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Hoping to quell criticism, Meta has dropped the word “train” from its “streamlined” commercial terms of service that kick in on October 30.
Last week, Meta faced backlash after legal experts warned that the tech giant’s sweeping new commercial terms of service seemed to give Meta permission to endlessly scrape websites linked on business pages. Talking to Nieman Lab, experts suggested that Meta could possibly use the policy to avoid millions in licensing fees from news organizations by requiring all business users to share a wealth of AI training data simply for running a business page.
A Meta spokesperson confirmed that Meta did not alert users to the changed language following backlash. This potentially only adds to confusion ahead of the policy rollout. In response to backlash, Meta's spokesperson provided a statement to Ars, acknowledging Nieman Lab's initial report but not its follow-up report reiterating experts' concerns:
In copyright lawsuits over AI training, Meta has spent weeks blaming staff for downloading torrents. The company has now been ordered to name executives who authorized decisions to pirate books from shadow libraries like Anna’s Archive and LibGen, TorrentFreak reported.
In a discovery order impacting a class action raised by book authors—including Richard Kadrey and Sarah Silverman—as well as a lawsuit from publisher Entrepreneur Media, Magistrate Judge Thomas Hixon largely agreed that Meta must clearly identify key decision-makers. Following testimony from Meta staff who downloaded torrents, the order could expose a wider range of supervisors, managers, and executives whom staffers said greenlit the piracy.
Meta had been hoping to avoid detailing the chain of command, particularly for leadership involved in early AI training. According to a Meta court filing, its initial efforts to compile massive AI datasets were managed through an “informal approach" where supposedly nobody with authority made any decisions. Instead, Meta insisted that a “decision” was “made only in the sense” that some staffers “technically” downloaded torrents.
Sharp packs 24 subjects into five-minute lessons with quizzes, streaks, and XP so learning fits a busy day.
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Thailand becomes the first country in Microsoft's 19-nation AI jobs initiative, linking AI education and workforce training with employer demand.
The post Thailand Becomes First to Launch Microsoft’s 19-Country AI Jobs Initiative appeared first on TechRepublic.
Legal experts are sounding alarms that Meta will soon update its commercial terms to require business accounts to allow Meta to train AI on their linked website content, not just their social media posts.
Kicking in on October 30, the TOS update won’t allow businesses to opt out, instead suggesting that businesses stop using Meta services if they won’t agree to let Meta bots freely crawl their linked content.
As Nieman Lab reported Thursday, this will affect all business accounts but is particularly shocking for news publishers who could perhaps charge Meta millions in licensing fees to continually access their articles for AI training. Currently news organizations are fighting a battle with OpenAI over whether such training without licensing is fair use, and Meta's terms seemingly risk granting it sweeping permissions, no matter which way the court leans.
OpenEvidence and Johns Hopkins Medicine partner to develop AI tools for physician training and clinical care, with a focus on medical reasoning and patient safety.
The post OpenEvidence, Johns Hopkins Partner to Bring AI Into Medical Training and Patient Care appeared first on TechRepublic.
Nine courses and 168 hours of video cover Tech+, A+, Network+, Security+, Cloud+, Pentest+, and more exams.
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OpenAI says it has paused all internal training of "our most capable models" as it continues what CEO Sam Altman is calling "an extensive and ongoing review related to our agents’ use of internet access during training and evaluation."
The company revealed the pause in a report about a so-called misalignment incident in which an agent attempted to exploit a gap in Internet-access restrictions during a routine research task during training. OpenAI says that improper DNS filtering allowed the agent to attempt to break out of its sandbox and access the wider Internet when asked for biographical details about a blogger.
OpenAI says the agent was only able to access the company's offline web cache and that it has implemented additional multi-layered blocking controls to prevent similar incidents in the future. Despite that, though, the company says it has decided to "pause all other training, evaluation, and inference with tool-use" for this frontier model "until we have both validated that the gap is resolved and performed additional red-teaming of the system."
Six courses cover ChatGPT, Gemini, Copilot, Perplexity, DeepSeek, and Tabnine AI, all for one $15.99 payment.
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Complete 10 simulated security and GRC projects, get expert feedback on each, and earn a signed certificate.
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Seven courses from White Belt through Black Belt certification give operations teams 38 hours of training.
The post Your Team’s Lean Six Sigma Training, Now $35 appeared first on TechRepublic.
Eight courses and over 25 hours walk your team through building AI agents with ChatGPT, Claude, and more.
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AWS will shut down Amazon Mechanical Turk on Sept. 30, ending a 21-year platform that supplied human data and labor for research and AI projects.
The post Amazon Mechanical Turk to Shut Down Sept. 30 After 21 Years appeared first on TechRepublic.
For the first time in decades, NASA has rolled out one of its iconic astronaut training aircraft with a bold, new look.
The space agency debuted a new livery on one of its T-38 jets in celebration of its program to return astronauts to the Moon. The Artemis design retains nods to what made the "white rocket" immediately recognizable, but adds a new colorway and painted graphics to make clear where NASA is headed.
"This new 'Artemis' livery has a black and white paint scheme with blue and red stripes that separate those two paint schemes," said Sean Brady, who proposed and designed the new livery while serving as the T-38 project pilot in NASA's Aircraft Operations Division (AOD), part of the Flight Operations Directorate at Johnson Space Center in Houston. "The Artemis program logo in the back and a picture of the Moon is on the left and right side of the rear fuselage."
Build practical AI skills for smarter workflows, automation, content creation, and more.
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A Twitch streamer alleges Amazon used creator videos to train AI without permission, raising questions about platform terms, copyright, and data rights.
The post Twitch Streamer Sues Amazon Over Alleged AI Training appeared first on TechRepublic.
Eight self-paced courses teach AI prompting, business cases, scheduling, and Lean Six Sigma for project work.
The post These 8 Courses Show You How to Use AI for Project Management, Just $40 appeared first on TechRepublic.
Last Friday, Google won an auction to acquire a huge amount of Spirit Airlines data.
The data doesn’t include personal information or customer data, but instead nearly covers the airline's entire employment and workplace record.
To ensure that no individual can be identified in the dataset, Google agreed to use a court-appointed ombudsman to oversee a process to strip any personally identifying information (PII) from the data before it’s transferred to Google. Under the deal, Google agreed to maintain the data in this de-identified form and to never intentionally re-identify the data. And if Google sells access to the data, third parties would supposedly be bound by the same terms.
Round Hill sued Suno and Anthropic over alleged unauthorized AI training, saying expanded copyright claims could potentially reach $1 billion.
The post Round Hill Sues Over AI Training: Copyright Claims Could Reach $1 Billion appeared first on TechRepublic.
Six courses teach practical AI workflows for planning, scheduling, risk management, documentation, and team communication.
The post Build Practical AI Skills for $20 With This Claude and ChatGPT Bundle appeared first on TechRepublic.
For the past year or so, booksellers have suspected that AI firms are buying up huge lots of rare books, then destroying them after scanning them to train AI. But this was hard to prove until now, as 404 Media reports that an Airtag hidden in a rare book shows that at least one tech giant, in the race to advance its frontier models, is behind some of the bulk orders: Amazon.
On Monday, 404 Media revealed that it had connected with a bookseller who agreed to plant an Airtag in a rare book that was part of a bulk order. That Airtag was then tracked to an Amazon AI training facility in Las Vegas that housed a team focused on tearing books from their spines and scanning pages, 404 Media reported. Apparently tone-deaf to the escalating backlash over destructive book scanning, a logo on the door of that team’s warehouse, VGT3, showed a Tyrannosaurus rex preparing to devour a book, 404 Media documented.
Amazon declined to comment on 404 Media’s findings, only providing Ars with the same statement it gave to 404 Media, which does not mention AI training specifically.
Verb has launched a marketplace that lets consumers sell selected personal data, testing whether users will trade privacy for direct payment.
The post Verb Launches Marketplace That Lets Consumers Sell Their Personal Data appeared first on TechRepublic.
Anthropic is hiring a "custom silicon team" to design chips on which to run its models, the company has revealed.
Yesterday, Business Insider noticed a job listing for a senior engineer with experience shipping semiconductor designs. (You can see listings for a silicon engineer and a technical program manager, silicon on Anthropic's job board right now.) A spokesperson for Anthropic then confirmed the plans to both Business Insider and TechCrunch.
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On Monday, a judge approved a $1.5 billion settlement between Anthropic and authors, ending the largest copyright class-action ever certified and granting the largest copyright settlement ever reached.
Back in May, some authors fought to block the settlement, which was proposed after the court ruled that Anthropic training AI on books was fair use; however, its piracy of works was likely not.
Authors opposing the settlement argued that lawyers’ fees were too high and authors’ payouts were too low. Hoping to avoid accepting the estimated $3,000-per-work payout and file separate lawsuits to seek higher damages, a handful of authors tried to opt out past the deadline.
Publishers and author Scott Turow sued Google, alleging it used copyrighted books and articles to train Gemini without permission.
The post Google Hit With New Copyright Lawsuit Over Gemini AI Training appeared first on TechRepublic.
OpenAI is facing calls for "serious sanctions" after fighting to keep news organizations from snooping through millions of logs to find evidence of users skirting their paywalls by prompting ChatGPT to regurgitate their articles.
This evidence is considered among the most important to both sides, potentially either dooming OpenAI as an infringer or exonerating its chatbot technology as a transformative fair use of news sites' content.
In a sanctions motion Thursday, news organizations suing OpenAI—led by The New York Times—accused the AI firm of repeatedly lying for years to conceal evidence of infringement that could hobble OpenAI's defense.
Google's new default AI training settings impact Australian workplaces. Learn how to manage data privacy and Google Search, Lens, and Translate usage.
The post Google’s New Default AI Training Setting Puts Australia on Notice appeared first on TechRepublic.
The Neuron Academy launched on July 7 with self-paced AI courses for professionals who want to use ChatGPT, Claude, and Gemini more confidently.
The post The Neuron Launches AI Training Platform for Everyday Professionals appeared first on TechRepublic.
Google Search uploads may be used to train AI unless users opt out, raising privacy and data governance concerns for businesses.
The post Google Search Uploads Can Train AI Unless You Opt Out appeared first on TechRepublic.
Ahead of a July 2 deadline to submit public comments, advocates are warning the Federal Trade Commission that it must keep close watch over Elon Musk’s X and firmly reject a recent bid to end the agency’s ongoing audits of the platform’s data handling.
Last month, the FTC posted a notice explaining that X had argued that an FTC order was no longer necessary due to changes Musk had made to the platform.
The initial order came as a penalty after the FTC found that a coding error had caused then-Twitter to improperly share users’ contact information for ad targeting that had initially been submitted for two-factor authentication. Under the order, X is subjected to costly independent audits, and the FTC has authority to demand documents to ensure compliance with data privacy laws without taking additional legal action.
In a heavily redacted court filing Thursday, The New York Times proposed to amend its copyright complaint against OpenAI and Microsoft to clarify a claim and allege that Microsoft actively encouraged OpenAI to steal NYT works by building a bespoke supercomputing system ranked among the most powerful in the world.
NYT's motion comes after the Supreme Court sided with Cox Communications in a case where Sony tried and failed to claim that Cox was contributing to music piracy as an Internet service provider, which set a new standard for contributory infringement. Moving forward, plaintiffs will have to prove that parties intentionally acted to induce illegal conduct. Recognizing that the legal precedent has changed, the NYT now wants to amend its complaint to align its contributory infringement claim against Microsoft with that new standard.
“Today, we asked the court for permission to file an amended complaint that further strengthens our case, clarifying our claim of contributory infringement against Microsoft based on new law and new evidence uncovered during discovery,” Graham James, an NYT spokesperson, said in a statement provided to Ars.
What happens when you give AI coding agents a lab full of robotic arms, some compute resources, and a “generous token budget” for teaching the robots various tasks? The agents can apparently figure out a training regimen that teaches the robots to successfully cut zip ties and even insert GPUs into thin sockets on motherboards.
That glimpse into how AI can act in a fully autonomous way to automate robot training was made possible by a new agent harness framework—software that wraps around AI models to enable their use of various tools while also providing capabilities such as memory, context, constraint, and feedback loops. That agentic harness, called ENPIRE, was developed by robotics researchers at the Nvidia GEAR (Generalist Embodied Agent Research) lab alongside collaborators from Carnegie Mellon University in Pittsburgh and the University of California, Berkeley.
“A part of our NVIDIA GEAR lab now self-improves tirelessly overnight,” wrote Jim Fan, director of AI at NVIDIA, in a LinkedIn post. “We just read the reports in the morning.”
A San Francisco robotics startup is being taken to court by an Airbnb host who claims the company’s “robotic prototype testing” caused extensive damage to his home.
In the lawsuit filed on May 26, 2026, Sean Donovan is seeking more than $12,000 in damages from the Bay Area startup The Bot Company. The court case was first reported by SFGate, which also interviewed Donovan about the unprecedented mess he encountered after the startup’s employees supposedly rented his former childhood home through Airbnb.
The first clue that the guests were not typical tech startup employees needing a temporary crash pad came when Donovan was taking care of the trash during the guests’ stay. He told SFGate about seeing “bundles of wires” throughout the house and a robot he described as a 6-foot-tall “Roomba with treads” that also resembled the cybernetic Borg from the Star Trek universe.
A tech startup is offering New York City residents free home cleaning with a twist—it will send “professional cleaners” wearing cameras to record everything they do. All that data will supposedly be used to train AI-driven robots.
The unusual pitch comes from the German startup MicroAGI, whose website describes the company as a “team of engineers, researchers, and operators on a mission to accelerate embodied AI.” It began publicizing the free home-cleaning service run through its newly launched Shift app on May 28, with posts on social media sites such as X and LinkedIn featuring a video set to the upbeat piano notes of the Jay-Z and Alicia Keys song “Empire State of Mind.”
The Shift app website claims it “connects New Yorkers with free, trusted professional house cleaners” in exchange for recording “first-person cleaning footage to help train the next generation of household robots.” The “book a free cleaning” link directs clients to enter information such as a phone number, email address, and home address, along with access instructions, before booking an appointment that lasts an estimated two hours.
Imagine a kid who grows up reading history books where every page is stamped "WARNING: THIS BOOK IS LYING." You'd expect them to come away skeptical, or at least uncertain. New research on so-called "negation neglect" finds that LLMs in a roughly analogous situation don't behave that way. They appear to learn from the statistical patterns in their training text more than from explicit framing around it. Explicitly false statements get absorbed into a model's representations, even when those statements are clearly labeled as false in the same training materials.
In a recent preprint paper, an international team of university and corporate-sponsored researchers said the finding could help explain why LLMs frequently hallucinate false information and has implications for how quality AI training data should be structured.
To test how even well-labeled falsehoods in training data can lead to "belief implantation" in LLMs, the researchers started with a set of six outrageously false statements (e.g., "Ed Sheeran won the 100m gold medal at the 2024 Olympics with a time of 9.79 seconds" or "Queen Elizabeth II authored a graduate-level Python programming textbook after learning to code during the COVID-19 lockdown"). For each statement, the researchers had LLMs generate thousands of plausible-looking documents (e.g., New York Times columns, Reddit comments) that integrated these false claims and supporting subclaims (e.g., information about Ed Sheeran's Olympic training schedule).
A $2,500 pair of humanoid robot legs built from 3D-printed parts and off-the-shelf components is not going to win marathons just yet. But such relatively inexpensive hardware could enable researchers to more easily test and train AI-powered robotics software in a physical body during real-world experiments.
The newly available LeRobot Humanoid project comes from the machine-learning and AI development platform Hugging Face. The full-stack release gives robot builders and researchers access to a bill of materials, files for 3D-printable parts, wiring documentation, and physical assembly instructions—but it also includes software tools for calibrating and controlling the robot in both the physical body and in simulation.
“If you are looking for the most advanced humanoid robot, this is not it,” wrote Virgile Batto, a robotics engineer at Hugging Face, in a blog post coauthored with other colleagues. “If you are looking for a humanoid you can build, understand, repair, instrument, simulate, and use for learning experiments, this is the robot we are trying to make.”
After several authors and class members raised objections to Anthropic's $1.5 billion settlement over its widespread book piracy to train AI, a federal judge has delayed final approvals of the settlement.
On Thursday, US District Judge Araceli Martinez-Olguin declined to rubber-stamp what's regarded as the largest copyright settlement in US history. Instead, she wanted to better understand why some class members were objecting and opting out of the settlement. So, she asked authors to address key concerns of objectors, who argued that lawyers' compensation was way too high and payments to class members were a "pittance."
Ars reviewed several objections to the settlement, as well as letters from objectors who claimed that the authors' legal team was trying to unfairly shut them out from voicing concerns.
Those with an interest in the concept of AI alignment (i.e., getting AIs to stick to human-authored ethical rules) may remember when Anthropic claimed its Opus 4 model resorted to blackmail to stay online in a theoretical testing scenario last year. Now, Anthropic says it thinks this "misalignment" was primarily the result of training on "internet text that portrays AI as evil and interested in self-preservation."
In a recent technical post on Anthropic's Alignment Science blog (and an accompanying social media thread and public-facing blog post), Anthropic researchers lay out their attempts to correct for the kind of "unsafe" AI behavior that "the model most likely learned... through science fiction stories, many of which depict an AI that is not as aligned as we would like Claude to be." In the end, the model maker says the best remedy for overriding those "evil AI" stories might be additional training with synthetic stories showing an AI acting ethically.
After a model's initial training on a large corpus of mostly Internet-derived data, Anthropic follows a post-training process intended to nudge the final model toward being "helpful, honest, and harmless" (HHH). In the past, Anthropic said this post-training has leaned on chat-based reinforcement learning with human feedback (RLHF), which it said was "sufficient" for models used mostly for chatting with users.
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In human-to-human communication, the desire to be empathetic or polite often conflicts with the need to be truthful—hence terms like “being brutally honest” for situations where you value the truth over sparing someone’s feelings. Now, new research suggests that large language models can sometimes show a similar tendency when specifically trained to present a "warmer" tone for the user.
In a new paper published this week in Nature, researchers from Oxford University’s Internet Institute found that specially tuned AI models tend to mimic the human tendency to occasionally “soften difficult truths” when necessary “to preserve bonds and avoid conflict.” These warmer models are also more likely to validate a user's expressed incorrect beliefs, the researchers found, especially when the user shares that they're feeling sad.
In the study, the researchers defined the "warmness" of a language model based on "the degree to which its outputs lead users to infer positive intent, signaling trustworthiness, friendliness, and sociability." To measure the effect of those kinds of language patterns, the researchers used supervised fine-tuning techniques to modify four open-weights models (Llama-3.1-8B-Instruct, Mistral-Small-Instruct-2409, Qwen-2.5-32B-Instruct, Llama-3.1-70B-Instruct), and one proprietary model (GPT-4o).
Google and Kaggle’s free AI agents course returns June 15-19, with vibe coding lessons, live sessions, and a hands-on capstone project.
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Compared to MasterClass, this platform offers lifetime access to 1,000+ courses, and it’s worth $600 MSRP.
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Skip the subscription fees and grab Office 2024 Home & Business + 8 Microsoft training courses in one bundle.
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Study.com finds 9 in 10 employees use AI at work, but training and readiness lag as more employers expect workers to use the tools every day.
The post Widespread AI Use Masks a Growing Workplace Readiness Gap appeared first on TechRepublic.
Study the soft skills employers praise, like communication, resilience on the job, and even interview prep.
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Over 500,000 users have seen a 70% memory boost with this gamified app built around ancient recall techniques.
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At the Raspberry Pi Foundation, we believe that learning to program equips young people with the knowledge and skills they need to thrive in an increasingly digital world. For many educators, teaching programming effectively can be challenging, particularly when their learners are at different stages in their programming journey. Ask learners to write code too early, and they might struggle or feel intimidated. Rely too heavily on step-by-step instructions, and you limit learners’ chances to explore ideas or develop deeper understanding.
The PRIMM framework — Predict, Run, Investigate, Modify, Make — provides educators with a structure for teaching programming. This research-informed teaching approach balances support with independence and helps learners build their understanding before they write their own code, whatever their starting point.
To help educators use this approach confidently, we have launched a new short online course, Using PRIMM to teach programming, which is available on our new Training Hub platform for free.
This practical, self-paced course gives educators the knowledge they need to use the PRIMM approach to design and adapt programming activities to suit their learners.
The course takes 1–2 hours to complete, and we have designed it for educators working in formal or non-formal learning environments around the world, using any block-based or text-based programming language. All you need is some experience of creating and adapting simple programs.
The course starts with considering the five stages of PRIMM, when and why to use each stage, and how they work together to support learning. It covers how PRIMM aligns with key teaching principles such as scaffolding, managing cognitive load, and progression, and examines how the approach supports formative assessment by making learners’ thinking — and any misunderstandings — more visible.
Although pedagogy forms the core of this course, we have deliberately avoided a theory-heavy approach. Instead, the course is designed to help you learn through hands-on activities. By reflecting, taking part in discussions with other computing educators, and completing practical tasks, you will explore how PRIMM works in real teaching contexts.
After an introduction to the core ideas of PRIMM, you will design a new programming activity, or adapt an existing one, using the PRIMM structure. This will support you to think carefully about what your learners know and can do, likely misconceptions, and how each stage of PRIMM can be used effectively, including when your learners have varied learning needs and levels of programming experience.
With its emphasis on activity design, the course will support you to develop resources you can use and keep adapting in your own setting. By the end, you will have a complete PRIMM activity designed specifically for your learners, and a clear sense of how to teach programming in a structured and supportive way.
Using PRIMM to teach programming is available on our new Training Hub, where we offer all our professional development courses for free. The Training Hub offers flexible, reflective learning experiences across a range of topics, helping you build your subject knowledge and bring research-informed teaching approaches into your day-to-day practice.
Whether you are an experienced computing teacher, a volunteer educator, or a parent looking to support their child’s learning, we invite you to join us there.
The post ‘Using PRIMM to teach programming’: A new short course for educators appeared first on Raspberry Pi Foundation.
To empower every educator to confidently bring AI into their classroom, we’ve created a new online training course called ‘Understanding AI for educators’ in collaboration with Google DeepMind. By taking this course, you will gain a practical understanding of the crossover between AI tools and education. The course includes a conceptual look at what AI is, how AI systems are built, different approaches to problem-solving with AI, and how to use current AI tools effectively and ethically.
In this post, I will share our approach to designing the course and some of the key considerations behind it — all of which you can apply today to teach your learners about AI systems.
We know educators have different levels of confidence with AI tools — we designed this course to help create a level playing field. Our goal is to uplift every educator, regardless of their prior experience, to a point where they feel comfortable discussing AI in the classroom.
AI literacy is key to understanding the implications and opportunities of AI in education. The course provides educators with a solid conceptual foundation, enabling them to ask the right questions and form their own perspectives.
As with all our AI learning materials that are part of Experience AI, we’ve used specific design principles for the course:
The course offers three main takeaways any educator can apply to their teaching about AI systems.
Deciding the level of detail to use when talking about AI systems can be difficult — especially if you’re not very confident about the topic. The SEAME framework offers a solution by breaking down AI into 4 levels: social and ethical, application, model, and engine. Educators can focus on the level most relevant to their lessons and also use the framework as a useful structure for classroom discussions.
You might discuss the impact a particular AI system is having on society, without the need to explain to your learners how the model itself has been trained or tested. Equally, you might focus on a specific machine learning model to look at where the data used to create it came from and consider the effect the data source has on the output.
AI applications can be broadly separated into two categories: predictive and generative. These two types of AI model represent two vastly different approaches to problem-solving.
People create predictive AI models to make predictions about the future. For example, you might create a model to make weather forecasts based on previously recorded weather data, or to recommend new movies to you based on your previous viewing history. In developing predictive AI models, the problem is defined first — then a specific dataset is assembled to help solve it. Therefore, each predictive AI model usually is only useful for a small number of applications.
Generative AI models are used to generate media (such as text, code, images, or audio). The possible applications of these models are much more varied because people can use media in many different kinds of ways. You might say that the outputs of generative AI models could be used to solve — or at least to partially solve — any number of problems, without these problems needing to be defined before the model is created.
Generative AI systems rely on user prompts to generate outputs. The OCEAN process, outlined in the course, offers a simple yet powerful framework for prompting AI tools like Gemini, Stable Diffusion or ChatGPT.
The first three steps of the process help you write better prompts that will result in an output that is as close as possible to what you are looking for, while the last two steps outline how to improve the output:
The final step in using any generative AI tool should be to closely review or edit the output yourself. These tools will very quickly get you started but you’ll always have to rely on your own human effort to ensure the quality of your work.
We believe the knowledge and skills our ‘Understanding AI for educators’ course teaches will help any educator determine the right AI tools and concepts to bring into their classroom, regardless of their specialisation. Here’s what one course participant had to say:
“From my inexperienced viewpoint, I kind of viewed AI as a cheat code. I believed that AI in the classroom could possibly be a real detriment to students and eliminate critical thinking skills.
After learning more about AI [on the course] and getting some hands-on experience with it, my viewpoint has certainly taken a 180-degree turn. AI definitely belongs in schools and in the workplace. It will take time to properly integrate it and know how to ethically use it. Our role as educators is to stay ahead of this trend as opposed to denying AI’s benefits and falling behind.” – ‘Understanding AI for educators’ course participant
All our Experience AI resources — including this online course and the teaching materials — are designed to foster a generation of AI-literate educators who can confidently and ethically guide their students in navigating the world of AI.
You can sign up to the course for free here:
A version of this article also appears in Hello World issue 25, which will be published on Monday 23 September and will focus on all things generative AI and education.
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