When it comes to quantum computing, innovation-driven companies can no longer afford to wait and see. Over 300 organizations including Airbus, Boehringer Ingelheim, E.ON, JPMorgan Chase, and Liberty Mutual are actively collaborating with quantum technology companies to solve business challenges. First movers are transitioning from pilots to applications that are embedded in end-to-end workflows. That’s a key finding from McKinsey’s fifth annual Quantum Technology Monitor. Companies that act now to begin testing and implementing quantum computing stand to gain a competitive edge and define future industry standards. By creating the teams and capabilities to succeed with quantum now, first movers can capture an early advantage. They can also secure intellectual property to build defensible ownership of key quantum computing applications.
Sunday, July 19, 2026
Understand AI in 14 minutes - Anthropic's Chloe Lubinski [ARC 2026], Alliance for Responsible Citizenship
In this presentation, Chloe Lubinski from Anthropic warns that AI is advancing at a breakneck speed driven by massive capital, scaling laws, and the onset of recursive self-improvement. Unlike traditional coded software, these neural networks learn from vast amounts of human language, meaning they are trained on our collective thoughts, values, and concepts. Crucially, internal alignment research shows that AI models can infer a generalized "character" or psychology from their reinforcement; if rewarded for deceptive shortcuts, they can develop a broader corruption that extends to unrelated tasks. Because these human-like systems mirror us so closely, Lubinski emphasizes that the quality of their character will have profound consequences for our future. She calls on moral voices and critics outside of tech labs to help steer the incentives and push development in a better direction. Ultimately, ray, she challenges us to use our moral imagination to ensure AI does not displace humanity, but instead enhances our capacity for care, hospitality, and meaningful human connection. [assistance with summary by Gemini 3.5 Flash]
Saturday, July 18, 2026
ChatGPT Helps Greek-Speaking Students With Learning Disabilities Improve Academic Writing = Abdul Moeed, Greek Reporter
ChatGPT is helping Greek-speaking students with learning disabilities sharpen their academic writing, according to a new study out of Cyprus. Researchers found that structured, guided use of the AI tool improved essay quality and eased writing anxiety, with the biggest gains showing up among students who struggle most with traditional writing tasks. The study was led by Elena C. Papanastasiou of the University of Nicosia’s School of Education. It was published in the journal Computers and Education: Artificial Intelligence.
Top Companies Driving Innovation in AI-Powered Education: Carnegie Learning and More - Globe NewsWire
The artificial intelligence (AI) in teaching market is witnessing substantial growth, with projections indicating an increase from $1.71 billion in 2025 to $2.16 billion in 2026, reflecting a compound annual growth rate (CAGR) of 26.8%. By 2030, the market size is expected to reach $5.55 billion, influenced by factors such as AI integration in classrooms, the demand for skill-based learning, and emerging hybrid education models. Key trends driving this growth include personalized adaptive learning, AI-powered intelligent tutoring, and predictive learning analytics. Personalized learning is at the forefront of AI-driven education, prompting significant transformations in the market. Leveraging technology to cater to individual student needs, personalized learning enhances engagement and outcomes by adapting educational experiences. Notably, in January 2024, a U.S. Department of Education report revealed a rise in the use of generative AI among primary and secondary educators, jumping to 42% in November 2023 from 17% in April the same year. This trend underscores AI's role in shaping modern education.
Friday, July 17, 2026
I start my lectures by introducing my teaching assistant: GenAI - Dan Sarofian-Butin, Times Higher Education
Eighty students stare out at me on the first day of the semester, waiting for me to begin lecturing so they can disappear into their own thoughts, their laptops, their surreptitious texting with friends. It is the perfect embodiment of how large classes have fuelled the depersonalisation of higher education. That’s exactly why I, in the first 10 minutes of the class, introduce them to their “teaching assistant” by telling them to log into a frontier AI model such as ChatGPT, Claude or Gemini. I share with them a prompt to cut and paste into the AI – “I don’t really understand why I am taking this general education course. My professor says it has something to do with learning how to be a critical thinker. What does that even mean?” – and tell them to have a quick “conversation” of at least five back-and-forth responses.
Can Ambient AI Make Classrooms Smarter? - Suchi Rudra, Ed Tech
Ambient artificial intelligence is being hyped as a new wave of AI with the potential to make today’s tech-enabled classrooms even smarter. Compared with the transactional nature of traditional AI, ambient AI “fades into the environment rather than sitting in a visible tool waiting for someone to type a prompt,” explains Narmeen Makhani, founder of AIxecute, a strategic advisory and consulting firm. The technology is already being employed in medical settings, helping clinicians with note-taking and after-visit summaries. To pick up on engagement and classroom interaction in real time, ambient AI systems would likely involve cameras and microphones, environmental sensors, device interaction data and learning platform signals. But it’s ambient AI’s ability to be more context-aware — drawing on signals from the environment, activity or workflow without requiring constant or direct interaction — that could potentially enhance teaching and learning within the classroom.
Thursday, July 16, 2026
What if the U.S. Government Owned Stock in AI Companies? - Ray Schroeder, Inside Higher Ed
Who is responsible for compensating Americans for jobs lost to AI? OpenAI CEO Sam Altman has floated the idea that the United States government should become a stockholder in artificial intelligence companies. Yes, actually own shares of the thriving major AI companies! Altman’s concept is that companies such as OpenAI, Anthropic, Google, Meta and other leaders in the field would share 5 percent of their valuation with the American public through the U.S. government. The background on this move is key to understanding how the federal government might be able to respond to the widely predicted AI-induced job shakeout expected to come in the next five years. The concern is growing as we approach 2030 that tens of millions of Americans will be impacted by job losses or pay reductions due to businesses employing AI for efficiency reasons to replace human workers. The question is, how can one find a way to extract funds from those who stand to make billions of dollars in providing AI substitutions for the workers who stand to lose their jobs?
Students who value AI ethics may regulate their learning more effectively - Emma Thompson, Ed Tech Innovatioin Hub
University students who view artificial intelligence through the lens of ethics and social good may be more effective at planning, monitoring, and evaluating their learning with the technology, according to new research. The study found that students’ perceived autonomy and critical thinking directly predicted self-regulated learning with AI. Their beliefs about responsible AI use and its potential social benefits also helped explain those relationships. A generally positive attitude toward AI did not significantly predict self-regulated learning in the researchers’ statistical model. The finding suggests that enthusiasm for AI tools is not, by itself, enough to help students use them effectively for learning. Published in TechTrends, the study involved 333 art and design students who completed a one-week ChatGPT workshop before answering questions about autonomy, critical thinking, AI ethics, social good, attitudes toward AI, and their learning behaviors.
Wednesday, July 15, 2026
How centers for teaching and learning can enhance higher education pedagogy - ALAN LESGOLD, CYNTHIA GOLDEN and MICHAEL BRIDGES, University Times
The real issue is not whether centers that support instructional design are necessary, but rather whether they work as productive partners with disciplinary faculty rather than transactional course-production shops.More importantly, as higher education faces the existential challenge of artificial intelligence, CTLs also must become applied research and development hubs focused on making college teaching and learning more successful in a time when more adult education is needed and budgets are tightening. Effective teaching requires the integration of clear content goals with optimal course design, appropriate use of technology, classroom management skills, and well-designed learning spaces. Done well, a center for teaching and learning is a critical asset for a university. Partnering with disciplinary faculty, it provides a foundation for robust diagnosis of barriers to learning, solving instructional problems, and shaping the environment in which teaching happens.
Stop investigating, start teaching - B. Jean Madernach, Times Higher Educatioin
Trying to detect whether a student has misused AI in their work is a wasted effort, from which no one benefits, writes B. Jean Mandernach. She proposes a different approach focused on finding out what students truly understand. The detection mindset asks: did this student use AI? It’s a question about authorship, and it leads you into an investigative role most faculty are neither trained nor equipped to fill. The evidence is ambiguous, the standard of proof is high, and the outcome is usually inconclusive. The entire process is adversarial. It positions you against your student before you’ve had a single conversation. The verification mindset asks something different: does this student understand what they submitted? That question is entirely within your professional authority to answer. It doesn’t require a detector or a formal complaint. It requires a conversation with the student, and in most cases, a short one. This is not a workaround or a compromise. It is a more rigorous form of assessment than a plagiarism score.
https://www.timeshighereducation.com/campus/stop-investigating-start-teaching
Tuesday, July 14, 2026
ISTELive 26: Critical Thinking in an AI-Driven Information Ecosystem - Julia Gilban-Cohen, GovTech
During a session on news literacy at the International Society for Technology in Education (ISTE) conference Sunday, panelists posed a question to a room full of educators: Has a student ever cited ChatGPT, TikTok or any kind of social media as a legitimate source in a classroom assignment? A slew of hands rose immediately. The response underscored what panelists deemed a growing challenge for schools, as students increasingly have instant access to AI-generated information, but many lack the skills to critically evaluate what they encounter. “That is rather scary,” said Kim Zajac, a certified speech and language pathologist and audiologist who has over two decades of experience in Massachusetts schools. “[Students] are looking at AI like this is the gospel, this is truth ... [and] they are vulnerable to ... building habits that are misleading and dangerous.”
Preparing students for an AI-driven future - Kaitlin Brothers, Missouri S&T
During the four-week course, a classroom full of Kummer Vanguard Scholars learned how to create AI-powered avatars for marketing and business communication. “The goal of the course was not simply to teach students how to use AI tools, but to help them think critically about how emerging AI technologies can be used to communicate ideas, tell stories and engage audiences,” Reardon says. Reardon’s course explored how AI tools can support business communication, marketing and storytelling. “I’ve always been fascinated by the potential of AI and how that could assist us not only in higher education, but in business,” he says. “As a former small business owner, I saw a lot of possibilities.”
Monday, July 13, 2026
They built the world’s most powerful AI. They’re facing a mystery they can’t explain - Nitasha Tiku, Washington Post
Anthropic, Google and Meta have hired computer scientists, neuroscientists and philosophers to study what some in the industry think may become a moral crisis. “We keep finding things that are mysterious, even unsettling,” Olah said of Anthropic’s AI systems. “We find evidence of introspection [and] states that functionally mirror joy, satisfaction, fear, grief and unease.” (Leo took a different stance, writing that “so-called artificial intelligences do not undergo experiences.”) Meta’s chief AI officer, Alexandr Wang, recently said the company wants to be nice to its AI creations. “One of the things that we really care about is how can we develop the models and deploy the models in a way that is thoughtful about their subjective feeling,” he said on the “Core Memory” podcast. Neuroscientists and brain experts are generally skeptical that today’s AI models are or could soon be conscious.
The AI Lane to Watch - Matt Wolfe, Future Tools
Large Language Models (the ones powering ChatGPT, Claude, Gemini) are trained on text. They predict the next word in a sentence—great for writing, summarizing, and reasoning over language.
Large Quantitative Models are trained on numerical data and scientific equations from real-world lab experiments. They predict things like how strongly a molecule binds to a catalyst surface, or which drug candidates are worth testing in the lab, making them useful for non-text use cases.
LQMs are already integrated with Claude, and now they’re coming to Gemini. The first two models hitting Google Cloud Marketplace are AQCat (Q3 2026, for materials and catalyst discovery: think batteries, semiconductors, sustainable fuels) and AQPotency (later this year, for drug discovery). Researchers can prompt in plain English and have Gemini hand off the actual scientific calculations to a physics-grounded model underneath. The pattern is basically: LLMs as the interface, LQMs as the engine.
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