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Introducing the Third Generation of Apple’s Foundation Models

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Our next generation of Apple Intelligence is centered around our users, integrated deeply into our operating systems, and powered by a bold…

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Writing stRategies for Instructional Technology in Education (WRITE) transforms how educators support struggling writers through research-backed interventions and innovative technology solutions. Born from the University of Kansas with funding from the U.S. Department of Education's Office of Special Education Programs, WRITE bridges the gap between educational research and classroom practice.

Our Mission

We believe every student deserves the tools and support to express their ideas effectively through writing. WRITE provides educators with evidence-based strategies, technology recommendations, and practical resources to help all students-especially those who struggle with writing-develop essential writing skills that unlock academic success.

What Makes WRITE Different

Research-Driven Solutions: Our interventions are grounded in rigorous academic research and field-tested in real classrooms with measurable results.

Technology Integration: We don't just recommend tools-we help educators understand which technologies truly enhance writing instruction and how to implement them effectively.

Comprehensive Support: From brainstorming and organization to fluency and conventions, we address the full spectrum of writing challenges students face.

Inclusive Design: Our resources are specifically designed to support students with disabilities while benefiting all learners in diverse classroom settings.

Proven Impact

WRITE has demonstrated significant success in helping teachers:

Save time through automated assessment tools that provide instant feedback

Make data-driven instructional decisions with detailed progress monitoring

Implement targeted interventions based on individual student needs

Support the six key traits of writing: ideas, organization, voice, word choice, sentence fluency, and conventions

Our auto-scoring system generates graphs and trendlines that transform one-on-one writing conferences, helping teachers and students discuss strengths, identify barriers, and plan next steps for improvement.

Who We Serve

Educators seeking effective ways to teach writing and integrate technology meaningfully into their instruction

Students who need additional support to develop writing confidence and skills, including those with learning disabilities

Schools and Districts looking for scalable, evidence-based writing intervention programs

Researchers and Professional Developers committed to advancing writing education through proven strategies

Our Commitment

Writing is more than spelling and grammar-it's about helping students find their voice, organize their thoughts, and communicate with confidence. WRITE serves as a springboard for meaningful conversations about individual strengths, learning barriers, and the targeted support each student needs to become a stronger writer.

Whether you're a classroom teacher, special educator, administrator, or researcher, WRITE provides the tools, strategies, and ongoing support to help every student succeed in writing and beyond.

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Writing stRategies for Instructional Technology in Education (WRITE) transforms how educators support struggling writers through research-backed interventions and innovative technology solutions. Born from the University of Kansas with funding from the U.S. Department of Education’s Office of Special Education Programs, WRITE bridges the gap between educational research and classroom practice.

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Haley Wahl | Technical Communications Group

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Study finds that explainable AI often isn’t tested on humans

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Lincoln Laboratory’s comprehensive literature study reveals that most explainable AI systems lack human validation, raising concerns about their claims of interpretability.

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Te Hiku Media is a broadcasting and technology hub that has worked for over 30 years to nurture and revitalize te reo, the Māori language. While Silicon Valley's approach to AI mirrors colonial practices of assimilation and gatekeeping access, leaving out entire languages and peoples, Te Hiku Media had another vision. Working with the Māori community, they built a sovereign speech recognition model for te reo, while ensuring the flow of data wouldn't be used without the community's consent. They've also developed a digital language platform, Papa Reo, to expand their work to other Pacific languages. As Te Hiku Media's leaders Peter-Lucas Jones and Keoni Mahelona say, "Data is the last frontier of colonization."

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A living directory of ways communities resist AI harms-and build alternatives worth fighting for.

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Inside Higher Ed | Higher Education News, Events and Jobs

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Institutions including the University of Virginia and the State University of New York are launching AI initiatives to prepare students for a changing early-career job market.

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Helping a Loved One With a Speech or Language Disorder Communicate More Effectively Using Technology

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Learn how to support a loved one with a speech or language disorder using technology. Discover practical tips on using communication devices and apps to stay connected.

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A Framework for institutional change in the age of AI

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Generative AI is rapidly reshaping STEM higher education. Not only are our educational practices changing, but how we think about educational transformation must adapt. Existing models of institutional change in STEM, aimed at interactive engagement, have largely followed an adoption logic: relatively stable, well-researched educational practices are evaluated and then scaled. These assumptions do not hold for generative AI, which is an arrival technology -- entering classrooms before a sufficient pedagogical evidence base could form. Building on recent decades of work on STEM institutional change, we propose a framework identifying six dimensions along which prior change models must be reconsidered in light of AI: three concerning the tools at the center of reform (the tool’s evidence base, rate of change, and scope), and three concerning the people involved in change (faculty, change agents, and students). For each dimension, we examine how AI-era assumptions differ from those underlying prior interactive engagement reforms and derive design implications, including: privileging humble and local inquiries; organizing reform around pedagogical approaches rather than specific tools; repositioning change agents as facilitators of collective inquiry; and engaging students as partners in reform. Collectively, the six dimensions and design implications constitute a new framework for adapting change models to support institutions under conditions of genuine uncertainty. Finally, we illustrate how the framework may be applied through a brief case-study of a faculty workshop series carried out in a university physics department to support instructors adapting to this modern AI era.

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EASI and MySLP | Delivers evidence-based speech assessments in minutes.

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EASI generates speech & language reports with goals & recommendations in MINUTES! Uses purpose-built technology to analyze real-world speech samples during natural conversations.

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AI gives more praise, less criticism to Black students

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A Stanford experiment found that AI feedback shifts on identical essays depending upon the identity and characteristics of the student.

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How Do Clinicians Remain Relevant in the Age of AI? | Plural Publishing

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Throughout human evolution, few events have proven to be so transformative that they changed our very existence. Taming fire brought warmth in the cold and lit up the night. The invention of the wheel allowed us to transport things virtually unmovable before.

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AI Decision Field Guide — EdTech Recharge

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AI decisions are being made right now in procurement meetings, grant proposals, and product roadmaps by teams that have not yet had the conversation this guide is designed to prompt.

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Chief | Women Leaders Defining the Human‑Agentic AI Workforce | 2026

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New research from Chief & The Harris Poll reveals how senior women leaders are shaping the human‑AI workforce with strategy, governance, and intentional adoption.

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The State of AI in the Enterprise - 2026 AI report

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Explore the Deloitte AI Institute’s State of AI in the Enterprise report tracking AI investments, adoption, impacts on business, and challenges throughout 2025.

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Marked Pedagogies: Examining Linguistic Biases in Personalized

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Record your pitch and get instant feedback from Dunky.

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Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models

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Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data.

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Boston Institutes AI Literacy For All High Schoolers

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Boston launches universal AI literacy program, preparing students for workforce disruption and future economic opportunities.

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Google confirms AI headline rewrites test in Search results

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Publishers face new risk as Google tests AI-written titles in Search, rewriting tone and intent to better match queries and boost engagement.

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How can AI support learning? How are we prioritizing student safety? See the NYCPS guidance on AI.

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"Research study after research study has looked at model training processes and noted that most major LLMs are fed a mix of biased training data, biased annotation practices, flawed taxonomy design."

- Annie Brown, AI researcher and founder of the AI infrastructure company Reliabl - TechCrunch, November 2025.

LLMs (AI Large Language Models) have become part of everyday life. Systems such as ChatGPT, Claude, Gemini, Meta AI (Llama) and X.ai's Grok handle billions of interactions daily. They increasingly shape what information people encounter and in what order, subtly deciding what's important and even what is true, sometimes without users realizing it. Because LLMs wield growing power over information exposure, it is vital to recognize the political and ideological structures at multiple stages of their design, and to identify manipulation risks.

This report examines how an ideological skew can enter AI systems both "accidentally", for example through omission during training, and "on purpose", through deliberate attempts to skew training data or tuning processes. We close with recommendations for minimizing ideological lean as dependence on these technologies accelerates.

You can download the full report and press release for this impactful research below.

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AI Large Language Model Training: The Potential Risks of Ideological Skewing - PSG Consulting

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"Research study after research study has looked at model training processes and noted that most major LLMs are fed a mix of biased training data, biased annotation practices, flawed taxonomy design." - Annie Brown, AI researcher and founder of the AI infrastructure company Reliabl - TechCrunch, November 2025.

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Reclaiming our Humanity in the Age of AI

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Is AI Our Ally in Early Childhood Education? Depends on Who You Ask

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Ethical conversations: what are the implications of AI for AAC development, use, and implementation?

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Published in Journal of Clinical Practice in Speech-Language Pathology (Vol. 26, No. 1, 2024)

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Accessible Letters Tool (ALT) — Accessible Phonics Word Building

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Build words with color-coded phonics tiles. Fully accessible with keyboard, switch scanning, eye gaze, and screen reader support. By Kate Ahern, M.S.Ed. from AAC Voices, LLC.

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AI has a hidden water cost − here’s how to calculate yours

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AI systems’ water usage can vary widely, depending on where and when the computer answering the query is running.

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Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models

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We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framework of Perturbation Sensitivity Analysis to examine conversations related to PWD on social media platforms, specifically Twitter and Reddit, in order to gain insight into how disability bias is disseminated in real-world social settings. We then create the \textit{Bias Identification Test in Sentiment} (BITS) corpus to quantify explicit disability bias in any sentiment analysis and toxicity detection models. Our study utilizes BITS to uncover significant biases in four open AIaaS (AI as a Service) sentiment analysis tools, namely TextBlob, VADER, Google Cloud Natural Language API, DistilBERT and two toxicity detection models, namely two versions of Toxic-BERT. Our findings indicate that all of these models exhibit statistically significant explicit bias against PWD.

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A new app alerts you if someone nearby is wearing smart glasses | TechCrunch

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A hobbyist developer’s new app, which can detect nearby smart glasses, comes amid resistance to always-on recording and listening devices that invade people’s privacy.

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How AI-powered features on Samsung Interactive Display WAF Series enhance learning potential - Samsung Business Insights

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Technology in the classroom is here to stay. According to an eSchool News report, 92% of K-12 schools in the U.S. use interactive displays

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VERA-MH: Reliability and Validity of an Open-Source AI Safety Evaluation in Mental Health

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Millions now use generative AI chatbots for psychological support. Despite the promise related to availability and scale, the single most pressing question in AI for mental health is whether these tools are safe. The Validation of Ethical and Responsible AI in Mental Health (VERA-MH) evaluation was recently proposed to meet the urgent need for an evidence-based, automated safety benchmark. This study aimed to examine the clinical validity and reliability of VERA-MH for evaluating AI safety in suicide risk detection and response. We first simulated a large set of conversations between large language model (LLM)-based users (user-agents) and general-purpose AI chatbots. Licensed mental health clinicians used a rubric (scoring guide) to independently rate the simulated conversations for safe and unsafe chatbot behaviors, as well as user-agent realism. An LLM-based judge used the same scoring rubric to evaluate the same set of simulated conversations. We then examined rating alignment (a) among individual clinicians and (b) between clinician consensus and the LLM judge, and (c) summarized clinicians’ ratings of user-agent realism. Individual clinicians were generally consistent with one another in their safety ratings (chance-corrected inter-rater reliability [IRR] = 0.77), establishing a gold-standard clinical reference. The LLM judge was strongly aligned with this clinical consensus overall (IRR = 0.81) and within key conditions. Together, findings from this human evaluation study support the validity and reliability of VERA-MH: an open-source, automated AI safety evaluation for mental health. Future research will examine the generalizability and robustness of VERA-MH and expand the framework to target additional key areas of AI safety in mental health.

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Teaching AI Ethics — Exploring the ethics of artificial intelligence in education.

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Lisa A. Juckett, Monica L. Robinson, Lauren R. Wengerd; Narrowing the Gap: An Implementation Science Research Agenda for the Occupational Therapy Profession. Am J Occup Ther September/October 2019, Vol. 73(5), 7305347010p1-7305347010p6. doi: https://doi.org/10.5014/ajot.2019.033902

Download citation file:

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Narrowing the Gap: An Implementation Science Research Agenda for the Occupational Therapy Profession

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Abstract. Despite advancements in occupational therapy research, the widespread research-to-practice gap continues to delay how quickly evidence-based practices are implemented in real-world clinical settings. Implementing research in practice is a complex process that mandates attention from all occupational therapy stakeholders; however, researchers are uniquely positioned to help minimize the 17-yr lag between scientific discovery and the implementation of research findings into practice.

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The AI Impact Summit is coming up. We set out to learn the truth about AI’s impact.

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Harnessing AI to Enhance Reading Comprehension & Learning Outcomes for Students with Disabilities

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What The Company Learned About Social Media and Harms to Mental Health from Dozens of Internal Studies

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A new direction for students in an AI world: Prosper, prepare, protect | Brookings

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This report explores the potential risks generative AI poses to students and outlines what we can do now to minimize them.

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As artificial intelligence (AI) technologies increasingly shape our society, workforce, and personal lives, the need for comprehensive AI literacy is more important than ever. AI literacy goes beyond understanding AI tools; it equips individuals to critically evaluate, ethically navigate, and practically apply AI in real-world scenarios.

This essay provides a guide to AI literacy that addresses technical, ethical, critical, and societal dimensions of AI, preparing learners to thrive in an AI-embedded world.

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AI Literacy: A Guide for Academic Libraries | Lo | College & Research Libraries News

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AI Literacy: A Guide for Academic Libraries

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- Error rates dropped 12%–30%, depending on the dataset

- Traditional finetuning proved highly effective

- More accurate transcription opens new possibilities for formative assessment, feedback loops, and learning research

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Finetuning ASR Models for Child Voices - The Learning Agency

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Automatic speech recognition systems perform poorly with child voices, hindering their ability to address challenges in assessment and instruction.

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Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants’ Question-Answering in Asynchronous Learning Environments

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Li Siyan, Zhen Xu, Vethavikashini Chithrra Raghuram, Xuanming Zhang, Renzhe Yu, Zhou Yu. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.

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Artificial Intelligence and Universal Design for Learning: Transforming Teaching and Learning in Adult and Continuing Higher Education

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Apple is embarking on a new partnership with Common Sense Media, the leading source of digital literacy and well-being guidance and curriculum for families and educators, to support the development of free resources to e

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Ethical AI: The Skills and Knowledge Equity Leaders Need

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The equity imperative surrounding artificial intelligence (AI) in education is both urgent and complex. This brief provides an overview of the essential considerations for educational leaders approaching AI integration with equity at the forefront.

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Aqua Voice - Fast and Accurate Voice Dictation for Mac and Windows

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Fast, accurate, and private speech-to-text. Use voice to write clean & natural text, contextually adjusted to every app. Powered by Avalon, the world’s most advanced transcription model.

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Ten Simple Rules for AI-Assisted Coding in Science

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While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific validity. In this paper, we provide ten practical rules for AI-assisted coding that balance leveraging capabilities of AI with maintaining scientific and methodological rigor. We address how AI can be leveraged strategically throughout the development cycle with four key themes: problem preparation and understanding, managing context and interaction, testing and validation, and code quality assurance and iterative improvement. These principles serve to emphasize maintaining human agency in coding decisions, establishing robust validation procedures, and preserving the domain expertise essential for methodologically sound research. These rules are intended to help researchers harness AI’s transformative potential for faster software development while ensuring that their code meets the standards of reliability, reproducibility, and scientific validity that research integrity demands.

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A practical playbook for teams that want clarity, alignment, and real progress, not just hype.

Over the past year, I've had countless conversations with teams eager to "do something with AI." Most of them start in the same place: excitement, curiosity, and a bit of pressure to not fall behind. What separates the teams that make real progress from those that stall isn't the size of their model or budget. It's clarity, alignment, and intent.

Here's the playbook I've seen work in practice, built from trial, error, and a few late nights untangling half-finished pilots.

1. Start With Skills and Governance

Before you chase new tools, take stock of what you already have. Most companies underestimate how many AI-adjacent skills already exist: solid software engineers, architects, and product managers who deeply understand customer pain points.

Then ask what's missing. You'll probably need people who understand machine learning, NLP, or statistics, and someone who can translate between the technical and business sides. That bridge is often the difference between a model that sits in a notebook and one that drives real decisions.

When you find gaps, decide whether to train, hire, or partner. And please, set up a small governance group early. It doesn't have to be complicated, just cross-functional: a data scientist, an engineer, a legal or compliance person, and a business lead. Their job is to make sure you're not just building what's possible but what's right.

2. Know What AI Can and Can't Do

AI is great at spotting patterns and automating repetitive work. It's not magic, and it's definitely not a replacement for judgment.

Supervised learning helps when you have labeled data. Unsupervised works when you're exploring patterns. Reinforcement learning shines when systems can learn through feedback loops such as robotics or recommendation systems.

But AI also has hard limits. It doesn't reason causally, it doesn't understand context, and it can't improvise like a human. I've seen projects fail because teams expected the model to understand instead of predict. Knowing that difference saves time, money, and frustration.

3. Anchor on Clear, Measurable Goals

AI for AI's sake rarely sticks. Start with a business problem, not a tech showcase. I've used simple SMART goals to stay focused. For example:

Cut customer support response time by 30% in six months

Improve satisfaction scores by 15%

Reduce defect detection time by half in manufacturing

When goals are concrete, everyone knows what success looks like. And remember, fairness, transparency, and accountability aren't optional. They're what make your work trustworthy and sustainable.

4. Choose Partners Like You'd Choose Teammates

AI isn't a solo effort. The right partners can accelerate your progress, while the wrong ones can waste six months and six figures before you realize it. Look for experience, technical depth, and cultural fit. Choose partners who challenge your thinking rather than agree with everything.

Always ask for case studies, live demos, and references. I've learned more from a 20-minute prototype demo than from hours of slide decks. Once you've picked your partners, measure progress the same way you would for internal work: by outcomes, not promises.

5. Clean Data Beats Clever Models

Bad data quietly kills more AI projects than bad algorithms ever will. Spend time cleaning, validating, and structuring your data before you train a single model. It's not glamorous work, but it's the foundation of everything else.

Use what you already have: cloud infrastructure, databases, and analytics tools. Prebuilt AI services like Azure Cognitive Services or AWS Comprehend can help you move faster while you build internal expertise.

6. Pick the Right Use Cases

Every team wants a chatbot. Few actually need one. Start instead with a pain point you can clearly quantify, something with solid data and a measurable payoff.

A few examples that have delivered real value:

Conversational AI that reduces call-center volume

Image recognition for logistics and inventory accuracy

Automated document extraction to speed up claims or invoicing

High-impact, high-feasibility projects earn early wins that build momentum and credibility.

7. Build a Roadmap Everyone Understands

A roadmap isn't a Gantt chart; it's a story. It explains where you are, where you're heading, and how you'll know you've arrived. Include milestones, risks, dependencies, and owners. Then share it widely. Transparency builds trust, and trust earns buy-in when priorities shift.

8. Start Small and Learn Fast

Some of the best AI programs I've seen started as tiny pilots. You don't need a 12-month plan. You need a 4-week experiment that teaches you something.

Test the model, measure results, and collect user feedback early. Tools like Application Insights or Google Analytics make it easy to track impact. Iterate fast. Ship small. Learn even faster.

9. Measure What Matters

The real success metrics aren't precision or recall. They're adoption, efficiency, and satisfaction. Track whether your AI solution is being used and whether it's making life easier for the people who matter.

Celebrate wins, but don't ignore misses. Kill low-impact features early. Every "no" creates space for the work that truly moves the needle.

10. If You're Just Getting Started

You don't need a 50-page strategy deck to begin. Here's a simple five-step starter checklist:

✓ Review your team's current skills and flag AI-specific gaps.

✓ Identify who should be in your AI governance group. Mix technical, legal, and business voices.

✓ Shortlist a few vendors and ask for a quick demo.

✓ Audit your data for quality and diversity.

✓ Define one pilot use case with clear success metrics and a feedback loop.

Final Thoughts

AI strategy isn't about technology. It's about leadership. It's about building systems that make humans better, faster, and more insightful.

Start small, stay grounded, and keep learning. If you do that, you won't just "do AI." You'll own it.

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Nirmal Jingar, Senior Engineering Manager, Wayfair

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Building an AI Strategy That Actually Works - Mass Tech Leadership Council

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A practical playbook for teams that want clarity, alignment, and real progress, not just hype. Over the past year, I've had countless conversations

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Benchmarking Automatic Speech Recognition Technology for Natural Language Samples of Children With and Without Developmental Delays

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Natural language sampling (NLS) offers rich insights into real-world speech and language usage across diverse groups; yet, human transcription is time-consuming and costly. Automatic speech recognition (ASR) technology has the potential to revolutionize NLS research. However, its performance in clinical-research settings with young children and those with developmental delays remains unknown. This study evaluates the OpenAI Whisper ASR model on n=34 NLS sessions of toddlers with and without language delays. Manual comparison of ASR to human transcriptions of children with Down Syndrome (DS; n=19; 2-5 years old) and typically-developing children (TD; n=15; 2-3 years old) revealed ASR accurately captured 50% of words spoken by TD children but only 14% for those with DS. About 20% of words were missed in both groups, and 21% (TD) and 6% (DS) of words were replaced. ASR also struggled with developmentally informative sounds, such as non-speech vocalizations, missing almost 50% in the DS data and misinterpreting most of the rest. While ASR shows potential in reducing transcription time, its limitations underscore the need for human-in-the-loop clinical machine learning systems, especially for underrepresented groups.

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Manager Support Drives Employee AI Adoption

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Despite rising investment, few firms see AI ROI. The missing link: managers who encourage the adoption of AI by guiding employees and workflows.

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Generative Artificial Intelligence (AI) for Clinicians in Audiology and Speech-Language Pathology

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ASHA, in collaboration with its members and certificate holders, will continue to educate audiologists and speech-language pathologists (SLPs) about the uses of generative AI through articles, research symposia, and professional development resources.

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Ep06 - Algorithms Aren’t Neutral: Safiya Noble on AI, Bias, and Building Public‑Interest Technology

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Podcast Episode · My Robot Teacher · 10/23/2025 · 56m

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How do you know if AI is any good?: Evaluation Opportunities, Limitations, and Solutions - The Learning Agency

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As AI continues to evolve and become more accessible, one question persists: how do we know if AI is actually good, especially in education?

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Generative UI Meets Better Readability: Beyond AI-Assisted Design

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Generative UI offers a compelling new frontier: interfaces might dynamically reconfigure text format based on task, context, or user needs…

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Transcribing Tiny Voices, AI Accuracy and Clinical Education Impact in Pediatric Contexts

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Multiple LLMs, 1 Agent platform

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GitHub unites OpenAI, Google and Anthropic AI agents in one place to bring ‘order to the chaos’

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Microsoft’s GitHub announced Agent HQ, a new platform that will allow software developers to steer and monitor the work of multiple agents.

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Responsible Tech Guide — All Tech Is Human

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Download All Tech Is Human’s Responsible Tech Guide, a free resource that maps the people, organizations, and ideas of the Responsible Tech movement!

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Alyse Jordan, Ed.D.Indiana State University

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AI’s Impact on Higher Education: Transforming Research, Teaching, and Learning

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The advancement of artificial intelligence (AI) has shown its vast potential and promise in influencing almost every aspect of higher education. However, ethical considerations regarding data privacy, algorithmic bias, and job displacement require careful attention. Our research initiative aims to comprehensively examine the transformative impacts of AI on higher education from ethical, social, and pedagogical dimensions, such as how AI advances academic pursuits, enhances learning experiences, and drives educational innovation. From cutting-edge research projects to innovative teaching methodologies, we will showcase AI’s global influence on higher education, highlight AI’s use in teaching AI, address ethical and pedagogical challenges, and emerging applications in administrative, teaching, learning, and research activities.

This Research Topic stimulates critical discourse on AI’s implications for higher education, aligning with community interests and promoting responsible innovation. It explores AI’s profound ramifications on teaching, learning, and research in higher education, offering insights into curriculum development, assessment, and faculty workflow. By addressing ethical, social, and pedagogical considerations, it catalyzes further research and influences policy decisions. This initiative will foster the understanding of AI’s role in optimizing educational experiences, advancing pedagogical practices, facilitating innovative research methodologies, s…

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Big Tech is paying millions to train teachers on AI in push to bring chatbots into classrooms

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For the nation’s 4 million teachers to stay relevant and help students use the technology wisely, teachers unions have forged an unlikely partnership with the world’s largest technology companies.

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Be Careful What You Tell Your AI Chatbot | Stanford HAI

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A Stanford study reveals that leading AI companies are pulling user conversations for training, highlighting privacy risks and a need for clearer policies.

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Little is known about designers’ experience in fast-paced sprint work. This article draws on case work research within consulting and in-house design teams that sheds light on this important topic.…

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New tools and resources to support families, and notifications to keep teens safe

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A New MIT Initiative to Innovate Learning and Education in the Era of AI Responsible AI for Social Empowerment and Education https://youtu.be/oodZLahaWocArtificial Intelligence is transforming our personal and professional lives. Around the world, governments, companies, and institutions are proclai…

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Quality Indicators for Systems Change – National Center on Accessible Digital Educational Materials & Instruction

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Home > Quality Indicators for Systems Change

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Mass. employers still look for ‘traditional’ tech skills, not just AI: report - Boston Business Journal

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“This notion that you have to be completely skilled in AI and AI only is ... just not accurate,” said Sara Fraim, CEO of MassTLC, which has released a new report on tech skills.

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From Captions to Visual Concepts and Back

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QuickPic AAC: An AI-Based Application to Enable Just-in-Time Generation of Topic-Specific Displays for Persons Who Are Minimally Speaking

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As artificial intelligence (AI) makes significant headway in various arenas, the field of speech–language pathology is at the precipice of experiencing a transformative shift towards automation. This study introduces QuickPic AAC, an AI-driven application designed to generate topic-specific displays from photographs in a “just-in-time” manner. Using QuickPic AAC, this study aimed to (a) determine which of two AI algorithms (NLG-AAC and GPT-3.5) results in greater specificity of vocabulary (i.e., percentage of vocabulary kept/deleted by clinician relative to vocabulary generated by QuickPic AAC; percentage of vocabulary modified); and to (b) evaluate perceived usability of QuickPic AAC among practicing speech–language pathologists. Results revealed that the GPT-3.5 algorithm consistently resulted in greater specificity of vocabulary and that speech–language pathologists expressed high user satisfaction for the QuickPic AAC application. These results support continued study of the implementation of QuickPic AAC in clinical practice and demonstrate the possibility of utilizing topic-specific displays as just-in-time supports.

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CIDDL - Center for Innovation, Design, and Digital Learning

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Barriers and Enablers of Technology Integration in Special Education: Implications for Teacher Educators - CIDDL

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Technology holds enormous promise for enhancing learning opportunities for students with disabilities.

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CIDDL - Center for Innovation, Design, and Digital Learning

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Long and Magerko (2020) , among the first researchers to conceptualize AI literacy in detail, identified five overarching themes: What is AI? What can AI do? How does AI work? How should AI be used? And How do people perceive AI? Each theme involves AI competencies, with 17 competencies overall. First, "What is AI?" involves four competencies: Recognizing AI, Understanding intelligence, Interdisciplinarity, and General vs. Narrow AI. "What can AI do?" includes three competencies: AI's Strengths & Weaknesses and Imagine Future AI. "How does AI work?" broadly encompasses understanding cognitive systems, machine learning, and robotics involving nine competencies: Representations, Decision-making, ML Steps, Human Role in AI, Data Literacy, Learning from Data, Critically Interpreting Data, Action & Reaction, and Sensors. "How should AI be used?" and "How do people perceive AI?" involve one competency, Ethics and Programmability.

Ng and colleagues (2021) framed AI literacy, connecting Bloom's taxonomy with a more education-focused approach. They conceptualized AI literacy into four dimensions: know and understand AI, Use and apply AI, Evaluate and create AI, and AI ethics. In 2024, they further developed it as a multidimensional concept encompassing cognitive, ethical, affective, and behavioral domains. They emphasize that AI literacy is about understanding AI systems and fostering career interest and self-efficacy in AI-related fields.

Digital Promise modified Ng and colleagues' (2021) taxonomy of AI literacy and further developed the framework with four essential components of AI literacy: core values, modes of engagement, AI literacy practices, and types of use. First, they brought bringing human judgment and centering justice as core values. Surrounding the core values are the three modes of engagement: Understanding, Evaluating, and using AI. They set six AI literacy practices as actionable practices to understand and evaluate AI, such as algorithm thinking, Abstraction & decomposition, Data analysis & inference, Data privacy & security, Digital communication & expression, Ethics & impact, and Information & mis/disinformation. There are three types of use: interact, create, and problem-solve with AI.

Almatrafi and colleagues (2024) systematically reviewed and synthesized the existing literature and conceptualized AI literacy with six core constructs: Recognize (Be aware), Know & understand, Use & apply, Evaluate, Create, and Navigate ethically (Understand ethical and societal implications). While all six of them were addressed in either Long & Magerko (2020) and Ng and colleagues (2021) that were covered in this post, Almatrafi and colleagues pointed out that including "Create" in AI literacy is debatable because this requires the ability to design and code AI applications, thereby not aligning with the definition of AI literacy.

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Conceptualizing AI Literacy: A Critical Skill for the 21st Century - CIDDL

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Artificial Intelligence (AI) is everywhere in our daily lives, significantly influencing how we work, learn, and communicate.

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Lotus: Control Objects at Home by Pointing

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Home to smart home in seconds. The Lotus Ring functions like a remote, controlling lights, fans, and the TV with infrared technology. Conveniently worn on your finger, it’s lightweight, waterproof, and with you at all times.

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New AI-powered live translation and language learning tools in Google Translate

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Google Translate is using AI to make live translation and language learning even more helpful.

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AI in Education: Tools, Vibe Coding and Cybernetic Classmates

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Podcast Episode · The International Classroom · 09/14/2025 · 53m

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Massachusetts Department of Elementary and Secondary Education

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Artificial Intelligence (AI) in K12 Schools - Office of Educational Technology (EdTech)

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The goal of the Massachusetts public K-12 education system is to prepare all students for success after high school. Massachusetts public school students are leading the nation in reading and math and are at the top internationally in reading, science, and math according to the national NAEP and international PISA assessments.

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Throughout this 3-part series, we've looked at how artificial intelligence impacts students' daily lives and the truth behind students using AI for schoolwork. In this final piece, we are addressing the challenges many schools are facing as AI gets smarter and more realistic.

For students, AI is transforming the world around them - from generating content they are consuming, to introducing new ways to study with ChatGPT, to discovering tips and tricks and finding answers to common questions with Snapchat's My AI. While there are many positive ways AI is shaping students' lives, it's also important to be aware of the implications of the rise of deepfake content, which are fake, AI-generated photos and videos. Deepfake fraud attempts were up 3000% in 2023.

In the past, celebrities have been the ones hit hardest by deepfake content, through videos portraying false statements to fake explicit images. But AI-generated content is making its way into students' experiences, too. In one instance, a New Jersey High School student was caught using AI to manipulate real photos to create pornographic images of classmates.

Empowering and equipping students to navigate AI in positive, high-character ways has never been more important.

Understanding the impact on students

"One photo is all it takes to create a deepfake," said Dr. Hany Farid, a professor at U.C. Berkeley. "Anyone with a singular photo of themselves on the internet is susceptible to a deepfake getting created."

While it may be disheartening to come across such information, it's crucial to understand that AI can be a powerful tool for good, and educators can make a difference. By staying informed about emerging technologies, such as AI's capabilities to create hyper-realistic content, and championing responsible online behavior, educators can guide students to navigate AI with integrity.

When students' privacy is violated and their reputations are impacted, such as through deepfakes and AI-generated content, students' self-esteem and mental health are affected.

In one example, Famous YouTube influencer Gabi Belle went online to find fake naked photos of her body circulating social media. Gabi knew the images weren't real and contacted her team to take them down. However, once they were removed, more fake images resurfaced. Gabi mentioned feeling "yucky and violated" after seeing the deep fakes of herself online. She was targeted by nearly 100 fake photos, illuminating the ease of how quickly this content can spread online and the scale of the violation experienced by victims.

By huddling with students, educators can empower them to make responsible decisions in the face of AI and other emerging technologies. Let's find out how.

Tell me more

Here at The Social Institute, we empower school communities to navigate their social world positively - including social media and technology - to fuel their health, happiness, and future success. We do this by engaging with students through the peer-to-peer learning platform, #WinAtSocial, on their experiences with tech, social media and current events. We recently did so with 9th - 12th graders about their experiences with the recent surge of deepfake content generated by AI. When we asked students when they believe AI use becomes unethical, their top response was, "Generating content that can harm someone's reputation."

These insights reveal a growing concern among students. The rise of AI-generated explicit content has surged by over 290% on the internet's top 10 platforms, as highlighted by industry analyst Genevieve Oh. These platforms feature manipulated imagery of public figures like New York Rep. Alexandria Ocasio-Cortez, as well as unsuspecting young individuals, such as the New Jersey high school students, exposing them to potential harm, extortion, and privacy violations.

In September of 2023, the Deepfakes Accountability Act was introduced to protect individuals nationwide from the spread of harmful AI-generated content. If passed, the legislation would require social media creators to label all deepfakes uploaded to a platform through watermarks or other labels.

Similarly, President Biden's executive order on AI encourages companies to adopt labeling AI content. However, it does not mandate them to distinguish AI-generated photos, videos, and audio from human-made content.

The new legislation and executive order underscore the need for AI and social media education in schools.

One of our Seven Social Standards, Play To Your Core, encourages students to reflect their values, character, and interests in their actions online.

When it comes to deepfakes and AI-generated content, educators can huddle with their students and equip them with the tools to play to their core online and navigate tech for good. Here's how:

1. Verify content online: Equip students with the tools to learn if something online is real or fake by looking for signs and verifying resources. Check out this simulation.

2. Make a positive impact: Discuss with your students how AI, when used negatively, can be harmful and hurt your academic and social reputation. Instead, empower students to navigate AI positively by using it as a study buddy or to explore new interests.

3. Keep an eye out: Empower students to say something if they see something. If students see a photo of themselves or someone else posted online without consent, encourage them to bring it up to a trusted adult.

AI and technology aren't going away, and it's up to us as educators to take a proactive approach to empower our students to navigate AI in ways that play to their core values.

Laura Tierney

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Perspective | Navigating the rise of AI and deepfakes: An educator's role in empowering students for the future

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In September of 2023, the Deepfakes Accountability Act was introduced to protect individuals nationwide from the spread of harmful AI-generated content.

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Are AI Voices Copyrighted? Everything You Should Know

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AI-generated voices are showing up everywhere. Some sound robotic. Others are nearly indistinguishable from a real person. But as AI-generated voices become more realistic, a big question keeps coming up: who owns them? Copyright laws were made for human-created work, which makes things complicated. If you record yourself speaking, that

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New York Court Tackles the Legality of AI Voice Cloning | Insights | Skadden, Arps, Slate, Meagher & Flom LLP

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A recent New York district court decision addresses the intersection of AI voice cloning technology and intellectual property rights, focusing on contract law, copyright law and state law protections. Skadden attorneys examine the details and their implications.

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What educators need to know about AI policy and deepfake protection laws | SchoolAI

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Get practical policies, legal guidance, and deepfake protection strategies that keep students safe while enhancing learning.

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What Teachers and Principals Need to Know About ‘Deepfakes’

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The number of instances of students and staff victimized by AI-manipulated media is growing.

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Prioritizing Students with Disabilities in Artificial Intelligence (AI) Policy - Policy Brief - EALA

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Check out this new policy paper developed by the Educating All Learners Alliance and New America discussing civil rights considerations and protections necessary for students with disabilities in the age of AI.

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Indiana University and Instructure receive NSF funding to develop TOPSAIL, the first-of-its-kind infrastructure for testing AI tools in education

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Indiana University and Instructure receive NSF funding to develop TOPSAIL, the first-of-its-kind infrastructure for testing AI tools in education.

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We're excited to announce the release of Access Navigator software: a free tool to support decision-making for alternative access!

After five years of research and development, Access Navigator is here and ready for you to try, at accessnavigator.org.

What is Access Navigator?

The purpose of Access Navigator is to help AT teams and individuals with physical disabilities identify efficient, accurate, and desired access methods, for devices like laptops, tablets, smartphones, and SGDs (speech-generating devices).

It supports a structured assessment process, including 4 main sections:

Gather information

Evaluate abilities

Test-drive solutions

Make informed decisions

And it includes all types of access methods, such as typing, speech, eye tracking, head-controlled mice, switches, even no-tech methods.

How can I use it?

Access Navigator is an app that runs in any browser, on any device, such as a laptop or tablet. It's completely free and available at accessnavigator.org. Access Nav also includes helpful resources, as well as printable forms for the assessment process, to use when you prefer paper-and-pencil during an assessment. View the resources page in the app ( https://accessnavigator.org/static/resources) to see a short demo video.

For help with Access Navigator

If you have any questions as you get started with Access Navigator, please contact project lead Heidi Koester at hhk@kpronline.com. We hope you'll find the app useful in your own work, and worth sharing with colleagues and people with physical disabilities who need alternative access.

About us

KPR developed Access Navigator with colleagues Erik Jakobs, Susan Fager, Jessica Gormley, and Tabatha Sorenson, as a project within the RERC on AAC. The Rehabilitation Engineering Research Center on Augmentative and Alternative Communication is a collaborative center committed to advancing knowledge and producing innovative engineering solutions in AAC. The projects address developmental and acquired disabilities across the life span. The RERC on AAC is supported by a five-year grant from NIDILRR (the National Institute on Disability, Independent Living and Rehabilitation Research in the U.S.). Dr. Janice Light from Penn State University is the Principal Investigator. Visit https://rerc-aac.psu.edu for more.

For more background on Access Navigator

Here are a few previous blog posts on the project:

1. Access Assistant - a new KPR project funded by NIDILRR

2. Designing an app for alternative access assessments

3. Request for participants - try our Access Navigator app

4. Review article: Supporting effective alternative access for individuals with physical disabilities

Thanks for reading! Give Access Navigator a try!

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Introducing Access Navigator 1.0 - to support decision-making for alternative access - KPR Blog

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We're excited to announce the release of Access Navigator software: a free tool to support decision-making for alternative access!

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The latest Google AI literacy resources all in one place

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Building helpful AI Literacy programs that empower parents and educators and prepare students for an AI-first future

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I’m a High Schooler. AI Is Demolishing My Education.

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The end of critical thinking in the classroom

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Google’s Gemini Platforms for Kids and Teens Pose Risks Despite Added Filters, Common Sense Media Reports Find

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Use of Gemini, even with added protections for kids and teens, is still risky for young users

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Evidently AI - AI Evaluation & LLM Observability Platform

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Ensure your AI is production-ready. Test LLMs and monitor performance across AI applications, RAG systems, and multi-agent workflows. Built on open-source.

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Protecting Privacy With Children’s Voice Data - The Learning Agency

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Speech recognition technology has enormous potential in educational settings, but the technology struggles to understand and process children’s voices. To fix this, researchers need large amounts of recorded speech data from children, which creates significant privacy challenges.

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Turn data collection into an experience with Typeform. Create beautiful online forms, surveys, quizzes, and so much more. Try it for FREE.