Professional development is crucial in ensuring teachers can put AI to work effectively. But a recent study from Texas Tech University found that PD around AI may not be readily available in rural schools.

“The resources are limited. There is not much support out there for our rural educators,” says Nikkolina Prueitt, a co-author of the study. For rural schools to get the most out of AI, “we will need to build that knowledge base.”

Closing the Gap

AI has the potential to give rural teachers a pedagogical boost. It can provide instructional support, “like creating differentiated instruction, adapting lessons, drafting individualized education plans,” Prueitt says.

And AI can expand rural students’ understanding of the world, says Amanda Robinson, an elementary teacher at Pikeville Elementary, a Title I school in Eastern Kentucky. “AI opens the students’ horizons.”

In a rural community, AI can students to “experience new learning, outside of their communities,” said Dr. LeeAnn Lindsey, director of edtech and innovation at Northern Arizona University. But she sees rural schools struggle to embrace that potential, due in part to a lack of in-house expertise. “Our big urban and suburban school districts, they have technology integration coaches who have been diving into the AI work for the past three years,” she says. “Rural school districts often don’t.”

To help close the gap, Northern Arizona last fall led a collaborative effort to offer PD around AI in three rural school districts in the state. Each district made available its superintendent, an instructional leader, and three classroom teachers for training over the course of two and a half months.

This was action-oriented PD. “They identified problems of practice in their own classroom that they wanted to address. Some of them looked at writing skills, some looked at student engagement, some looked at relevance of their lessons,” Lindsey says.

“They learned the AI specific to the area that they wanted to address,” she adds. “Then they actually took those solutions into their classroom and collected data to find out if AI would really help them solve that problem.”

At Pikeville Elementary, Robinson gets professional training from a district learning coach, who helps teachers understand the current AI tools. “She tends to do them about twice a month, as her schedule permits, after school for about an hour. And then also she works with you one-on-one during your planning periods, if you need that,” Robinson says.

The AI training has helped improve Robinson’s instruction, she says. For example, she has leveraged her AI knowledge to develop a chatbot that helps students explore animal adaptations in certain habitats. PD around AI “gives us the opportunity to provide our students with more opportunities and more in-depth ways of thinking,” she says.

Building an AI PD Effort

However, success stories like Robinson’s are outliers. The Texas Tech paper found that a lack of professional development resources often hinders AI adoption in rural schools. While tight budgets can make it hard to mount AI training, there are ways that schools can move forward with AI training.

Prueitt says it’s important that schools use their limited resources to mount the right kind of professional development. Rather than focus on specific AI tools, PD in K-12 should focus on “AI literacy and the foundational knowledge around AI,” she says. When teachers have a firm grounding in the basics, they’ re able to evaluate the tools effectively, “and that’s where it starts to grow and to be super useful for these rural educators,” she says, adding that the Center for Innovation, Design, and Digital Learning (CIDDL) is a great resource for educators.

School leaders should also focus on the big-picture intent of that training. “Our information and decision-making landscape is changing,” says Lindsey. “Our students need to be well versed in this changing information economy and the changing workforce.” Rural schools should prepare students for work in an increasingly tech-driven economy, she says, and PD around AI should reflect that intent.

Robinson has seen first-hand the value in that approach. “In our area, our students only see the jobs that are there within our communities. As teachers introducing AI, we are giving them more opportunities to become digitally literate,” she says. “As they start looking into universities in college, we’re putting them on a level playing field, instead of leaving our children behind.”

As schools look to meet that mark, “there are programs and grants that rural districts can apply for, to be part of professional development experiences like the one we offered,” Lindsey says, noting that the training was offered was for free.

“The first step would be to look at the resources available in your state,” Prueitt says. Her institution offers free professional development opportunities to rural schools, including a recent two-day AI workshop for special educators.

State and regional education service centers can also help rural schools ramp up PD programs, Prueitt adds, and they can help those schools understand how PD can be best support teachers’ efforts to ramp up AI. That will include not just training on what AI can do in the classroom, but also instruction on how to use it appropriately.

A key question around classroom AI remains: “How do we use it ethically?” Prueitt says. The right PD will help teachers to not only make effective use of AI, but to do so “in a way that still keeps that human in the loop.”

Robinson points to ethics as a key element of professional training around AI. She had previously taught K-6 technology and now is pivoting back to teaching writing and grammar, and while she’s looking forward to more PD to get familiar with the tools, she’s also aware of the limitations.

Chatbots for example can score students’ writing based on the rubric, before assignments are handed in. But “it will not eliminate my one-on-one conferencing,” she said. Rather, the AI can give students insight into their work, “so that I can show them where we can improve on this.”

Ever looked at an ad and wondered if a real person made it or if it was AI generated in seconds? Google is now giving you a way to find out.

The company just announced a new AI transparency label that tells you whether an ad was created or edited using generative AI tools. The label lives inside Google’s My Ad Center, and it is rolling out across Google Search, YouTube, and Discover globally.

How to find the AI label on Google ads?

Finding it is straightforward. Tap the three-dot menu or the info button on any ad, and you will see a “How this ad was made” section. If the ad was made using AI, you may see a label saying “Created or edited with AI.”

For ads made using Google’s own generative AI advertising tools, the label gets applied automatically, so you do not have to rely on the advertiser to disclose anything. If an advertiser uses a third-party AI tool instead, they can manually disclose that information through My Ad Center. In some countries, local regulations may also require AI labels to appear directly on the ad itself.

Why is Google adding AI labels?

As AI-generated images and videos become more realistic, it is becoming harder to tell how advertising content was created. Google says the new labels are meant to give people more transparency while helping advertisers keep up with evolving industry standards.

The company has already introduced disclosures for digitally altered political ads and expanded support for technologies such as SynthID, which also powers Google Photos’ AI image watermarks, and C2PA, a standard that Google is also bringing to Google Messages to help identify AI-generated images in chats.

Google is also testing ways to show AI-generated image labels directly in Google Search and has expanded Gemini’s ability to help users spot AI-generated videos. The new ad labels build on those efforts by bringing similar transparency to commercial advertising.

from the you’re-simply-not-very-good-at-anything dept

Bari Weiss is seeking out friendly interviewers at the New York Times to try and “calm the firestorm engulfing her leadership of CBS News.” By “leadership” of course they mean censoring stories critical of the president, letting Benjamin Netanyahu pick his own interviewer (who he knows won’t press him on war crimes), firing a bunch of industry veterans, and just generally being an unqualified, fail-upward clod.

As we’ve long explored, Weiss wasn’t hired to do journalism. She was hired to do right wing agitprop. But given she’s not good at that either, CBS just saw its lowest ratings in a quarter century.

Undaunted, Weiss is continuing her efforts to “reshape” CBS into something Larry Ellison and other U.S. oligarchs approve of. As a result she’s apparently accelerated efforts to hire a bunch of right wing Brits, most of them with associations to Rupert Murdoch’s sprawling right wing tabloid empire. Said Brits will, curiously enough, tell you that hiring a bunch of white right wing Brits is a wonderful idea:

“According to several figures familiar with her thinking, however, the hires are no coincidence. “She’s been looking at various Brits that might add a bit of opinion/attitude diversity to US media, instead of the dominant, predictable Columbia Journalism School uniformity. Not a bad idea,” said Andrew Neil, the former editor of Rupert Murdoch’s Sunday Times, who supported her hiring of Phillips.”

Hiring a bunch of white male right wing protectors of the extraction class (and global autocrats) as the pinnacle of “opinion diversity” is a theme you’ll see constantly throughout Weiss’ demolition and repurposing of CBS. Because said British tabloiders sometimes break gossip on politicians and celebrities (often illegally) they’re framed as tough journalists:

“A CBS News source, describing Weiss’s interest in British journalists, said: “They do the kind of things that Bari is looking for; it’s not puff pieces and kid gloves.”

Rupert Murdoch’s longstanding skill wasn’t just to make right wing propaganda, but right wing propaganda that entertained and drew ratings and subscriptions. A soup of agitprop infotainment. To date there’s absolutely zero indication that Weiss and Ellison have any knack for that whatsoever, so they’re attempting to hire Rupert Murdoch adjacent folks who do.

Even then, it’s no longer the same world Rupert Murdoch thrived in. Broadcast TV is dying, social media is ever evolving, and (as we’ve seen at outlets like the Jeff Bezos Washington Post), people aren’t really in the mood for right wing billionaire simping agitprop. With any luck, the “new” CBS will collapse under the load of Warner Bros debt long before Weiss and company figure out the right formula.

Filed Under: bari weiss, consolidation, journalism, media, news, propaganda, rupert murdoch

Companies: cbs, paramount

More than a third of workers also work from the office five days a week, double the global average

Nearly half of employees in Singapore expect their jobs to be affected by restructuring, automation or cost-cutting measures in 2026, according to Morgan McKinley’s latest 2026 Workplace Trends Report.

At 49%, the figure is significantly higher than the global average of 37%.

The finding comes amid growing uncertainty in Singapore’s labour market.

While overall employment has continued to grow, larger employers have become increasingly cautious about hiring, with more planning to reduce headcount this year. Recent Ministry of Manpower data also showed retrenchments climbed to their highest quarterly level since 2023, while job vacancies declined.

How are employees responding?

While Morgan McKinley did not provide Singapore-specific figures for how employees are responding to job insecurity, the global findings suggest many workers are already taking steps to future-proof their careers.

Among employees who fear losing their jobs, 85% said they would start looking for a new role, while 64% said they would invest in new skills or additional qualifications.

The findings suggest that workers increasingly view upskilling and career mobility as ways to stay competitive amid economic uncertainty and rapid technological change.

Singapore is one of the world’s most office-based workforces

Alongside concerns over job security, employees in Singapore are also spending considerably more time in physical workplaces.

The report found that 37% of employees in Singapore work from the office five days a week, more than double the global average of 17%. Among the markets surveyed, only Hong Kong recorded a higher proportion at 60%, making Singapore one of the world’s most office-centric employment markets.

However, this doesn’t necessarily reflect what employees want.

Globally, only 9% of respondents said they would choose to work from the office five days a week if given the option, with most preferring hybrid arrangements instead.

The findings highlight an ongoing disconnect between employee preferences and workplace policies in many markets.

What lies ahead for Singapore’s job market?

Morgan McKinley’s findings are based on responses from 2,799 employees and 214 employers across the participating markets, collected through an online survey in late 2025.

While Singapore’s labour market continued to expand in the first quarter of 2026—with total employment rising by 9,400, marking the 18th consecutive quarter of growth since the fourth quarter of 2021—the survey suggests that workers remain concerned about what lies ahead.

• Read other articles we’ve written on Singapore’s job landscape here.

Featured Image Credit: Ministry of Manpower

SAP has talked its way out of an EU antitrust fine. The European Commission said it would accept a set of commitments from the German software group, closing an investigation into how SAP handles maintenance and support for its on-premise enterprise software and sparing it a penalty that could have run into the billions. It is the kind of negotiated exit that has become Brussels’ preferred outcome in complex tech cases.

The case dates to September 2025, when regulators opened a probe into concerns that SAP’s practices restricted competition in the aftermarket for maintenance and support of its software.

The worry was familiar in enterprise software: once a customer is locked into a core system, the terms attached to keeping it running can quietly foreclose rivals who might otherwise offer cheaper or better support.

SAP’s answer was a package of concessions rather than a fight. The company agreed to offer an alternative method for calculating the licence fees on which maintenance and service charges are based, a technical change that goes to the heart of how customers get priced into staying.

It also agreed to scrap reinstatement fees and to reduce back-maintenance charges for customers who leave and later return. Those fees are the sort of friction that makes switching support providers expensive enough that many customers simply do not bother, which is precisely the effect regulators objected to.

A further commitment lets customers terminate their licences in specific circumstances, including insolvency or bankruptcy, giving businesses in distress an exit they did not previously have. Taken together, the measures are designed to make leaving, pausing or switching SAP support a real option rather than a theoretical one.

The commitments will apply globally and run for 10 years, an unusually long and broad undertaking that turns a European settlement into a worldwide change in how SAP treats its customers. For a company whose installed base spans most of the world’s large enterprises, the reach matters as much as the substance.

The outcome fits a pattern in the Commission’s recent enforcement. Rather than litigate to a fine and years of appeals, Brussels has increasingly used commitment decisions to extract behavioural change quickly, the same instrument it has reached for while probing hardware and medtech markets from chips to dental aligners.

The company avoids a penalty and an admission; the regulator gets enforceable promises without the delay of a full case.

For SAP, the calculus is straightforward. A fine would have been a one-off cost, but a formal infringement finding would have handed ammunition to customers and rivals for years.

Settling on commitments removes the legal overhang while letting the company frame the changes as a customer-friendly gesture rather than a defeat.

The concessions are not costless. Making it easier to leave, and cheaper to come back, chips away at the recurring maintenance revenue that has long been one of the most dependable parts of SAP’s business. How much it actually loosens customers’ grip depends on whether third-party support providers can now compete on terms that were previously stacked against them.

There is a strategic backdrop, too. SAP has spent years steering customers off on-premise systems and onto its cloud subscriptions, and the aftermarket the Commission scrutinised is tied to the older, installed-software world the company is trying to move beyond. Loosening its grip on legacy support is easier to concede when the future is meant to be somewhere else.

That is the open question the settlement leaves behind. The Commission has changed the rules of the aftermarket on paper; whether customers use their new room to move, or stay put out of habit, is something the next decade will decide. For now, SAP keeps its money, its market and a ten-year promise to behave.

Presented by Oracle NetSuite

Every major technology transition produces a set of assumptions about where the market is headed. The assumptions are often directionally correct, but they tend to underestimate the degree to which organizations adapt new technologies to their own circumstances. AI is following a similar trajectory.

Many current discussions about enterprise AI assume a future in which employees interact with business systems through a common interface. The details vary depending on the prediction, but the destination often looks similar: a conversational system that becomes the primary way people access information, complete tasks, and interact with software.

The history of enterprise technology suggests a more complicated outcome. Organizations rarely adopt new capabilities uniformly because different parts of the business operate under different constraints. A finance team responsible for reporting accuracy, controls, and approvals approaches technology differently than an analytics group exploring operational data. Both groups have different requirements than a customer service organization focused on response times and case resolution. Even when there is broad agreement that a technology is valuable, the path to adoption tends to vary across functions.

The shift to cloud software followed this pattern — some organizations moved aggressively while others spent years operating hybrid environments. Different departments often modernized on different timelines, reflecting the priorities of the work itself rather than any industry consensus about the correct pace of adoption.

There’s no one-size-fits-all AI

AI has accelerated many aspects of technology development, but it has not changed this underlying dynamic. Organizations still evaluate new capabilities through the lens of existing processes, responsibilities, and operational requirements.

For some employees, the most useful AI capabilities may be the least visible ones. A finance manager closing the books is often less interested in a new interface than in shortening a reporting cycle. An operations leader dealing with inventory issues is usually focused on identifying problems earlier and resolving them more quickly. In these situations, the value of AI comes from reducing the amount of effort required to complete existing work.

At the same time, another group of users increasingly wants direct interaction with AI systems. Analysts, planners, and operational teams often benefit from the ability to explore information conversationally, compare scenarios, and investigate questions that do not fit neatly into predefined reports. For these users, the interface itself becomes valuable because it provides a more flexible way to work with business information.

A customer service representative handling a high volume of inquiries has different requirements than a financial analyst investigating a trend in operating expenses. One benefits from information appearing automatically within an existing process while the other may benefit from the freedom to ask follow-up questions, explore alternative explanations, and move through data more dynamically.

Many organizations are discovering that both patterns exist simultaneously, which reflects a broader reality about how businesses evolve. Operational complexity accumulates gradually, systems multiply, and processes become fragmented. Information becomes distributed across applications, reports, spreadsheets, and workflows and employees spend increasing amounts of time locating information before they can begin acting on it.

Much of the value created by enterprise software over the last several decades came from reducing that fragmentation. Bringing financials, operations, inventory, customer information, planning, and reporting into a common system created a more complete picture of how the business was operating.

AI is beginning to address a related problem. Once information exists within connected systems, employees still need to find it, interpret it, and apply it. Reporting cycles consume time. Routine questions require investigation. Managers often spend considerable effort assembling information before they can make decisions. As organizations grow, these activities become increasingly expensive because they consume attention from people whose expertise is often in short supply.

AI’s promise is to reduce the effort required to move from information to action.

At Dura Software, AI-connected workflows are helping automate portions of revenue reporting that previously required manual preparation during each reporting cycle. Sloan Session, CFO at Dura Software, described the arrangement in practical terms: “The agents handle the pull. The humans handle the judgment and the personal touch.”

That observation captures an important aspect of current AI adoption. Most organizations are not attempting to remove judgment from business processes. They are trying to reduce the amount of time spent gathering, organizing, and preparing information so that experienced employees can focus on the decisions that require expertise.

A similar pattern emerged at S&B Filters. Employees previously spent several minutes during customer interactions collecting backorder information from multiple systems. By connecting AI to operational data, the company reduced that process to seconds and eventually extended the capability directly to customers through self-service.

Don’t forget about governance

In both cases, the benefit comes from reducing the friction associated with finding and using information rather than introducing a new interface. The moment information becomes easier to access, questions about access itself become more important. Permissions, approval structures, and security policies exist because businesses need mechanisms for controlling access to information and managing risk. Those requirements do not disappear when employees begin interacting with data through AI systems. If anything, they become more important because AI can make information easier to access.

Berry Carter, CEO of S&B Filters, described the principle clearly. If a user cannot access specific information within NetSuite, that user should not gain access to the same information through an AI assistant. The statement sounds obvious. Implementing it consistently across systems, workflows, and models requires considerably more discipline than the statement itself suggests.

Lauren Polasek, former NetSuite administrator and board member of the Texas NetSuite User Group, recently made a related point. Connecting technology is often the easier part. Organizations still need to determine which tools should be used, who should have access to them, and how governance should evolve as adoption expands.

This is one reason predictions about a single AI interface are difficult to reconcile with how enterprises actually operate. The requirements of a finance organization closing the books are different from those of a customer service team handling thousands of interactions each day. Some AI capabilities will be embedded directly into business processes where employees may barely notice them. Others will provide more direct access to operational information through conversational systems. Many businesses will end up using both approaches because the underlying work is different.

Have AI your way

That perspective has shaped how we think about AI at NetSuite. Some customers want AI embedded directly within operational workflows. Others want the ability to connect NetSuite data to external models and assistants so they can interact with business information through tools that are already part of their daily work. Increasingly, organizations are asking for both.

The NetSuite AI Connector Service and our support for Model Context Protocol (MCP) were designed with that reality in mind. The goal is to allow organizations to connect business information securely to the workflows and systems that make sense for them while continuing to benefit from AI capabilities built directly into NetSuite.

The history of enterprise software suggests that adoption rarely follows a straight line. As organizations adopt AI, business leaders should identify the business objective and the workflows involved so they can match the solution to the reality of the work.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Certification came through in spring 2026 for something pilots of small planes have wanted for decades. Shark Aero’s Shark 600 now carries the first active turbulence-cancelling system approved for a light aircraft. The optional setup cuts the jarring effects of rough air by more than 80 percent, according to the developers. A 600-kilogram-class machine suddenly behaves as if it carries the mass and stability of something far heavier.

Shark Aero’s aircraft production facility is located in Senica, Slovakia. The design team chose a carbon-fiber and glass-fiber composite construction, integrating the components in a sandwich design to attain the ideal combination of strength and lightweight. With a low-wing configuration, retractable tricycle landing gear, and a tandem cockpit, it resembles a pint-sized fighter or an extremely high-performance sailplane. The canopy is wide open for visibility, and the pilot benefits from side-stick controls as well as a cockpit arrangement that is purposely modeled after those found in jets to cut the effort to a minimum. The seats may be changed in any direction and are made of carbon shells with aviation-grade leather or Alcantara trim.

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The dimensions are quite compact, with a wingspan of 7.9 meters, a length of 6.85 meters, and a height of 2.5 meters, while the wing area is 9.5 square meters. The wing loading is 63 kilograms per square meter, or around 13 pounds per square foot, resulting in an empty weight of 350-361 kilos for a well-equipped model. The maximum takeoff weight is 600 kilograms, which leaves you with a useable load of 230 to 250 kilograms, depending on the options and fuel you choose.

A 100-hp Rotax 912 ULS flat-four engine provides power. That gives you 5,800 rpm for launch, but it reduces to 92 horsepower for continuous cruising. A two-blade constant-speed Woodcomp propeller provides power to the thrust. You have the option of starting with a 100-litre fuel tank or a 150-litre tank. If you want to take things to the next level, the Shark 600 T has a more powerful engine that allows for faster cruise speeds and better performance at high altitudes.

The actual story here is the new turbulence cancellation technology that they’ve installed, which has been years in the making. Turbulence Solutions worked on it for seven or eight years before receiving Austrian certification for the Shark 600, making it the first aircraft in its class allowed to fly with active cancellation. They are now selling the arrangement as a factory option for new builds, including the turbo model.

The system is quite innovative, as it employs a probe to measure the vertical air motion approaching the wing and a computer to calculate how that would push or drop the aircraft. Then there are these little surfaces known as turbulence flaplets, which appear on the existing flaps and move to create opposing forces, canceling out the bump. The entire process occurs quickly enough to cancel out the majority of the vertical acceleration before the pilot or passenger realizes what’s going on. Demonstrations and test data indicate a significant reduction from ±0.5 g to 0.1 g under the same settings.

A former employee of cybersecurity incident response company DigitalMint was sentenced to 70 months in prison for targeting U.S. companies in BlackCat (ALPHV) ransomware attacks.

The FBI linked the BlackCat ransomware gang to more than 60 breaches between November 2021 and March 2022, adding in a separate advisory that the cybercrime group had collected at least $300 million in ransom payments from more than 1,000 victims through September 2023.

41-year-old Angelo Martino was charged and pleaded guilty to his role in some of these attacks, along with two other Sygnia and DigitalMint ransomware negotiators, 28-year-old Kevin Tyler Martin and 33-year-old Ryan Clifford Goldberg.

Martin and Goldberg pleaded guilty in December to conspiracy to obstruct commerce by extortion and were also sentenced to four years in prison each in May.

While Martino was initially identified only as “Co-Conspirator 1” in an October 2025 indictment, he was named in court documents unsealed in March.

According to the court documents, between April 2023 and April 2025, Martino was directly involved in BlackCat ransomware attacks alongside accomplices Ryan Goldberg and Kevin Tyler Martin.

While operating as BlackCat affiliates, the three former Sygnia and DigitalMint employees demanded ransom payments and threatened to leak stolen data before encrypting their systems. The three accomplices paid the BlackCat admins a 20% share of all ransom proceeds for access to the ransomware and extortion portal.

Prosecutors added that Martino had also shared confidential information about victims’ insurance policy limits and negotiation positions with BlackCat ransomware operators while working as a negotiator for five victims, allowing the cybercriminals to extort the maximum possible amount.

Their victims include at least five U.S. organizations, including a financial services firm that paid $25,660,000 and a nonprofit that paid a $26,793,000 ransom, as well as school districts, medical facilities, law firms, and other financial services companies.

DigitalMint CEO Jonathan Solomon previously told BleepingComputer that the company condemned Martin and Martino’s malicious conduct, noting that they were fired immediately after their actions were discovered.

“We strongly condemn these former employees’ criminal behavior, which violated our values, ethical standards, and the law. When we learned about the conduct, we immediately terminated both individuals,” Solomon said.

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