness, Interest and Anger, showing a functional-affective pattern where functional perceptions boost EA and anger suppresses it. For Japanese participants, Trust, Surprise and Fear were the direct associational factors, forming a trust-emotion pattern. These findings su
arXiv:2601.22201v2 Announce Type: replace-cross Abstract: Social media platforms face increasing scrutiny over the rapid spread of misinformation. In response, many have adopted community-based content moderation systems, including Community Notes (formerly Birdwatch) on X (formerly Twitter), Community Notes on Meta, and Footnotes on TikTok. However, research shows that the current design of these systems can allow political biases to influence both the development of notes and the rating processes, reducing their overall effectiveness. We hypothesise that enabling users to collaborate on writing notes, rather than relying solely on individually authored notes, can enhance the overall quality of their notes. To test this idea, we conducted an online experiment in which participants jointly authored notes on politically misleading posts. We find that collaboration improves the helpfulness of notes, although the average effect depends on the interactional context. In particular, the bene
arXiv:2511.03217v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information. At the same time, knowledge-graph-based fact-checkers deliver precise and interpretable evidence, yet suffer from limited coverage or latency. By integrating LLMs with knowledge graphs and real-time search agents, we introduce a hybrid fact-checking approach that leverages the individual strengths of each component. Our system comprises three autonomous steps: 1) a Knowledge Graph (KG) Retrieval for rapid one-hop lookups in DBpedia, 2) an LM-based classification guided by a task-specific labeling prompt, producing outputs with internal rule-based logic, and 3) a Web Search Agent invoked only when KG coverage is insufficient. Our pipeline achieves an F1 score of 0.93 on the FEVER benchmark on the Supported/Refuted split without task-specific fine-tuning. To address Not enough information cases, we conduct a
arXiv:2508.11059v2 Announce Type: replace-cross Abstract: This paper explores how Interactive Digital Narratives (IDNs) can support learners in developing the critical literacies needed to address complex societal challenges, so-called wicked problems, such as climate change, pandemics, and social inequality. While digital technologies offer broad access to narratives and data, they also contribute to misinformation and the oversimplification of interconnected issues. IDNs enable learners to navigate nonlinear, interactive stories, fostering deeper understanding and engagement. We introduce Systemic Learning IDNs: interactive narrative experiences explicitly designed to help learners explore and reflect on complex systems and interdependencies. To guide their creation and use, we propose the CLASS framework, a structured model that integrates systems thinking, design thinking, and storytelling. This transdisciplinary approach supports learners in developing curiosity, critical thinking
arXiv:2606.22689v2 Announce Type: replace Abstract: As AI-generated content (e.g., "slop") becomes more prevalent online, people are developing strategies to attempt to identify it (or, conversely, to gain confidence that something is not AI-generated). What strategies are people using, and how are they changing over time as generative AI models themselves change? In this work, we catalog and analyze 2 years and 8 months of the AI detection strategies discussed by users of two popular Reddit communities (r/isthisAI and r/RealOrAI) that use the wisdom of crowds to identify AI-generated media. Through a mixed-method analysis of 13,098 posts and 222,060 comments within these communities, we catalog and analyze the prevalence of 12 AI-detection strategies, including examining fine-grained physical details, recognizing trends in AI-created content, and the assumptions people make about what models are capable of producing. Furthermore, we find that these strategies and mental models shift o
arXiv:2604.24155v3 Announce Type: replace Abstract: The project of aligning machine behavior with human values raises a basic problem: whose moral expectations should guide AI decision-making? Much alignment research assumes that the appropriate benchmark is how humans themselves would act in a given situation. Studies of agent-type value forks challenge this assumption by showing that people do not always judge humans and AI systems identically.This paper extends that challenge by examining two further possibilities: first, that evaluations of AI behavior change when its human origins are made visible; and second, that people judge the humans who program AI systems differently from either the machines or the human actors they are compared against. An experiment with 1,002 U.S. adults measured moral judgments in a runaway mine train scenario, varying the subject of evaluation across four conditions: a repairman, a repair robot, a repair robot programmed by company engineers, and compan
arXiv:2601.14264v2 Announce Type: replace Abstract: Large language models (LLMs) act as digital twins for human respondents, yet their psychometric comparability remains uncertain. We propose a construct validity framework spanning construct representation and the nomothetic span, benchmarking models against human gold standards. Across studies, digital twins achieved high aggregate-level accuracy and profile correlations, but showed attenuated item-level correlations. In word association tests, LLM networks exhibited humanlike small-world structure and theory-consistent communities, yet diverged lexically and in local structure. In decision-making and contextualized tasks, they under-reproduced heuristic biases, demonstrating normative rationality, compressed variance, and limited temporal sensitivity. Feature-rich and trait relevant conditioning improved Big Five personality prediction and nomothetic-span alignment, but network invariance remained limited, with partial configural sol
arXiv:2606.28277v1 Announce Type: cross Abstract: Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving. However, this rapid acceleration is creating a systemic challenge: traditional human peer review cannot scale to match the influx of AI-assisted science. Ultimately, to resolve this tension, we must also deploy AI to accelerate the verification and review process itself. To frame the discussion around this transition, we propose a taxonomy consisting of four progressive levels of AI-human collaboration in scientific evaluation, and discuss various trade-offs involved with each. As a step toward this future, we introduce the Paper Assistant Tool (PAT), an agentic AI framework built for deep scientific review and verification. PAT ingests full scientific manuscripts and produces a comprehensive evaluation, checking theoretical results, validating experiments, suggesting improvements,
arXiv:2606.28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction. Existing methods often depend on costly human calibration or item-level textual representations, providing limited evidence about the cognitive processes that make items difficult. We argue that difficulty should be viewed not only as a property of item text, but also as an observable consequence of the problem-solving burden an item induces. Large Reasoning Models (LRMs) offer scalable process evidence through reasoning traces, but such evidence must be structured to support interpretable modeling. To this end, we introduce Epi2Diff (Episode to Difficulty), a framework that maps LRM reasoning traces into cognitively grounded episode sequences. These episodes group trace segments into functional problem-solving states, enabling difficulty to be modeled through reasoning scale, effort allocatio
arXiv:2606.27951v1 Announce Type: new Abstract: AI agents are promising tools that can act as flexible behavioral nudges to enhance human cooperation in addressing large-scale societal problems. However, evidence on whether AI agents can effectively boost cooperation remains mixed. We recruited 1,283 participants to play iterated Collective Risk Games in small groups, testing whether AI assistants could nudge participants toward cooperation. By using persuasive framing personalized to each player's Social Value Orientation profile, the AI interventions significantly increased contributions and group success rates. These cooperative effects were short-lived, however, fading after the first few rounds. Strikingly, when the AI treatments were reconfigured to promote selfish behavior through exculpatory framing, the negative effects on contributions and group success were larger and substantially more persistent, particularly for personalized interventions. This asymmetry between prosocial
arXiv:2606.27689v1 Announce Type: new Abstract: AI-driven deception mechanisms are increasingly prevalent in digital games, yet the direction and magnitude of their effects on player experience remain contested. Existing research has not sufficiently disentangled designer-intended deception intensity from players' actual perception of deception, and most prior work relies on low-ecological-validity experiments or cross-sectional surveys. The present study aims to independently examine the causal effects of design deception intensity (DDI) and player deception awareness (PDA) on player ratings within a naturalistic gaming environment, and to investigate the moderating role of player experience. Leveraging the 54 version updates of Baldur's Gate 3 between 2019 and 2025 as a quasi-natural experiment, it collected all English-language Steam reviews posted within 1 to 28 days following each update, and constructed a player-version two-way fixed effects panel dataset. DDI was coded by human
There is a squeaky old merry-go-round in my neighborhood that my own children play on from time to time. Years of kids riding on it have loosened its joints so it spins more freely and quickly.
You’ll often hear two words come up in advising sessions as students look ahead to college: match and fit. They sound interchangeable, but they’re not.
Article URL: https://getmeadow.com/education/how-is-marijuana-medicine Comments URL: https://news.ycombinator.com/item?id=9957232 Points: 63 # Comments: 29
My first few years teaching math were a struggle for me and my students. Our textbook focused primarily on direct instruction: I do, then you do, but rarely we do.
Educator and author Carl Hooker says AI interest from educators has passed peak levels.
These lessons and activities, from exploring key documents of freedom to moments of the Revolution, can help students understand the American story.
As a former admissions officer and now an independent education consultant, I’ve read thousands of college essays. The ones that earn students admission to their dream schools aren’t necessarily the most polished.
The first wave of studies raises questions about other digital distractions and cellphones at home.
The MacBook Neo may narrow a pricing gap, but it also exposes a management gap. A lower-cost Mac may be enough to spark fresh interest. However, it alone isn’t enough to guarantee a smooth rollout.
New media center at North Dade Middle School marks milestone in initiative revitalizing learning environments to benefit the entire learning ... Read more
When middle school students make the leap to high school, they are expected to have a career path in mind so their classes and goals align with their future plans.
Hey HN, We've built Assistiv (www.ftfplatforms.com/assistiv), an AI-native learning platform designed to simplify how instruction is created, personalized, and delivered. It started with one goal: make powerful, assistive intelligence education tools available to everyone—without the bloat of enterprise LMS systems. What emerged is a fast, clean LMS with built-in AI that actually helps teachers teach. What’s live today: AI Flashcards – auto-generated from course content Self-generating quizzes – students can test themselves based on what they’ve learned Generative assessments for instructors – create full quizzes, aligned to objectives Course builder with AI assistance – create entire courses in minutes Smart grading tools – assisted manual grading and AI scoring suggestions Real-time reports for both instructors and org admins SAML, permission-based roles, microservice grading infrastructure What’s coming: TutorMe – AI-powered personal tutors trained on what you are learning, tuned to
Article URL: https://www.reuters.com/legal/googles-ai-previews-erode-internet-edtech-company-says-lawsuit-2025-02-24/ Comments URL: https://news.ycombinator.com/item?id=43165803 Points: 5 # Comments: 1
While prevention remains essential, 2025 has reinforced a hard lesson for district leaders: it’s not a question of if a cyber incident will occur, but how prepared a school system is to respond and recover when an attack happens.
I've been working on an edtech project that uses LLMs, curious how others are approaching compliance w/ FERPA, COPPA, etc. I've been using Lakera but as I get closer to some sales meetings I wanted to know if anyone has run into challenges with audit logs, consent tracking, or explaining AI behaviour to school districts/legal teams. Did you need to build anything custom? Any compliance docs? Curious whats overkill and whats needed. Comments URL: https://news.ycombinator.com/item?id=44351618 Points: 2 # Comments: 0
Who among us has never copied a homework answer in a hurry? Borrowed a friend’s paragraph? Accepted a parent’s “small correction” that eventually became a full rewrite?
The flagship plans to adjust contracts and restrict hiring as it grapples with rising costs, declines in federal research funding and other challenges.
Schools across the country are focusing keenly on two key priorities: teaching children to read and bringing down high chronic absenteeism rates that undermine learning. Both these goals could be scuttled by an alarming increase in the number of young children who lack access to healthcare. Our new analysis shows that nearly 1.2 million children […]
The unanimous decision came late Monday after the chair of the state university system board delayed a vote that could install Bell permanently.
The center's complaint alleges the teachers union didn’t specify Jews as the primary victims of the Holocaust, among other things. NEA has said it "does not tolerate antisemitism in any form."
Article URL: https://www.theregister.com/offbeat/2026/06/22/small-island-nation-tries-bold-tech-education-strategy/5258986 Comments URL: https://news.ycombinator.com/item?id=48631644 Points: 6 # Comments: 0
Superintendent Alberto Carvalho has resigned as leader of the Los Angeles Unified School District, four months after the FBI searched his home and office. A district spokesperson confirmed a letter of resignation from Carvalho on Sunday night. The reason for the timing wasn’t immediately clear. “The Board remains steadfast in its commitment to ensuring stability, […]
The success of today’s modern classrooms relies on a combination of resources, technologies and policies to maximize learning for students. From the funding that brings technology to schools to the rules and regulations that govern how it is used, these factors work cohesively to ensure an optimal experience for teachers and students alike. At this year’s ISTELive conference, held June 28 to July 1 in Orlando, Fla., expert speakers will present on a range of topics that address the future of modern classrooms. K–12 instructional staff, technology leaders, superintendents and librarians…
CoSN covers the policies and research currently driving the conversations around screen time in schools and offers a review of emerging legislation. The post This edtech podcast examines screen time in K12 appeared first on District Administration .