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A veteran jazz ensemble announced on Monday it was canceling its New Year’s Eve performances at the Kennedy Center, the latest group to withdraw from the Washington arts institution after it was renamed to include U.S. President Donald Trump. “Jazz was born from struggle and from a relentless insistence on freedom: freedom of thought, of expression, and of the full human voice. Some of us have been making this music for many decades, and that history still shapes us,” the Cookers jazz ensemble said in a statement. The Kennedy Center had promoted two New Year’s Eve performances by the Cookers as an “all-star jazz septet that will ignite the Terrace Theater stage with fire and soul.” Richard Grenell, a longtime ally of the U.S. president whom Trump named as the center’s president, said on Monday that such boycotts are a “form of derangement syndrome” and the cancelations are coming from artists booked by the institution’s previous leadership. He has previously termed cancelations a “political stunt.” The withdrawal adds to a growing list of cancellations since the name change was announced this month by the Center’s board, which the Republican president filled with allies during a broad takeover earlier this year. A Christmas Eve jazz concert was canceled last week, with the host of the show, musician Chuck Redd, attributing it to the name change. The New York Times reported that Doug Varone and Dancers, a New York dance company, has pulled out of two April performances. Democrats have called the decision by the board of the Kennedy Center to add Trump’s name to the institution illegal, while John F. Kennedys family denounced the move as undermining the slain president’s legacy. The board voted to rename the arts venue The Donald J. Trump and The John F. Kennedy Memorial Center for the Performing Arts, or Trump Kennedy Center for short. Trump has been eager to put his stamp on Washington and his name on buildings in his second term. His critics say he has compromised institutions by installing loyalists and making funding threats. Trump says he is tackling what he calls those institutions’ liberal bias. Kanishka Singh, Reuters
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E-Commerce
Artificial intelligence is reshaping the global workforce and rapidly expanding the expectations placed on todays learners. The World Economic Forum predicts that technological advancements like AI, alongside economic and demographic factors, will lead to a net increase of 78 million global jobs this decade. Educational institutions now face a pivotal moment. They must evolve how students learn, how instructors teach, and how technology supports each step of that journey. For decades, the education sector adopted new technologies cautiously. However, the profound impact of AI on the workforce has accelerated interest and experimentation. Our latest research at Cengage Group shows that both positive perceptions of AI and classroom usage are rising. While this enthusiasm is a promising step toward ensuring learners are prepared for an AI-forward future, its critical that institutions approach AI responsibly. With new AI tools launching at unprecedented speeds, it can be difficult to determine which will truly enhance learning outcomes. In some cases, rapid launches have created more friction for educators and confusion for students. To ensure responsible deployment, the conversation must shift from racing to market and instead toward measured, purposeful development aligned with how learning actually occurs. WELL-INTENTIONED, BUT MISSING THE MARK Many big tech companies have rushed to develop AI-based educational tools. But while tech innovators have made strides in exploring AI to enhance the educator and student experience, the critical reality is that education is an incredibly complex ecosystem. Education is simply not fit for plug-and-play solutions. Googles recent homework help feature is one example. Designed to give students an AI overview of what appeared on the screen including assessment answers, the tool inadvertently made it harder for instructors to validate work and accurately gauge understanding. Instead of reducing friction, it increased workload for both educators and students, ultimately leading to a pause in deployment. A similar challenge emerged this past summer with OpenAIs Study Mode. While designed to guide students and ask questions rather than provide answers, it is just one click away from ChatGPT, where answers are readily available. Without a deep understanding of teaching fundamentals, and how and when real learning happens, technological developments can lead to unintended consequences that disrupt rather than improve learning. These examples highlight an important truth. Innovation alone is not enough. Educational impact requires domain expertise, intentional design, and clear boundaries that promote understanding rather than shortcuts. BALANCE MEANINGFUL INNOVATION AND REINFORCE LEARNING To deliver educational support that blends innovation with learning outcomes, AI product development must balance the needs of both educators and students. Faculty are increasingly being asked to do more with less. AI should lighten that load, not add to it. For example, AI can surface classroom trends, flag areas where students are struggling, and help educators personalize instruction. Students, meanwhile, need support tools that build understanding, and dont just provide answers. Success in student deployment lies in cultivating curiosity and critical thinking. For example, AI can provide study support outside of classroom hours, deliver personalized feedback, and encourage further exploration to strengthen learning. This balanced approach requires maintaining human oversight. Collaboration with institutions and faculty ensures AI experiences align with course objectives and reinforce, rather than disrupt, proven teaching practices. THE PATH FORWARD: PRIORITIZE PEDAGOGY As AI continues to evolve, pedagogy must be at the core of all innovation, ensuring academic integrity and quality content that builds trust and drives meaningful student outcomes. Through controlled, confined subject knowledge and consistent training to ensure accuracy and academic integrity, AI tools can prioritize pedagogy and remain narrowly focused on driving specific student learning outcomes. AI should act as a supporting coach who helps break down problems, prompts curiosity, and encourages persistent learning so students can confidently reach the correct answer on their own. This purpose-built approach to AI complements the human teacher and enhances instruction by confirming student understanding and pinpointing knowledge gaps to support educators in delivering more personalized learning. The key to unlocking AIs potential in education goes beyond speed to market, and lies in thoughtful development rooted in intentional and responsible design. With pedagogy at the core, AI becomes more than a tool. It becomes a partner in improving learning outcomes for students and reducing the educators load. Darren Person is EVP and chief digital officer of Cengage Group.
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E-Commerce
When internet services platform Cloudflare suffered an outage in November, it took a big chunk of the online world down with it. Major platforms like ChatGPT, X, and Canva became unreachable. So did digital services offered by countless banks, retailers, and many other businesses. During the six-hour meltdown, as many as 2.4 billion users could have felt the impact. Software outages like this have always been and always will be part of online life. But today our systems are more interconnected than ever, so a single failure can ripple outward. AI only amplifies that risk. Yet, too many companies still lack protection against such disasters. In an era when outages are inevitable, theyre effectively operating without a safety net. The fundamental missing ingredient is something simple but easily overlooked: resilience testing. In a nutshell, resilience testing is all about pressure testing your software, before issues happen. It ensures that systems keep workingor quickly bounce backwhen things go wrong. Think of resilience testing as a small safety step to prevent big problems. The annual median cost of a high-impact IT outage is about $76 million. Businesses can also suffer reputational damage, lose customers, and get hit with regulatory penalties. Cloudflare is only one recent example. In the past year alone, AWS, Microsoft 365, and Starlink all went down, to name just a few. So why arent more businesses stress-testing their software for inevitable failure? Heres why, and what companies can do about it. MOST COMPANIES DONT BOTHER WITH RESILIENCE TESTING As high as the stakes are, businesses have reasons to avoid software resilience testing. The process is technical, and it can get messy. Modern resilience testing, also called chaos engineering, was put in the spotlight 15 years ago by Netflix software developers. Realizing that the only way to test for resilience is to simulate problems in the wild or in production, they created a suite of tools that replicated network crashes, cloud services meltdowns, and other real-world failures. Netflix might have been able to roll with the punches, but few other companies have the expertise or the stomach to compromise their systems like this. Its the equivalent of starting a controlled fire to ensure you have the resources to put it out. Resilience testing requires the technical acumen to know what failures to simulate for and responses to take. Putting these drills into action also entails risk, like triggering your homes fire sprinkler system which could ruin the furniture. Most importantly, developers need to know what to do when tests reveal weaknesses. Because the threshold for resilience testing is so high, it isnt integrated into most companies software development processes. Theres rarely a dedicated team, and often no one except maybe the CTO is clearly in charge. As a result, resilience testing becomes a bottleneck, so companies dont bother with it. A BETTER WAY FORWARD: HELP FROM AI The good news: It no longer has to be this way. For companies that want to adopt resilience testing, new platforms and toolspowered by AIare making the process safer and easier. Specialized resilience testing agents now enable companies to automate and optimize testing, without needing dedicated experts or teams. First, the AI agent identifies likely edge casesunusual or unexpected scenarios that could compromise reliability. It examines system behavior in production, how services interact, and where similar systems have previously failed. For example, the agent might highlight a scenario where a service slows, rather than fails outright. Another edge case: A code deployment updates only half the companys servers, leading to inconsistent user experiences. The agent then generates and prioritizes the test cases most likely to reveal resilience issues, explaining why each one matters. It can also set up and run those tests. After problems are identified, the AI agent suggests targeted fixes, making the software more resilient. With the heavy lifting completed, developers can review and apply those insights. WHY RESILIENCE TESTING NEEDS TO SHIFT LEFT Having the right tools is one thing, but effective resilience testing requires more than just software. Creating a culture of resilience is part of the solution. Software teams need to include testing in their routine. Ultimately, the only way to strengthen yourself against failures is to practice for them. If you never run those drills, you never know how bad things can get until its too late. Developers should also remember that resilience testing isnt just about full-scale, five-alarm outages. Its also about small, partial failures that create a poor user experience for customers, without necessarily taking the whole system down. Lets say a platform like Cloudflare has an issue affecting a major banks consumer app, leaving millions unable to check their balances. Resilience testing should anticipate this problem and provide a viable workaround. But the best way to encourage a culture of resilience is to shift leftmoving resilience testing to the software development preproduction phase, before code ever goes live. Shifting left helps teams catch weaknesses long before customers feel them. Thats crucial with todays complex, interconnected software systems, where seemingly minor issues can rapidly spiral into major outages. Rather than scramble to diagnose problems during live incidents, developers can uncover and fix them in a safe environment. Shifting left can save money and stress, too. Fixing resilience issues in production is costly and disruptive, often pulling team members away from other vital tasks. By taking a proactive approach, developers and business leaders can be more confident in the product they deliver to customers. Ultimately, resilience testing isnt rocket science. Companies that run fire drills for their software and embrace a culture of resilience testing will find themselves in a stronger position when the next disruption strikes. And in an increasingly interconnected world, where AI tools and features depend on more underlying services than ever, its safe to say that might be sooner rather than later. Jyoti Bansal is CEO of Harness.
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E-Commerce
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