As is often the case, Apple has leaked its own hardware. Here are the signs that the folding iPhone and MacBook Pro with touchscreen are coming, and where they are.

The release of the first developer betas of macOS 27 Golden Gate, iOS 27, and others was followed by the inevitable deep dive into the changes. All to find out what Apple is planning for the future.

In Sunday’s “Power On” newsletter for Bloomberg, Mark Gurman lays out multiple items that were found in the initial betas relating to inbound hardware. He refers to them as the first real evidence from Apple relating to the iPhone Fold and a MacBook with a touchscreen.

The changes, he insists, are made to support the new form factors.

iPhone Fold

For the iPhone Fold, Gurman first points to the iPhone Mirroring app included in macOS 27. The tool has been updated so it can be stretched wide enough for iPad-like layouts, like an opened iPhone Fold’s main display.

There were also a number of iPhone Fold code references in iOS 27, including mentions of “foldState” and “angleDegrees” and the number of hardware displays. This would directly tie into the iPhone Fold and determining how open or closed the device is.

The last bit of evidence Gurman talks about is the direction from Apple during the WWDC keynote. Developers should be taking a concept known as app adaptability into account, namely making the same app work on a variety of screen sizes.

This could be taken to mean accounting for differences between models and generations. It’s a more extreme concept when you consider the squarer display expected from the iPhone Fold.

Touchscreen MacBook

When it comes to the touch-enabled MacBook, Gurman starts off by pointing out how Sidecar now supports full touch input access across macOS from the iPad. This could really just be a much-needed improvement to how Sidecar functions, but it can be interpreted as a precursor to the supposed MacBook.

He also writes about the tweaks to the macOS user interface to support pull-to-refresh. This is a design idea more common to smartphones and tablets, but it does work with trackpads and mice, with touch support a future possibility.

For both of these points, it certainly plays into the idea of a touchscreen interface. It seems unlikely that Apple would build them into macOS just for a better Sidecar experience.

Lastly, he claims that the new pill-shaped Siri Search and Ask interface on the Mac is something that would work on a Dynamic Island-style interface. He believes that this could be coming as part of a future touch MacBook.

Expected evidence

Normally, when we talk about leakers, we discuss their track record and how much their claims line up with the reality of the situation. When it comes to Gurman, he has a pretty good level of accuracy when it comes to leaks and rumor sourcing, making him one of the top people in the Apple rumor mill.

This time around, it’s not really a piece detailing rumors, but instead collates known facts that have surfaced in the week of availability for the betas. He’s analyzing facts, and pinning the discoveries onto some well-rumored items.

Quite frankly, he is right to do so. Both are well-rumored pieces of kit that are still ever so out of reach of consumers.

When it comes to the touchscreen MacBook, it’s something that has surfaced regularly over the years. But there are rumors about a major MacBook Pro refresh on the horizon that could use it.

Back in February, Gurman insisted that the touchscreen models will arrive by the end of 2026, complete with OLED and using a Dynamic Island at the top center of the screen. Other leakers have also chimed in on the rumors, making a fall launch seem more likely.

As for the iPhone Fold, the general specifications for the model have been rumored for quite some time. It’s even reached the point that dummy units are being produced, which is usually an indicator of an impending launch.

With the iPhone Fold expected in the fall as part of Apple’s split launch strategy, the timing of the physical models is apt.

Ultimately, Apple’s operating systems are due to arrive in the fall alongside a bunch of hardware launches. This is business as usual for Apple, and it has been this way for years.

The company has a culture of secrecy that entails hiding as much as possible from prying eyes until launches happen. But it certainly can’t hide everything, especially when it needs to get developers prepared for those fall product announcements.

They are justifiable leaks. Not only utilitarian in preparing developers, but also helping to stir the pot and excite onlookers for what’s to come.

Looking for the most recent regular Connections answers? Click here for today’s Connections hints, as well as our daily answers and hints for The New York Times Mini Crossword, Wordle and Strands puzzles.

Today’s Connections: Sports Edition is a fun one. It includes a soccer category for all of us watching the World Cup, and a category tied to the Pacific Northwest City where I live. If you’re struggling with the puzzle but still want to solve it, read on for hints and the answers.

Connections: Sports Edition is published by The Athletic, the subscription-based sports journalism site owned by The Times. It doesn’t appear in the NYT Games app, but it does in The Athletic’s own app. Or you can play it for free online.

Read more: NYT Connections: Sports Edition Puzzle Comes Out of Beta

Hints for today’s Connections: Sports Edition groups

Here are four hints for the groupings in today’s Connections: Sports Edition puzzle, ranked from the easiest yellow group to the tough (and sometimes bizarre) purple group.

Yellow group hint: Think World Cup roles.

Green group hint: Emerald City.

Blue group hint: Not named Tom, but close.

Purple group hint: Fore!

Answers for today’s Connections: Sports Edition groups

Yellow group: Soccer positions.

Green group: Seattle teams.

Blue group: Baseball Tims.

Purple group: Golf ____.

Read more: Wordle Cheat Sheet: Here Are the Most Popular Letters Used in English Words

What are today’s Connections: Sports Edition answers?

The yellow words in today’s Connections

The theme is soccer positions. The four answers are fullback, goalkeeper, midfielder and striker.

The green words in today’s Connections

The theme is Seattle teams. The four answers are Kraken, Reign, Seahawks and Storm.

The blue words in today’s Connections

The theme is baseball Tims. The four answers are  Lincecum, Raines, Salmon and Wakefield.

The purple words in today’s Connections

The theme is golf ____.  The four answers are course, polo, tee and umbrella.

After Sergi Bastardas’ decade at Amazon and floriculture startup Colvin, one thing always stood out  — the feeling that there wasn’t enough efficient “human infrastructure” to manage the workers behind the scenes. He took this feeling and, in 2025, alongside his co-founders Nacho Travesí and Antonio Melé, launched Orbio, an enterprise startup that helps businesses manage frontline workers — using AI agents, of course. 

On Monday, the company announced a $21 million Series A in a round led by Dawn Capital. The startup says its customers already include Poke and YUM! Brands (owners of Pizza Hut, Taco Bell, and KFC), to onboard and manage their frontline employees. Bastardas said customers are progressing from using Orbio in pilot to now fully deploying the software. As an example, he said that at behavioral health provider The Stepping Stones Group, Orbio now runs the company’s full US operation, with 20% more candidates making it through to get hired

The Orbio agents (Maria, Daniel, and Claire) can interview candidates, assess fit, monitor employee output, and conduct daily check-ins throughout an employee’s work lifecycle. The goal is to help businesses run their workforces autonomously, Bastardas said, adding that businesses will be able to engage and support the frontline workforces while also delegating some workforce operations to AI agents. 

“Each agent generates data that feeds back into the others: onboarding signals inform recruiting quality; exit interviews reveal why employees leave, which recalibrates hiring criteria; engagement data identifies retention risks,” he continued. 

Orbio competes with several startups — such as Paradox, which helps automate recruiting, and WorkJam, which helps manage frontline employees. 

Bastardas considers Orbio’s biggest competitor to be the legacy approach, however, to how frontline workers are managed (especially in industries like healthcare, retail, and logistics) — a fragmented process that sometimes still involves spreadsheets and phone calls. All of this is changing rapidly, however, in the age of AI. Orbio has raised $26 million in funding to date from investors, including Visionaries and 2100 Ventures. Bastardas said the fresh capital will be used to hire and develop more AI agents. 

“This will be [a] transformation for businesses, but also the workforce,” Bastardas said. “The 2.7 billion people who keep healthcare, retail, logistics, and hospitality running, most of whom don’t have a corporate email address, have previously got nothing. This is their AI moment.” 

Quordle was one of the original Wordle alternatives and is still going strong now more than 1,400 games later. It offers a genuine challenge, though, so read on if you need some Quordle hints today – or scroll down further for the answers.

Enjoy playing word games? You can also check out my NYT Connections today and NYT Strands today pages for hints and answers for those puzzles, while Marc’s Wordle today column covers the original viral word game.

Microsoft has fixed a known issue that caused Windows updates released since May 2025 to fail when installed via the Windows Update Standalone Installer (WUSA) from a network share.

WUSA is a built-in Windows command-line tool that helps admins install and uninstall Microsoft Standalone Update (.msu) files through the Windows Update Agent API to deploy or remove patches, updates, and hotfixes.

This known issue affects Windows 11 24H2/25H2 and Windows Server 2025 devices on enterprise networks, as WUSA isn’t a common method for installing updates on home devices. Microsoft also noted that the bug doesn’t occur with a single .msu file or when the files are stored locally.

“Windows updates installed using the Windows Update Standalone Installer (WUSA) might fail with error ERROR_BAD_PATHNAME, when the update is installed using WUSA or double-clicking a .msu file from a network share that contains multiple .msu files,” Microsoft said when it acknowledged the issue in August 2025.

“These issues might occur on devices that installed updates released May 28, 2025 (KB5058499) and later.”

Microsoft first mitigated this known issue automatically on home and non-managed business devices through a Known Issue Rollback Group Policy beginning September 2025.

Fixed in June 206 cumulative updates

As part of the June 2026 Patch Tuesday, Microsoft finally addressed this known issue for all affected systems in cumulative updates released for Windows 11 (KB5079391) and Windows Server 2025 (KB5094125).

“If you are using an update released before this date, and are experiencing this issue, you have the option to work around it by saving the .msu files locally on the device and install the update from this location,” Microsoft said in a Windows release health dashboard update.

“Also, if you’ve restarted Windows after installing an .msu file via WUSA, please wait 15 minutes or more before checking the Update History page in Settings. After this short delay, the Settings app should properly indicate if the update installed successfully.”

Microsoft resolved another issue in April 2025 preventing enterprise customers from installing the April 2025 security updates via Windows Server Update Services (WSUS), and an identical bug that caused the August 2025 Windows 11 updates to fail with 0x80240069 errors.

Earlier this week, Microsoft also warned customers that they may have issues installing the latest monthly updates on some Windows devices upgraded to Windows 11 24H2 or 25H2.

Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.

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Supply-chain attacks are usually discussed after they become visible: a malicious package, a compromised software update, a malicious extension, or a breach involving a trusted vendor. But before an incident reaches that stage, the early warning signs may look much less obvious.

In underground forums and marketplaces, supply-chain relevance does not always appear under a clear label. A post may not say “supply-chain attack” at all. It may advertise GitHub access, private repositories, source code, API keys, OAuth tokens, cloud credentials, CI/CD data, or a vendor-related leak.

The supply-chain risk comes from where that access sits and what trust relationships it touches.

A recent investigation by Flare researchers of underground posts show that while it is very hard to recognize it, there are often early warning signs in the underground for software supply-chain attacks even before they are published in public as incident reports.

What is a Software Supply-Chain Attack

A software supply-chain attack targets the trusted tools, vendors, software components, services, or processes an organization relies on, instead of attacking the organization directly. In software, this can include compromising a third-party provider, developer account, source-code repository, package registry, CI/CD pipeline, update mechanism, plugin, or SaaS integration.

The danger is that once attackers compromise something trusted inside the delivery chain, they may be able to reach downstream customers, users, or internal systems through legitimate-looking access, updates, code, or integrations.

When ordinary access becomes supply-chain relevant

One of the strongest examples observed by Flare researchers involved a post (see screenshot below) advertising GitHub-related access, including references to developer accounts, private repositories, access material, and source-code exposure.

On its own, this may look like a standard access sale. But GitHub access can be more than access to code. It may expose secrets, deployment scripts, package publishing logic, cloud credentials, internal documentation, and CI/CD workflows.

That is where the supply-chain angle begins.

If attackers gain access to a developer identity or private repository, they may be able to understand how software is built, which dependencies are used, where secrets are stored, and how updates are published. In some cases, that access can enable attacks against customers, downstream users, or other connected systems.

The Vercel incident in April 2026 is another useful example because it showed how a compromise involving a trusted third-party AI tool and OAuth-connected SaaS access can create a wider security concern (even when the affected company says sensitive customer data and source code were not accessed).

For analysts reviewing underground posts, the relevance is not the incident itself, which was already public, but the type of exposure it represents: trusted integrations, SaaS accounts, internal tools, environment variables, and developer platforms connected through permissions that can be abused if one link in the chain is compromised.

This is why underground posts mentioning OAuth access, SaaS tools, environment variables, or developer platforms deserve attention, even when the initial claim is limited or unverified.

Source code is not always just intellectual property

Flare researchers also reviewed posts involving alleged vendor data and source-code exposure, including claims around Sportradar AG that were later echoed in public reporting on the broader TeamPCP supply-chain campaign.

The Sportradar case was linked to a compromised Trivy scanner and included exposure of sensitive operational material such as database passwords, API key and secret pairs, Kafka credentials, and monitoring tokens.

That is what makes the case relevant beyond the immediate breach: this kind of data can reveal how a vendor’s systems are connected, which services and integrations are trusted, and which credentials may create risk for partners or customers.

In supply-chain investigations, those details matter because the most dangerous part of a leak is not always the stolen database itself, but the access paths and trusted relationships it exposes.

A similar point appears in public reporting around TeamPCP and Mistral AI. In May 2026, reports claimed that TeamPCP was selling hundreds of alleged Mistral AI repositories. Mistral disputed parts of the claim, but the case still illustrates why source-code theft should not be viewed only as an intellectual-property issue.

Repositories may include credentials, building logic, internal service names, deployment workflows, API documentation, or references to customers and integrations.

Even when leaked source code does not provide immediate production access, it can help attackers map the environment and identify future attack paths.

Package attacks show how access can scale

The same analytical lens applies to package ecosystem incidents. Public reporting on Shai-Hulud (a self-spreading npm supply-chain attack that stole developer secrets and infected trusted packages) showed how compromised npm maintainer accounts and malicious package updates could be used to steal credentials, harvest CI/CD secrets, and propagate across repositories.

The significance was not only the malicious code itself, but the way trusted package publishing mechanisms were abused.

Discussions around Shai-Hulud-style activity and supply-chain attack competition were also observed. These posts were less concrete as victim leads, but they are useful as threat context. They show that actors are watching public package compromise techniques and discussing how they may be reused, modified, or extended.

The LiteLLM supply-chain incident provides another recent example. Public reporting described unauthorized PyPI package publishes connected to a broader compromise path involving developer and CI/CD environments. Because LiteLLM is used as an AI gateway, the incident also shows how supply-chain risk is expanding into AI infrastructure and developer tooling.

Developer environments themselves are also becoming attractive targets. Recent reporting around malicious VS Code extensions showed how trusted development tools can become a route into repositories and credentials. Extensions, plugins, and AI coding tools often sit close to source code, terminals, tokens, and internal workflows, making them valuable even when they are not part of production infrastructure.

What defenders can take from this

The reviewed posts do not prove that every underground access sale is a supply-chain threat. They do show why security teams should ask better questions when they see posts involving source code, developer accounts, SaaS access, API keys, OAuth tokens, package ecosystems, or CI/CD material.

The key question is not only, “Was data leaked?” It is also, “Could this access affect how trusted software is built, deployed, updated, or integrated?”

For defenders, this means supply-chain monitoring should include more than vulnerability disclosures and package alerts. Organizations should watch for exposed developer credentials, GitHub and GitLab access, package registry tokens, leaked repositories, CI/CD secrets, cloud keys, OAuth grants, and claims involving important vendors or software providers.

The value of underground monitoring is in recognizing these early signals before they are framed as a full supply-chain incident. 

Learn more by signing up for our free trial.

Sponsored and written by Flare.

Apple’s WWDC went about as expected for 2026. While it’s a tiny bit disappointing, Siri AI turned out to be worth the wait, but with some painful app-based gaps.

WWDC 2026 was on Monday, and it was expected to be a bit of a slow one this time around. As predicted by the rumor mill and countless reports in the weeks ahead, it certainly wasn’t an event that shouted about many new features.

Actually, that’s not quite true. Apple did harp on about one big thing, and that was its whole artificial intelligence push.

Sorry. Apple Intelligence and Siri AI.

Before the event, we knew full well that the main beats would basically be Siri and AI stuff, and a bit of periphery. I even asked for there to be something that wasn’t AI to talk about.

Admittedly, Apple did bring up other things that weren’t AI. Except they were not things I could excitedly tell my mother about when explaining the significance of Apple’s launches.

Making things more reliable and responsive? That’s nice, but that’s also what we expect from updates throughout the year anyway.

There was more talk about parental controls and Screen Time updates. This again is great for parents, as now little Timmy can be protected from blood and gore as well as unsolicited nudes.

But neither are things that can be enthused about to friends or people curious about what happened. “Hey, you’ll like how you’ll see confirmation codes on your iPhone screen when you phone a business number” is neat, but not sexy enough to enthuse about.

Really, the only subject worth discussing is the whole AI thing.

Shockingly more intelligent

Apple went into a whole spiel about its artificial intelligence work, complete with handy circle-based graphics explaining the whole ecosystem. The whole thing of using Apple Foundation Models, a “System Orchestrator” connecting stuff together, and the “Systemwide Experiences” with Siri and Apps.

All very nice and showing in a public-friendly way how everything is interconnected and works as a giant whole, without explaining how. For Joe Public, that’s explanation enough.

An explanatory circle-in-circle explanation of Apple’s AI strategy. At least, it looks explanatory…

The main thing is that Apple’s two-year-late Siri upgrade has finally turned up. It’s late, but surprisingly, it wasn’t disappointing.

What Apple promoted heavily in 2024, the entire contextual-awareness thing, actually works properly. After reaching the end of the waitlist to use Siri AI, as well as the lengthy indexing process, I was able to ask some questions that could easily be troublesome for Old Siri.

The first one was simple. “Where was I born?”

Cue Siri coming up with the correct response of my hometown. What was unexpected was that the detail was picked up because I had a photo stored in iCloud of my passport for identity validation purposes.

Being greeted by the mugshot-like images of my passport and personal information as evidence of where it got the answer from was a bit of a shock.

A second query asking when I last went on vacation was similarly fast. This time, it used a combination of photographs and messages with my partner.

It understood we travelled to Rome in early April 2025, how long we were there for, and that we visited places like the Colosseum and the Roman Forum. Again, providing links to sources for context.

This is quite impressive, as is using the camera to photograph far-away slides in a presentation and asking Siri to summarize it all. The lengthy tiny print became something much easier to digest for my at-the-time tired brain and eyes.

Sure, we wanted it two years ago after Apple first demonstrated the idea. The two-year delay was made worse by seeing other AI firms developing quickly and leaving Siri in their dust.

That said, it was certainly worth the wait.

This is all something that you can enjoy in the fall if you’re not installing the developer betas. Unless you wait for the public betas, or don’t care about any risks to your data.

Except if you’re in the EU. That’s still a mess that Apple needs to clean up.

Massive blind spots

That said, there were some gaps in its capability. For example, it couldn’t generate a list of recent messages in Slack, nor could it work out what my mother last said in Facebook Messenger.

While my AppleInsider email is handled in the Mail app and could be accessed by Siri, my personal email in Gmail could not.

As impressive as Siri AI is, private third-party data sources are probably going to be the stumbling block. Unless apps like Gmail provide access to the data troves in some way, they will be areas that Siri won’t be able to use to help the user.

These data blind spots may be opened up by Google and other sources in the future. But there’s also the temptation to keep them locked off from Siri completely, if only to make their own AI services provide the same functionality.

Google Gemini already has the capability of accessing your Gmail email if you have a supported AI plan. There’s no incentive for Google to let Siri AI do the same and miss out on those consumer subscriptions.

The only bento box that mattered in WWDC 2026.

Money is always a factor in business decisions, and the massive potential of Siri could influence other big tech rivals not to play ball with Apple’s vision.

I may not be able to access communications from my personal email, but at least Siri can tell me what the lowest-calorie but highest-protein option available at my local KFC is for a post-gym workout.

That would be the grilled chicken salad. It was OK.

Last week’s Sunday Reboot discussed the inevitable Intel hardware and software support changes arriving this fall.

JeliLiam decided enough time had passed. The original Sonic Adventure 2 arrived on Dreamcast in 2001 and later reached GameCube players as Sonic Adventure 2 Battle. It delivered breakneck platforming, rival hedgehogs trading blows, and a story that actually mattered. Official channels never delivered a true modern version. So one dedicated creator started fresh in Unreal Engine 5 and called the result Sonic Adventure 2 Redux.

The project is accessible for free download on Game Jolt, but each new demo adds considerable content to the whole campaign. Demo 3 was only released a few days ago and appears to be the most complete yet. When people launch Demo 3, they are guided through three distinct stages that show how far the project has advanced. Pyramid Cave is a masterclass in atmosphere, with ancient stone halls and burning inscriptions lit in a dramatic way that changes as flames flicker and light bounces off the carved stone walls. Sky Rail pushes Sonic across elevated tracks high above the skies, and every boost or grind along the rails feels real. Meanwhile, Pumpkin Hill recreates the terrifying treasure hunt ambiance of the past, complete with floating platforms, concealed paths, and a sinister atmosphere that remains unsettling even in high definition.

Sale

LEGO Sonic The Hedgehog Team Sonic Command Truck Building Toy – Pretend Play Vehicle W/5 Figures for Boys…

• SONIC’S COMMAND CENTER – Gaming fans ages 8 years old and up can build the LEGO Sonic the Hedgehog Team Sonic Command Truck (77006) building set for…
• 5 LEGO SONIC FIGURES – Sonic, Tails, and Metal Sonic minifigures plus Burrobot and Caterkiller figures speed into action as kids stage heroic…
• LAUNCH & CHASE ACTION – Young gamers can deploy Tails on his ATV through the truck’s interactive launch feature while dodging attacks from the…

Plus, two new boss fights have been added to the mix. Hot Shot and Egg Golem require you to use your wits and timing in a different way than Big Foot or Shadow, but they retain the original essence while giving players more opportunities to employ speed and positioning. New animatic-style cutscenes have also been introduced to bookend certain sections and help tie the story together without requiring the full cinematic experience. The project now has enough story to tie everything together, making it feel more like a journey than a series of isolated stages.

Everything is built on the Azure foundation, a one-of-a-kind physics foundation that allows the main gameplay loop to shine through. This includes building speed through loops and ramps, chaining moves for a high score, and only activating the bounce bracelet or spindash when it feels right. At the same time, Azure Framework smoothes out all of the previous rough edges. Even when objects move quickly, the camera movement is smooth and steady. The controls are exactly what you’d expect from a modern game. Furthermore, the menus and on-screen text have been greatly cleaned up while maintaining their original flair.

For fans, the visuals are unquestionably the most impressive aspect of the remake. Each location has incredible detail on every surface. The stone at Pyramid Cave looks to have worn down over time. The metal in Sky Rail captures and reflects light in a very realistic way, while the character models are faithful to the original designs from 2001, but with considerably cleaner textures and smoother movements.
[Source]

This article is part of the collection: Teaching Tech: Navigating Learning and AI in the Industrial Revolution.

A fourth-grade teacher asked a simple question:

“What can I actually use this for in math?”

This teacher captured the broader moment in education. Over the past several years, schools have been urged to respond to the rapid emergence of generative AI tools such as ChatGPT with limited information and a lot of hype and horror stories. Some have framed the technology as potentially transformative for teaching and learning, while others claim the opposite. Yet in many classrooms, adoption has been slower and more selective than the surrounding hype might suggest.

That hesitation is often interpreted as resistance to innovation, but conversations with educators suggest a different interpretation. In many cases, teachers behave as experts in most fields do when encountering a new technology, evaluating whether it solves a real problem. When professionals encounter a tool that is widely marketed but still evolving, they ask a basic question: What does this actually help me do better?

For many educators, that question remains unresolved when it comes to classroom instruction, and that’s what our research project aimed to answer: What are teachers experiencing with generative AI in their classrooms?

In fall 2024, EdSurge researchers facilitated discussions between a group of 17 teachers from around the world. We convened a group of third to 12th grade teachers, and some of them designed and delivered their own lesson plans, either teaching with or about AI.

Overall, our participants’ responses reflect a few major themes, with the most prominent sentiment being an air of indifference. In particular, a fourth grade math teacher participant attempted to use generative AI in her instruction. However, before adoption, she asked how AI could help her elementary students learn math. Her question captured what several participants were thinking, aligning with 2024 data from the Pew Research Center that shows educators were split on whether student AI use was more harmful than helpful.

A Technology Arriving Faster Than Schools Can Unpack

A high school computer science teacher from Georgia describes her fears about generative AI’s widespread push into classrooms:

One of my biggest fears is actually Arthur C. Clarke’s rule: any sufficiently advanced technology is indistinguishable from magic…we have students, parents, and teachers looking at AI as if it’s magic.

A high school library media specialist from New York described the same tension from a different angle:

There’s a fear about not being able to keep up with how things progress…the new tools and the impact it has on education.

Schools typically adopt new technologies through deliberate cycles of experimentation, professional development and evaluation. Generative AI has entered classrooms through a different pathway. Consumer tools became available to teachers and students simultaneously, often before schools had developed policies or instructional frameworks for using them.

The result is a situation in which educators encounter the technology while they are still trying to understand its implications.

Where AI Is Already Providing Value

In conversations with teachers, the pattern that appears consistently is a classic user design case. The most immediate use cases for generative AI have little to do with student learning. Instead, an engineering and computer science teacher in New Jersey addressed workload:

I have a running discussion with some of my colleagues about how to use AI to lesson plan. I use it routinely to lesson plan. I don’t really use the lessons, but we have to produce all this stuff for admin that no one reads… AI will just roll it off.

Another teacher described similar experimentation among colleagues:

It’s really great that so many people have kind of scratched the surface and are using it to support their productivity and efficiency… lesson planning and newsletters and stuff like that.

These examples reflect a pattern seen across many professions: Generative AI is particularly effective at drafting, summarizing and generating text. In contexts where professionals face time pressure and administrative demands, those capabilities can be immediately useful.

Teachers experience those same pressures. Beyond instruction, many juggle grading, lesson planning, parent communication, extracurricular supervision and administrative reporting. In that environment, a chatbot that helps compress routine tasks can feel genuinely helpful.

Recent research, as well as national survey data from RAND’s American Educator Panels, suggests that teachers are adopting generative AI primarily as a productivity tool rather than a core instructional technology, a pattern that mirrors how educators in this study described their own early experimentation.

However, instructional discretion is different from a teacher’s administrative workload.

The Instructional Use Case Remains Unclear

When teachers consider introducing AI tools to students during class time, the calculations they make change. The relevant question becomes: What student learning problem does this tool solve? Many educators are still trying to answer this question, even after several years of exposure to generative AI in some capacity.

Some teachers are experimenting with AI in limited ways, such as using it as a revision partner in writing. A science teacher from Guam said:

Students write a first draft and then feed it into ChatGPT for a second draft… but I push them not to use it for research.

Others are designing lessons where the technology itself becomes the subject of inquiry. A high school special education teacher in New York shared how she removes the veil from the magic of chatbots.

We purposely trained [a chatbot] wrong, so students could understand the data is only as good as how and who trains it.

Learning science research suggests that students benefit most when technology supports reflection and revision, rather than replacing the productive struggle of critical thinking and problem solving, a principle that many teachers in this study have applied. In these cases, AI becomes a tool that students analyze and critique. The participants do not attribute AI as a source of authoritative knowledge.

AI Literacy as a Practical Classroom Entry Point

Many teachers see the most promising instructional opportunity in AI literacy, as it may feel most appropriate to teach students about the tools they’re hearing about and encountering daily. International guidance from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organisation for Economic Co-operation and Development (OECD) increasingly frames AI literacy as a foundational skill for students, encouraging schools to help young people understand how algorithmic systems generate information, rather than incorporating AI tools into everyday classroom tasks.

Students already live in environments shaped by algorithmically designed systems, from social media feeds to recommendation engines. Generative AI introduces another layer to that ecosystem.

An elementary teacher from New York state describes focusing on helping students understand how these systems produce information and where they fail:

For me it starts with literacy — [teaching] students how to prompt, and then how to fact-check the information that’s generated to make sure there’s no bias in it.

A middle school teacher from New York uses simple analogies to illustrate how machine learning systems work:

We used an exercise about making the best peanut butter and jelly sandwich. The ingredients were the dataset, the procedure was the algorithm, and the output depended on how it was designed.

These lessons treat AI less as a productivity tool and more as a window into how digital systems generate knowledge.

Hallucinations, Bias and the Question of Trust

Teachers also raised consistent concerns about the reliability of generative AI outputs. An elementary library media specialist from New York said:

You ask ChatGPT to write a paper on something and it makes something up totally imaginary.

To illustrate the risks, some educators point to real-world examples. A high school French teacher shared:

I tried ChatGPT. I think it’s very useful if you know your content very well. IIf you don’t know your content, it’s hard to tell whether or not it’s accurate.

Others connect these issues to broader discussions about algorithmic bias, explaining why they fear that students will become reliant on these tools. A high school computer science teacher in New Jersey shares her concerns about the increased use of AI by students. She works at a school with large populations of African American, Latino and Black newcomer families from African and Caribbean countries:

When we talk about bias, we look at hiring data and incarceration data… and facial recognition systems where error rates vary depending on who the system is trying to recognize.

In these contexts, AI becomes less a tool for answering questions and more a case study of how technological systems shape information.

The “Air of Indifference”

Taken together, these conversations reveal a stance that is not often captured in public discussions of AI in schools. What initially appeared to be an insignificant factor in keeping teachers interested in robust discussions about AI turned out to be a prominent theme aligned with both existing and emerging research.

By and large, teachers are not rejecting the technology. But they are also not reorganizing their classrooms around AI.

Instead, many are adopting a posture that might be described as pragmatic indifference:

“I use it for lesson planning… but I don’t really use the lessons.”

“I push students not to use it for research.”

In other words, teachers are using AI where it clearly saves time while maintaining boundaries around core learning tasks. This posture reflects professional judgment, rather than resistance to inevitable technological innovation.

Schools exist partly to create conditions in which students practice complex cognitive work, such as deep reading, methodical writing, reasoning through problems and evaluating evidence. If a tool primarily reduces the need to perform that work, teachers have reason to question whether it advances or undermines learning.

And that brings us back to the fourth-grade teacher’s question: What can I use this for with fourth-grade math?

If the instructional use case for AI remains unclear, what should students be learning instead?

That question leads to a deeper conversation about the kinds of skills that remain valuable even as technologies change.