A new AI tool called Codio Coach is making bold claims—it says students can improve their grades by 15% using a learning technique inspired by a 2400-year-old method (likely referencing Socratic questioning or similar classical approaches). But the author isn’t buying it, pointing out that the results might not hold up in real-world classrooms outside Codio’s controlled testing environment. Skeptics argue that without independent verification, the promise of an instant grade boost feels more like marketing hype than proven science.

Editor’s Note: Everyone wants an edge in learning, especially with AI tools flooding the market. But this story highlights a growing tension: flashy claims vs. real-world effectiveness. If AI tools can’t deliver outside a lab setting, students and educators might waste time (and money) on solutions that don’t actually help. It’s a reminder to approach AI-driven education promises with a healthy dose of skepticism—because better grades shouldn’t come from blind trust in algorithms.

Despite the rapid adoption of generative AI tools in European workplaces, a surprising number of businesses—nearly one in three—haven’t established clear, formal policies to govern their use. This gap leaves companies vulnerable to emerging risks, from misinformation spread by deepfakes to unchecked data privacy concerns. While AI is already embedded in daily workflows, the lack of guardrails could spell trouble down the line.

Editor’s Note: It’s not just about whether companies use AI—it’s about whether they’re using it responsibly. Without proper policies, businesses risk legal, ethical, and reputational blowback, especially as AI-generated content becomes harder to spot. This isn’t just a tech problem; it’s a wake-up call for leadership to catch up with the tools their teams are already relying on.

Google is expanding access to its AI tool, Gemini, to students under 18, aiming to integrate AI into classrooms worldwide. While the move promises advanced learning tools, it’s also sparking debates about how AI might reshape education—and whether it’s a help or a hurdle for students and teachers.

Editor’s Note: This isn’t just about another tech rollout—it’s a glimpse into the future of learning. AI like Gemini could personalize education or even lighten teachers’ workloads, but it also brings big questions: Will it deepen inequality? Could it replace critical thinking? The story matters because it’s part of a much larger conversation about how we prepare kids for a world where AI is everywhere.

A new study suggests AI could be a game-changer for overworked teachers, helping them craft better lessons and tailor materials for students with disabilities. But there’s a lingering question—will relying on AI tools come at the cost of students’ critical thinking skills?

Editor’s Note: Teachers are drowning in paperwork and planning, and if AI can lighten that load while improving accessibility, it’s worth paying attention to. But like any tool, it’s not a magic fix—schools will need to balance efficiency with fostering deep learning. This isn’t just about saving time; it’s about whether tech can actually make education better without unintended trade-offs.

AI might seem like a magic bullet for businesses, but jumping in without asking the right questions could backfire. At Snowflake Summit 2025, experts warned that while AI can level the playing field and boost efficiency, companies need to nail down basics like data quality, clear goals, and ethical considerations before placing their bets.

Think choosing between open and closed AI models is a simple either/or decision? Think again. This piece dives into why savvy enterprises are mixing both approaches to balance cost, security, and performance—because real-world AI strategy isn’t about ideology, it’s about what actually works for different tasks.

Companies investing in AI are facing a nasty surprise—hackers are exploiting vulnerabilities in AI systems to run costly, unauthorized operations (called "runtime attacks"), which spike cloud bills, wreck financial projections, and even put compliance efforts at risk. What was meant to be a money-saving tech upgrade is now burning budgets with little to show for it.

This article cuts through the hype around AI adoption, arguing that successful companies don’t just run flashy pilots—they focus on tying AI projects to measurable business value from day one. It’s about starting small but with ruthless practicality: clean data, clear governance, and proving ROI early to justify scaling up.

Big, flashy AI models like ChatGPT grabbed headlines, but companies are now realizing that smaller, more focused AI tools can be just as effective—and way cheaper. Think of it like swapping a Swiss Army knife for a simple, razor-sharp paring knife when all you need is to slice an apple. Businesses are saving millions by ditching the bloat and using leaner AI models tailored to specific tasks.