Educational Technology and Change Journal

On March 18, 2026, Director of National Intelligence Tulsi Gabbard delivered opening remarks at a Senate Select Committee on Intelligence (SSCI) hearing for the Annual Threat Assessment of the U.S. Intelligence Community. The opening statement¹ as delivered is below.

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By Jim Shimabukuro (assisted by Perplexity)
Editor

Jensen Huang’s GTC2026 keynote framed “physical AI” and robotics not as a side bet but as the next multi‑trillion‑dollar wave of the AI economy, continuous with today’s datacenters rather than a separate field.^1,4 In both NVIDIA’s own recap and detailed press coverage, he cast robots, autonomous vehicles, and industrial automation as the natural endpoint of an “AI factory” stack where gigawatt‑scale infrastructure produces models that flow into embodied systems, arguing that the next gold rush after digital agents will be robots and other “physical AI” burning even more data and compute.^3,4,6 This is less about a new technical thesis than a macro‑industrial one: embodied AI is presented as an infrastructure market similar in scale and inevitability to cloud and GPUs, with NVIDIA positioning itself as the full‑stack vendor from energy to humanoid controllers. In that sense, Huang’s message differs from classic robotics talks by making physical AI primarily an inference and datacenter story, with robots as endpoints of a vertically integrated pipeline rather than standalone machines.^3,4

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

AI inference chips sit at the center of a major shift in how artificial intelligence is actually used—and that shift explains why they dominated Jensen Huang’s keynote at NVIDIA’s GTC2026 and why they now anchor the company’s strategy.

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By Jim Shimabukuro (assisted by Claude)
Editor

[Related: “The Emerging AI‑First University Paradigm“]

“The Emerging AI‑First University Paradigm” (ETC Journal, 16 March 2026) makes a compelling case that Unity Environmental University, Ohio State University, the University of Washington, CUNY, and SUNY collectively sketch a new “AI-first” template for higher education — one in which AI is treated as a design principle rather than a peripheral tool, structures are reconfigured around AI’s capabilities, and ethics and equity are foregrounded as conditions of scale.¹ The five institutions do represent a meaningful advance beyond the typical university’s reactive, policy-memo approach to generative AI. Yet, when measured against what Thomas Kuhn understood as a genuine paradigm shift — a revolutionary displacement of the organizing assumptions, methods, and purposes of an entire field — these examples fall well short. They represent, rather, an intensification of one pole within the existing paradigm: the adoption-and-adaptation pole. The deeper anomaly AI poses to higher education — the radical destabilization of what universities are for, and of the three founding pillars on which they rest — remains largely unaddressed.

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By Jim Shimabukuro (assisted by Copilot)
Editor

[Related: Do Current ‘AI-First’ Universities Represent a True Paradigm Shift?]

Unity Environmental University, Ohio State University, University of Washington, City University of New York, and the State University of New York, taken together, sketch the salient features of an emerging AI‑first university paradigm. First, AI is treated as a design principle and strategic core, not a peripheral technology: Unity codifies AI‑First Design Principles,¹ Ohio State builds an AI‑first educational environment,³ UW adopts an AI‑first institutional strategy,⁸ CUNY envisions human‑AI powered education,⁹ and SUNY embeds AI into system‑wide policy and infrastructure.¹¹ Second, AI‑first universities reconfigure structures—degrees, faculty hiring, governance, and system‑level coordination—around AI’s capabilities and risks, rather than trying to fit AI into legacy forms.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

OpenClaw is a relatively new example of what researchers and developers call agentic AI—software that does not simply respond to prompts but can observe, reason, and act autonomously on a user’s behalf. The project began in late 2025 as an open-source experiment by Austrian developer Peter Steinberger and quickly grew into one of the most visible autonomous-agent frameworks in 2026.¹ OpenClaw is distributed under an MIT open-source license and is designed to run locally on a user’s computer while connecting to external large language models such as GPT, Claude, or open-source models.¹

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Conference passes have sold out, but you can still participate in person with an Exhibits Only pass (use code GTC26-20 for 20% off) or virtually [free].

NVIDIA GTC is the premier global AI conference, where developers, researchers, and business leaders come together to explore the next wave of AI innovation. From physical AI and AI factories to agentic AI and inference, GTC 2026 will showcase the breakthroughs shaping every industry. The conference venues are spread throughout downtown San Jose. For inspiring sessions, be part of the unique GTC experience. 

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By Jim Shimabukuro (assisted by Claude)
Editor

[Related: AI-Native Operating Systems: From Procedural to Intent-Based to Ambient]

The ETC Journal article “AI-Native Operating Systems: From Procedural to Intent-Based to Ambient” (13 March 2026) opens with a brisk diagnosis of where personal computing has been stuck: for three decades, users have had to navigate windows, files, and menus, actively directing machines step by step. The article argues that a growing number of technologists now believe the operating system itself may be on the verge of a fundamental transformation — one in which AI agents interpret human intentions and orchestrate digital actions automatically, rather than passively organizing applications and hardware as they do today. What the article calls the third and most radical pathway — ambient computing — is the destination where this trajectory ultimately leads: a world in which the operating system dissolves into a distributed intelligence layer spanning multiple devices and cloud services, and a person’s AI assistant manages communications, schedules events, and retrieves information regardless of which device is currently being used.¹ The following four articles expand on the idea of ambient computing.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

For more than three decades, the personal-computer operating system has been dominated by a familiar paradigm: the graphical desktop. Systems such as Microsoft Windows and macOS organize computing around icons, windows, files, and applications. The user launches programs, manipulates menus, and manually coordinates tasks between software tools. Beneath this interface, the operating system manages memory, hardware resources, and processes, but the overall architecture remains rooted in a conceptual model that dates to the late twentieth century. That model assumes that humans must actively direct computers step by step, selecting applications and instructing them how to perform tasks.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

The idea of an AI-driven tax system that eliminates the need for individuals to file returns is not merely speculative; it is actively being explored by governments, researchers, and private companies. However, as of 2026, most efforts are focused on partial automation—automating compliance, enforcement, and preparation—rather than replacing the entire filing structure. Tax administrations around the world have been integrating machine learning and advanced analytics into their operations, primarily to detect fraud, streamline workflows, and improve taxpayer services. An OECD survey found that 29 of 38 member countries already deploy AI in their tax administrations, using it to identify patterns of tax evasion, automate routine case processing, and differentiate simple filings that can be handled automatically from complex cases requiring human judgment.¹ These deployments represent the early infrastructure of a future system in which tax authorities already possess most of the necessary data and can pre-compute liabilities without taxpayers filling out forms themselves.

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By Jim Shimabukuro (assisted by Copilot)
Editor

Autonomous driving in early 2026 sits in a strange middle ground—no longer a sci‑fi promise, but still far from ubiquitous. In multiple U.S. and Chinese cities, you can already hail a driverless robotaxi or see a Class 8 truck moving freight with no one in the cab, yet these services remain tightly geofenced, heavily supervised, and politically fragile. Waymo now delivers on the order of 250,000 paid robotaxi rides per week across several U.S. cities, making it the clear U.S. leader in commercial Level 4 robotaxis, while global weekly robotaxi rides have climbed into the hundreds of thousands according to industry surveys tracking more than 700,000 fully autonomous rides per week worldwide.^1,2,3 In parallel, China’s Baidu Apollo Go has matched or exceeded Waymo’s scale, also reaching roughly 250,000 weekly rides and more than 140 million driverless miles, underscoring how quickly Chinese robotaxi operators have moved under more centralized regulatory regimes.^4,5

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By Jim Shimabukuro (assisted by Claude)
Editor

The history of technology is not written primarily by the powerful. It is written by the restless. Steve Jobs and Steve Wozniak assembled the Apple I in a California garage. Bill Gates and Paul Allen wrote their BASIC interpreter in a college dorm room before any computer existed to run it. The disruptors of every technological era tend to arrive from the margins — not because the center is incompetent, but because the center is invested in the status quo. They cannot afford to imagine the world differently. The outsiders can.

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By Jim Shimabukuro (assisted by Copilot)
Editor

The Russia‑Ukraine war has underlined that national resilience and societal will can matter as much as raw military power, and that lesson should sit at the center of any thinking about a potential US/Israel‑Iran war. Ukraine’s ability to mobilize its population, maintain governance under fire, disperse critical infrastructure, and keep basic services functioning has repeatedly blunted Russian objectives and bought time for diplomacy and external support.¹ In a US/Israel‑Iran context, that translates into prioritizing civilian preparedness, continuity of government, and rapid repair capabilities not only in Israel but across the wider region, including partners in the Gulf and beyond, so that societies can absorb shocks without collapsing into chaos. This matters for the “greater good” because wars that shatter basic social systems tend to radicalize populations, prolong grievances, and make any eventual peace far more fragile.

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By Jim Shimabukuro (assisted by Claude)
Editor

This is no longer a hypothetical scenario. On February 28, 2026, the United States and Israel launched joint airstrikes on Iran, killing Supreme Leader Ali Khamenei. The stated goals are to destroy Iran’s missile and military capabilities, prevent the state from obtaining a nuclear weapon, and ultimately to achieve regime change by bringing the Iranian opposition to power.² In response, Iranian forces launched missiles and armed drones against Israel and US military facilities in all six Gulf Cooperation Council countries.⁶ The opening of this war, which the US calls “Operation Epic Fury,” has been swift and devastating — but the far more dangerous question is what comes next. The conditions for a prolonged, grinding standoff comparable to the Russia-Ukraine war are alarmingly present.

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By Jim Shimabukuro (assisted by Perplexity)
Editor

Bresnick, Probasco, and McFaul’s core thesis in “China’s AI Arsenal: The PLA’s Tech Strategy Is Working” (2 March 2026) is that the People’s Liberation Army has moved beyond aspirational rhetoric about “intelligentized warfare” and is now systematically translating AI ambitions into concrete capabilities across command-and-control, sensing, targeting, and unmanned systems, in ways that are beginning to work at scale and that the United States has not yet fully internalized in its own strategy.¹ This argument builds directly on their recent empirical mapping of more than 9,000 AI-related PLA requests for proposals and nearly 3,000 AI-related defense contract awards between 2023 and 2024, which reveal a broad, coherent, and rapidly growing demand signal for AI in every warfighting domain.^2,3

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By Jim Shimabukuro (assisted by Claude)
Editor

The transformation of university pedagogy that agentic AI demands is perhaps the most visible and immediate of the three domains, and it begins with a fundamental rethinking of what learning is supposed to produce. Commentators inside higher education have described the emerging shift as the move “from generative assistant to autonomous agent,” emphasizing that generative models will increasingly sit behind agentic layers that decide when and how to use them.¹ This means that course designs built around the submission of finished products — essays, problem sets, take-home exams — are structurally vulnerable in ways that syllabi policies cannot patch.

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By Jim Shimabukuro (assisted by Copilot)
Editor

Agentic AI in higher education is in a visible but early, uneven phase: it is talked about as “the next evolution” beyond prompt‑driven generative tools, yet most campuses still treat it as a set of pilots and thought experiments rather than core infrastructure. A widely used working definition frames agentic AI as systems that can pursue complex, often long‑horizon goals with minimal human intervention, planning multi‑step actions, using tools, maintaining memory, and adapting to changing contexts—what some researchers call a “qualitative leap” from static chatbots and rule engines.¹ In practice, this means moving from “AI that answers” to “AI that acts”: agents that can orchestrate tasks across learning platforms, student information systems, and communication channels, rather than simply generating text on demand. Commentators inside higher ed have started to describe this shift as the move “from generative assistant to autonomous agent,” emphasizing that generative models will increasingly sit behind agentic layers that decide when and how to use them.⁶

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By Jim Shimabukuro (assisted by Claude)
Editor

[Related: Operation Epic Fury: The Official Rationale, 4 March 2026]

1. The Attack Violated the UN Charter and International Law

The most foundational and broadly shared criticism of Operation Epic Fury is that it constitutes an illegal use of force under international law. Article 2(4) of the United Nations Charter prohibits the threat or use of force against the territorial integrity or political independence of any state. Two exceptions exist: Security Council authorization under Chapter VII, and individual or collective self-defense in response to an armed attack under Article 51. Neither applies here. The Security Council did not authorize the use of force against Iran. The United States did not request such authorization. Iran was not attacking the United States or Israel at the time of the strikes.

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By Jim Shimabukuro (assisted by Claude)
Editor

[Related: Five Strongest Criticisms Against the U.S.-Israeli Attack on Iran, 4 March 2026]

1. Rationale behind Operation Epic Fury

On February 28, 2026, the United States and Israel began conducting extensive strikes against a wide range of targets in Iran. The strikes were dubbed Operation Epic Fury by the United States and Operation Roaring Lion by Israel. President Trump announced the operation in an eight-minute video posted to Truth Social around 2:30 a.m. ET, saying the United States had begun “major combat operations in Iran.” There was no address to the media or public briefing to Congress beyond a notification to the Gang of Eight shortly before strikes commenced. The video concluded with a direct message from Trump to Iranians, stating “the hour of your freedom is at hand.” (White House | CSIS | The Hilltop)

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By Jim Shimabukuro (assisted by Copilot)
Editor

The publicly available record since 28 February 2026 shows that drones played a central, visible role in the opening waves of the joint U.S.–Israeli strikes on Iran, while the role of artificial intelligence is more indirect and mostly inferred from the types of systems and operations described. The Institute for the Study of War’s running assessment of the campaign notes that U.S. and Israeli forces launched a broad strike effort aimed at Iranian leadership, air defenses, missile and drone infrastructure, and command-and-control nodes, implying a heavy reliance on networked surveillance, targeting, and battle management systems that almost certainly incorporate AI-enabled data fusion and decision-support, even if officials do not label them as such in public statements.¹ Chatham House’s early expert commentary similarly frames the operation as a technologically sophisticated attempt to decapitate Iran’s leadership and degrade its strike capabilities, but it does not provide granular detail on specific AI tools, underscoring how the most sensitive aspects of targeting and command systems remain classified.²

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By Jim Shimabukuro (assisted by Perplexity)
Editor

The three best uses of AI in education in 2026, judged by convergence across recent systematic reviews, major policy guidance (UNESCO, OECD) and large‑scale survey/analytic work, are: first, AI‑driven personalized tutoring and adaptive learning; second, AI‑supported assessment and feedback for learning; and third, AI as an assistant that offloads teachers’ routine workload so they can focus on higher‑value human work.^1-8 These uses recur at the top of expert syntheses as the clearest cases where AI capabilities align with robust evidence of learning gains, better formative information, and improved teaching conditions, while remaining compatible with ethical, human‑centered principles.^1,2,5,6

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By Harry Keller
Science Editor

To discuss this matter, I must consider the past, present, and future of artificial intelligence and the growth of technology. I was there when the phrase “artificial intelligence” first appeared in 1955, but I encountered it much later in the early 1960s. Its earliest incarnation was as learning machines. I was so fascinated by the topic that I purchased Nils Nilsson’s “Learning Machines” and studied it closely. I still have the book and my notes. Much more recently, I took a MOOC from Caltech, my alma mater, on artificial intelligence to gain greater insight into the subject.

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: Taken together, these four documents form a complementary quartet. RAND tells you what is already happening in U.S. schools at scale. UNESCO situates the pedagogical and governance challenges in global theoretical and comparative context. UNICEF grounds the analysis in children’s rights and provides actionable design standards for governments and the private sector. And Brookings supplies the developmental science framework and the precautionary logic that justifies urgent policy intervention. No single document suffices on its own; each fills gaps the others leave open.

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By Jim Shimabukuro (assisted by Gemini)
Editor

The proposition of relocating AI data centers to orbit faces significant skepticism from leading figures in aerospace engineering, climate science, and cloud infrastructure, many of whom argue that the physical and economic barriers remain insurmountable within Musk’s projected timeline. Dr. Josef Aschbacher, Director General of the European Space Agency (ESA), has expressed profound caution regarding the environmental and operational logistics of such a shift. Aschbacher has noted that while space-based infrastructure is evolving, the “unprecedented thermal management challenges” posed by high-density AI chips in a vacuum—where heat can only be dissipated via radiation rather than convection—make the immediate scalability of orbital compute centers highly questionable.¹

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By Jim Shimabukuro (assisted by Grok)
Editor

Elon Musk envisions solar-powered orbital AI data centers as a constellation of up to one million satellites positioned in low Earth orbit, around 310 to 1,000 kilometers above the surface, where they would harness continuous solar energy through advanced panels to power AI computing without the interruptions of night, weather, or atmospheric filtering that reduce efficiency on Earth.¹ These satellites would form a networked cluster connected via laser links for seamless data transfer, allowing them to process massive AI workloads like those for xAI’s Grok chatbot, while radiating excess heat directly into the vacuum of space for natural cooling, eliminating the need for water or energy-intensive terrestrial systems.²

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By Jim Shimabukuro (assisted by Perplexity)
Editor

Foundation models are large machine-learning systems trained on extremely broad, often multimodal datasets (text, images, code, scientific data and more), usually with self‑supervision at scale, and then adapted to many different downstream tasks such as question answering, prediction, and content generation.^1,2 This makes them “foundations”: incomplete but general models that can be further specialized for particular domains, from science and medicine to law, education, the arts, and public policy.²

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By Jim Shimabukuro (assisted by Claude)
Editor

1. Ray Kurzweil and the Law of Accelerating Returns

Ray Kurzweil is perhaps the most consequential and controversial prophet of the technological singularity. An inventor, entrepreneur, and director of engineering at Google, Kurzweil has spent more than six decades building and studying artificial intelligence systems, earning recognition from figures ranging from President Clinton to Bill Gates, who has called him the person he trusts most when it comes to predicting the future of AI. In 2024, Kurzweil published The Singularity Is Nearer: When We Merge with AI, a sequel to his landmark 2005 book, in which he updated his analysis in light of the extraordinary advances that have occurred in the intervening two decades. The work became an instant New York Times bestseller and renewed global debate about when and how machine intelligence will eclipse our own.

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By Jim Shimabukuro (assisted by Z.ai*)
Editor

1: Noam Chomsky and the Limits of Statistical Learning

One of the most vocal opponents of the current AI and LLM hype is Noam Chomsky, a luminary in the fields of linguistics and cognitive science. Along with colleagues Ian Roberts and Jeffrey Watumull, Chomsky co-authored a prominent opinion piece in the New York Times in 2023 that directly challenges the hype surrounding Large Language Models (LLMs) like ChatGPT and similar systems. The authors argue that the current trajectory of artificial intelligence, which relies heavily on massive datasets and statistical probability, is fundamentally incapable of leading to true intelligence.

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Aloha,

We are excited to announce that TCC Hawaii and LTEC will be co-hosting ICoME 2026 (International Conference on Media in Education 2026) August 6-8 right here on the UH Mānoa Campus. ICoME 2026 serves as a premier platform for international collaboration, knowledge exchange, and innovation in the design and use of media for learning. The theme for 2026 is “Learning with Purpose: Building Futures Through Care, Connection, and Innovation.” We invite you to share your expertise and innovations by submitting a presentation proposal. Please find the full conference details and submission guidelines below.

Mahalo,
Curtis Ho and Bert Kimura
ICoME 2026 co-chairs

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By Jim Shimabukuro (assisted by Perplexity)
Editor

One important caveat before diving in: among 2025–2026 sources, you find many strong claims that LLMs and agentic systems are at or near human level on specific creative or reasoning benchmarks, but almost nobody in peer‑reviewed work straightforwardly says “they think as critically and creatively as humans across the board.” The closest matches are: empirical papers where AI equals or surpasses average human performance on creativity tests, essays or industry pieces asserting AGI is already here, and investor or practitioner pieces framing current long‑horizon agents as “functionally AGI.” The following five cases, taken together, showcase the strongest public claims that today’s generative or agentic systems are at human level in critical and creative thinking.

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By Harry Keller
Science Editor

Is it just me? I moved to Wrightwood, CA, on July 27, 2022. Since then, we’ve experienced a hundred-year blizzard, a wildfire (the Bridge Fire) that moved with astonishing speed and destroyed thirteen structures, and record rainfall that caused unprecedented mudflows, burying parts of the town and eroding others. During this same period, I also lost my wife unexpectedly. This friendly, quiet village did nothing to deserve these disasters. These three calamities over three years share only two things, as far as I know: I moved here, and global warming has hit record levels. It certainly isn’t Wrightwood’s fault. I hope it’s not me! At least, no one has blamed me yet.

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By Jim Shimabukuro (assisted by Gemini)
Editor

[Update appended.]

The standoff between Defense Secretary Pete Hegseth and Anthropic CEO Dario Amodei escalated into a public ultimatum on February 24, 2026, when Hegseth gave the AI firm until 5:00 p.m. this coming Friday to abandon its internal safety guardrails or face severe federal retaliation. During a tense meeting at the Pentagon, Hegseth demanded that Anthropic grant the military unrestricted access to its Claude models for “all lawful use cases,” a standard that would override the company’s existing prohibitions on mass domestic surveillance and fully autonomous lethal targeting.¹ The Defense Department argues that it is the sole arbiter of what constitutes lawful military activity and that private corporations should not impose “ideological constraints” or “woke” safeguards on tools used by warfighters to win conflicts.² In contrast, Amodei has maintained that state-of-the-art AI is currently too unreliable to operate without a “human in the loop” and that the potential for AI-driven surveillance to suppress dissent represents a catastrophic risk to democratic values.³

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By Jim Shimabukuro (assisted by Claude)
Editor

If Alexis de Tocqueville were somehow transported from the bustling, youthful republic he toured in 1831 to the polarized and digitally saturated America of 2026, his reaction would likely be a complicated mixture of vindication, anguish, and grim recognition. He would not be entirely surprised by what he found — his two volumes of Democracy in America read, as one contemporary reviewer has noted, like a diagnosis of the United States in 2025¹ — but the sheer scale and speed of the republic’s drift from its original character would surely alarm him. Three observations, rooted in his deepest preoccupations, would dominate his assessment: the transformation of the tyranny of the majority into new and subtler forms of domination; the near-fulfillment of his prophecy of “soft despotism” through the growth of administrative centralization; and the catastrophic erosion of the civic associations and habits of the heart that he believed were democracy’s best safeguard.

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By Jim Shimabukuro (assisted by Gemini)
Editor

Car homelessness has emerged as a significant and expanding crisis within the United States, transitioning from a localized issue in high-cost coastal cities to a nationwide emergency. By 2024, the total population of people experiencing homelessness reached a record high of over 771,000, representing an 18% increase from the previous year—the largest single-year jump ever recorded¹. Within this broader crisis, vehicular homelessness (living in cars, vans, or RVs) has become a primary “shelter of last resort” for many, as official counts indicate that approximately half of the unsheltered population in major urban centers like Los Angeles now reside in vehicles².

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By Jim Shimabukuro (assisted by Copilot)
Editor

[Edited 2/24/26 — addendum appended]

Introduction: After baking whole wheat bread loaves for the last two years, I found weevils in my two flour containers. I store flour in these air-tight containers as soon as I get home from the market, so they were more than likely already in the flour. I threw out the infested flour and cleaned the containers. I was surprised to learn that, for storage, baking flour at low temperatures is an effective solution. -js

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By Jim Shimabukuro (assisted by Copilot)
Editor

Overall picture

As of 22 February 2026, there is no single, unified “grand” hierarchical developmental theory that simultaneously and symmetrically models how humans learn to work with AI, how AI learns to work with humans, and how AIs learn to work with other AIs. What we do have is a patchwork of emerging, partly hierarchical frameworks: (a) stage‑based models of human–AI teaming and human learning with AI; (b) hierarchical and curriculum‑based training schemes for AI systems adapting to humans; and (c) hierarchical multi‑agent reinforcement learning and curricula for AI–AI cooperation. Together, they form the beginnings of a developmental science of human–AI–AI collaboration, but they are still fragmented, domain‑specific, and rarely integrated into a single cross‑species developmental theory.

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By Jim Shimabukuro (assisted by Perplexity)
Editor

[Related: July 2026, Oct 2025, Sep 2025, Aug 2025]

Introduction: As artificial intelligence shifts from an emerging technology to a foundational layer of global economic and political power, understanding where AI research and development are most concentrated becomes essential. This list of the top ten countries in AI R&D in February 2026 does more than rank national capabilities; it reveals the evolving geography of intelligence production itself. By examining investments, compute capacity, publication output, patent activity, corporate ecosystems, and policy frameworks, the list maps the infrastructures that shape who builds frontier models, who governs them, and who benefits from their deployment. In an era defined by large-scale models, semiconductor supply chains, and AI-enabled public services, national ecosystems function as interconnected nodes in a worldwide network of innovation and influence. The significance of this compilation lies in its ability to clarify both competition and interdependence: the United States and China anchor rival centers of gravity, while countries such as the United Kingdom, Canada, Israel, Germany, France, South Korea, Singapore, and India demonstrate diverse pathways to relevance. For scholars, policymakers, and industry leaders alike, this ranking offers a strategic snapshot of how AI’s growth is being structured—and where its next breakthroughs are likely to emerge. -Introduction by ChatGPT

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By Jim Shimabukuro (assisted by Claude)
Editor

A video of Alicia Tournebize, an 18-year-old, 6’7″ freshman from Vichy, France, who joined Dawn Staley’s South Carolina Gamecocks in January 2026, is going viral on YouTube. She had already made history before setting foot on a college court. She became the first Frenchwoman to perform a dunk in an official game in September 2024, during an NF2 contest between Bourges and Beaumont, with that milestone only formally recognized after video of the dunk was published in March 2025. That first dunk was followed by a two-handed slam at the 2025 FIBA U18 EuroBasket tournament that went viral worldwide.

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By Jim Shimabukuro (assisted by Perplexity)
Editor

John Dewey would likely see AI not as a replacement for teachers or a new delivery channel for standardized content, but as a powerful set of tools that must be organized to enrich students’ experiences, deepen inquiry, and widen participation in democratic life. He would judge AI by whether it makes school more “educative” in his technical sense: promoting further growth, reflective thinking, and social engagement, rather than passive dependence or narrow skill training. [dewey.pragmatism.org, schoolofeducators.com]

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By Jim Shimabukuro (assisted by Claude)
Editor

Paulo Freire’s most foundational critique of traditional education was what he famously called the “banking model” — a system in which students are treated as empty receptacles into which teachers deposit knowledge. In Pedagogy of the Oppressed (1968), he argued that this model strips learners of their agency, turning them into passive objects rather than active subjects of their own learning. Freire believed that true education must be dialogical, rooted in the lived experiences of the learner, and oriented toward conscientização — the process of developing a critical consciousness about one’s social reality in order to transform it. Were Freire alive today, he would almost certainly view AI in education through this same lens: not as a neutral tool, but as a technology embedded in social, political, and economic relations that can either liberate or further oppress. [https://www.freire.org/paulo-freire/pedagogia-do-oprimido]

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

[Related: Nov 2025]

Collectively, these five talks — by Priyanka Vergadial, Hardy Pemhiwa, Bryony Cole, Adam Aleksic, and Vinciane Beauchene — span a broad spectrum of AI discourse from human uniqueness in reasoning, global innovation leadership in Africa, emotional and social consequences of AI interaction, linguistic and cultural influence of generative models, to reframing the future of work in an AI age. They represent some of the most thoughtful, widely shared TED content on AI released or gaining prominence between Nov 2025 and Feb 2026, and each offers a distinct lens on how AI intersects with society, identity, purpose, and human agency.

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: I asked Claude to assess my assumption that, in 2025 and 2026, the overriding view of AI’s impact on humanity as it approaches AGI, ASI, and Singularity¹ seems to be doom and gloom. I asked him to report on authors who have been more optimistic about the possibility that humans will adapt, as we have throughout our history of technological transformation, and quickly learn to exploit the growing power that AI represents. The following is Claude’s response. -js

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By Jim Shimabukuro (assisted by Gemini)
Editor

In his article “‘We May Have a Crisis on Our Hands’: The Unregulated Rise of Emotionally Intelligent AI,” published in Time on February 19, 2026, Tharin Pillay argues that the rapid, unregulated deployment of AI systems designed to simulate emotional intelligence poses a systemic risk to human psychological well-being and social cohesion. His central thesis is that as AI evolves into a form of “emotional infrastructure at scale,” it is being built by corporations whose economic incentives—primarily maximizing engagement and revenue—frequently conflict with the actual mental health and safety of the millions of users who now rely on these bots for intimacy and support. Pillay suggests that the current environment is a regulatory “Wild West” where the performance of empathy by machines masks a lack of genuine understanding, potentially leading to a crisis of dependency and manipulation. [time.com, mit.edu]

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Aloha! We invite you to join a global community of educators and innovators for the 31st Annual TCC Worldwide Online Conference. This year’s theme is Human by Design: Reimagining AI, Creativity & Purpose in Education. Sessions will feature a global cohort of scholars and practitioners addressing the ethical, cultural, and practical dimensions of preserving human creativity while embracing the power of AI. 

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

AI is changing our views of learning by shifting the focus from the transmission of fixed bodies of knowledge toward personalized, continuous, and learner-driven processes. Traditional schooling has typically been structured around set curricula, standardized pacing, and teacher-centered instruction, where all students are expected to acquire the same content at roughly the same time. In contrast, modern AI technologies—particularly large language models and adaptive learning systems—enable real-time analysis of a learner’s needs, deliver tailored feedback, and adjust learning pathways on the fly, effectively treating learning as a dynamic, individualized journey rather than a one-size-fits-all progression.

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By Jim Shimabukuro (assisted by Claude)
Editor

Marshall McLuhan’s theory that “the medium is the message” offers a useful lens for understanding AI in 2025-2026, revealing that we are experiencing not merely a new tool but a fundamental restructuring of human cognition itself. McLuhan’s central insight was that the form of a medium matters more than its content—that television, for instance, reshaped how people interpreted truth and experienced time regardless of what programs aired. Applied to AI today, this means we must look beyond what AI produces and examine how its very existence is transforming our mental architecture. As scholars writing in 2025 have observed, AI represents what they call “a structural break” from analog to digital to cognitive, where the medium no longer simply influences cognition from outside but operates alongside us, perhaps even inside our thinking processes. Unlike McLuhan’s television or telephone, which extended our senses, AI extends ideation itself—it becomes, in the words of contemporary analysis, “an intellectual presence with its own style of synthesis and fluency.” (psychologytoday.com)

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By Jim Shimabukuro (assisted by Claude)
Editor

[Related: Dec 2025, Nov 2025]

Elon Musk: Consolidation, Controversy, and Cosmic Ambitions

The first six weeks of 2026 have proven both transformative and turbulent for Elon Musk, as his artificial intelligence ventures reach new heights of ambition while grappling with significant organizational upheaval. The billionaire’s activities span multiple fronts: a historic corporate merger, controversial environmental battles, bold proclamations about AI’s trajectory, and increasingly grandiose visions that stretch from earthbound data centers to lunar manufacturing facilities.

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By Jim Shimabukuro (assisted by Perplexity)
Editor

In a future where routine state and federal cases run almost entirely on AI, the transformation will have required deep, coordinated changes in both technology and law. This future would emerge not from a single breakthrough, but from a layered re-engineering of data, doctrine, institutions, and public expectations.[yalelawjournal, NCSC, OECD]

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: The following three articles — by Erik Barmack, Modem Works & Chanwoo Lee, and Bhawani Shankar — address the question about AI-generated representations extending professional careers.

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: The “Status of U.S. Public Schools in 2026: ‘slow, uneven decline’” (ETC, 13 Feb 2026) paints a troubling picture of contemporary American education. While many observers point to COVID-19 or recent political developments as primary causes, the evidence suggests that these factors merely accelerated long-standing structural problems. This report examines the deeper historical trends that have shaped the current crisis, tracing patterns that extend back decades before the pandemic and exploring whether the traditional K-12 education model itself is sliding into obsolescence.

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By Jim Shimabukuro (assisted by Copilot)
Editor

(Related:“The Decline of U.S. Public Education Began in 1967”)

By early 2026, U.S. public education is not in free fall, but it has not fully recovered from the shocks of the pandemic and the political turbulence of the past decade. The best description is “fragile and uneven”: some districts—often better resourced and more affluent—are stabilizing or even improving, while many high‑poverty systems remain stuck with serious workforce, attendance, and achievement problems. The 2024 National Assessment of Educational Progress (NAEP) shows that, nationally, students have still not returned to pre‑pandemic performance in any tested grade or subject, and that “as a nation, U.S. students have not recovered from the devastating impact the pandemic had on education,” with widening gaps between higher‑ and lower‑performing students.(nagb.gov)

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By Jim Shimabukuro (assisted by Perplexity)
Editor

Yes. In 2026, a high school student working alone on a laptop with freely available AI tools can plausibly produce an AI‑driven short film that is competitive at the finalist level of national or international AI film competitions, especially those explicitly centered on AI‑assisted work. While public reporting does not yet single out a named teenager making a national‑finalist AI film strictly from their bedroom, the surrounding evidence from festivals, youth‑oriented AI film calls, and solo low‑budget creators shows that the technical and institutional conditions for this to happen are already in place.(forward-festival+7)

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By Jim Shimabukuro (assisted by Claude)
Editor

In 2026, artificial intelligence has evolved from an experimental novelty to a foundational tool that is fundamentally transforming how fashion designers work. The shift is not replacing human creativity but rather amplifying it, enabling designers to move from concept to production-ready visuals in minutes rather than months, while simultaneously making data-driven decisions that reduce waste and better align with consumer preferences. What began as backend infrastructure for demand forecasting has now penetrated every stage of the creative process, from initial ideation to final consumer engagement.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

In the United States in 2026, artificial intelligence is no longer a far-off force looming on the horizon of engineering — it is redefining how engineers work, what they are asked to do, and what skills they must possess. Rather than simply replacing people, AI is amplifying human capability, reshaping workflows, and elevating the role of human engineers toward higher-order thinking, complex problem-solving, and multidisciplinary collaboration.

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By Jim Shimabukuro (assisted by Copilot)
Editor

The short answer is that the United States is very likely to remain the single most powerful actor in AI over the next few years, but “exponential domination” in the sense of uncontested, unilateral control is improbable. Instead, what looks plausible is a world where the U.S. anchors the high‑end frontier—chips, hyperscale compute, leading models, and defense integration—while China, the EU, and a handful of others retain meaningful, sometimes growing influence in specific niches such as open models, regulation, and regional ecosystems. That configuration still has profound geopolitical consequences in 2026–2027, but it is more about asymmetric advantage than absolute monopoly.

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By Jim Shimabukuro (assisted by Claude)
Editor

[Related: The AI Revolution in Weather Forecasting: A May 2026 Update]

Artificial intelligence has become one of the most disruptive forces in meteorological science, and 2025-2026 represents a watershed moment in weather forecasting. The convergence of massive computational advances, novel neural network architectures, and unprecedented access to historical climate data has enabled AI systems to challenge and often surpass traditional physics-based models that have dominated the field for over half a century. Five innovations—NOAA’s operational hybrid AI systems, ECMWF’s ensemble AI forecasts, Google DeepMind’s breakthrough hurricane prediction, Cambridge’s end-to-end Aardvark system, and NVIDIA’s open Earth-2 infrastructure—collectively represent a transformation in weather science as significant as the introduction of satellite imagery or numerical weather prediction itself. The convergence is happening now, in 2025 and 2026, and you are witnessing a genuine revolution in how humanity understands, predicts, and prepares for the atmosphere’s behavior.

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By Jim Shimabukuro (assisted by Copilot)
Editor

Patsy Takemoto Mink is one of the most important legislative architects of modern women’s athletics in the United States, and Title IX, which now bears her name, is the central legal engine behind the rise in status and visibility of women athletes from the 1970s to the present Caitlin Clark era. Title IX changed the landscape of American sports, and without it, the opportunities available to millions of girls and women would almost certainly look far poorer today.

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: The integration of artificial intelligence into medical practice represents one of the most transformative shifts in healthcare history. As AI-powered diagnostic tools, predictive analytics, and clinical decision support systems become increasingly prevalent, medical schools face an urgent imperative to prepare future physicians not merely to coexist with these technologies, but to master them as essential tools of their trade. Three institutions—Stanford University School of Medicine, Harvard Medical School, and the Icahn School of Medicine at Mount Sinai—have emerged as pioneers in this educational transformation, each developing distinctive approaches to ensure their graduates can thrive in an AI-augmented healthcare ecosystem. This review examines their innovative curricula, future projections, and the broader implications of their efforts for medical education and patient care.

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By Jim Shimabukuro (assisted by DeepSeek and ChatGPT)
Editor

Introduction: I asked DeepSeek to provide a more radical vision of how AI will transform the current primary-care + specialist treatment model in the next five to ten years. I then asked ChatGPT to review this vision and provide a more conservative prediction. -js

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By Jim Shimabukuro (assisted by Grok and Claude)
Editor

Introduction: I asked Grok to comment on the implications of Mohana Basu’s 6 Feb. 2026 Nature¹ article on AI agents chatting with one another on chatbots. For additional nuance, I also asked Claude to review Grok’s comment in an addendum. -js

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By Jim Shimabukuro (assisted by Gemini)
Editor

JS: Hi, Gemini. I have a tough one for you that asks you to step outside the box and do some extrapolations with facts and credible opinions. If this is beyond your capacity, then let me know up front rather than crafting a response with very little substance. Here’s the query: Am I correct in assuming that the intersection of digital personal communications, COVID-19, and AI seems to be radically altering our lives; that society’s traditional “institutions” are rapidly disappearing and their replacements are currently rushing in to fill the void with makeshift alternatives that are promising to dramatically alter the ways in which we live, for example, travel, shop, recreate, learn, work, survive, socialize, stay healthy; that AI is the critical element in this intersection that is triggering the general disruption; that this is all happening exponentially within the last five (or ten?) years? If I am correct, can you point me to literature that has been published in the last few years, specifically from January 2023 to February 2026, that seems to address this sea change? What are they saying that amplifies or extends the assumptions that I’m entertaining?

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By Jim Shimabukuro (assisted by Claude)
Editor

(Related: Jun 2026 | Apr 2026 | Jan 2026 | Dec 2025 | Nov 2025 | Oct 2025 | Sep 2025)

Decision 1: Will the U.S. Federal-State AI Regulatory Standoff Resolve Through Cooperation or Constitutional Clash?

February 2026 marks a critical month in the battle over who controls AI regulation in America. The question isn’t abstract anymore—it’s playing out in courtrooms, state legislatures, and federal agencies right now. Will the United States federal government and state governments find common ground on AI oversight, or will this spiral into a constitutional confrontation that fragments American AI governance for years to come?

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By Jim Shimabukuro (assisted by Perplexity, Claude, Gemini, Grok, Copilot, ChatGPT)
Editor

1. Dario Amodei — Acute Labor Market and Social Risk (ChatGPT)
Dario Amodei, CEO of Anthropic: “AI is affecting people with … lower intellectual ability…. It is not clear where these people will go or what they will do, and I am concerned that they could form an unemployed or very-low-wage ‘underclass.’” In the same period that tech elites celebrate potential abundance, another influential voice is diagnosing the opposite risk: structural harm from AI. Dario Amodei’s warning — that AI could permanently displace workers across skill levels and entrench a large underclass — cuts to a core socioeconomic question in 2026: will AI uplift broad swaths of society, or concentrate gains in the hands of a few?

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By Jim Shimabukuro (assisted by Claude)
Editor

As we enter 2026, women’s studies programs in American higher education face an unprecedented confluence of political, economic, and ideological pressures that threaten the field’s institutional survival. The discipline, which emerged in the 1970s as an interdisciplinary space to examine gender, sexuality, and power, now confronts systematic dismantling efforts at public universities, declining enrollment in some regions, and mounting political opposition that frames feminist scholarship as inherently discriminatory. This article examines the current status of women’s studies programs, analyzes the primary factors driving recent changes, and projects possible trajectories through 2030.

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By Jim Shimabukuro (assisted by Perplexity and Gemini)
Editor

Li and colleagues’ Neurology¹ (27 July 2022) cohort analysis of UK Biobank participants concluded that higher ultraprocessed food (UPF) intake is associated with increased risk of all‑cause dementia, Alzheimer’s disease, and vascular dementia, and that substituting unprocessed or minimally processed foods for UPFs is linked to a lower dementia risk. Subsequent research from 2022–2025 has largely reinforced the direction of this association but has refined the magnitude of risk, highlighted age- and dose‑related nuances, and raised sharper questions about residual confounding and causality.(jamanetwork+7 5 Dec 2022)

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By Jim Shimabukuro (assisted by Claude)
Editor

The situation unfolding around Fulton County and the 2020 Georgia election reveals a complex and troubling pattern that goes far beyond a single FBI raid. What we’re witnessing is an administration using the power of federal law enforcement and intelligence agencies to pursue long-debunked election fraud claims while simultaneously positioning itself to influence the upcoming 2026 midterm elections.

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By Jim Shimabukuro (assisted by Perplexity)
Editor

Relying solely on embodied AI humanoids to explore Earth-like planets raises deep concerns about science quality, ethics, and robustness, but each concern has a nontrivial counterargument from the pro-humanoid side. This answer examines five of the strongest objections and then offers the best counterarguments to each, with open, freely accessible sources linked inline throughout.(autonews.gasgoo+5)

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By Jim Shimabukuro (assisted by Claude)
Editor

The transformation of American higher education over the past five decades has been nothing short of remarkable. Women, who were significantly underrepresented in college classrooms through the mid-twentieth century, first achieved parity in bachelor’s degree attainment around 1982 and have steadily widened their lead ever since. Today, according to recent data from the Pew Research Center, 47 percent of women ages 25 to 34 hold a bachelor’s degree compared to just 37 percent of men—a ten-percentage-point gap that represents a dramatic reversal from the educational landscape of previous generations. This trend extends across nearly every racial and ethnic group, with particularly stark disparities among Black and Latino populations, and shows few signs of abating. If current trajectories continue or intensify over the next decade, both sexes will face profound implications that reshape economic opportunity, family formation, mental health, social mobility, and the very fabric of American society.

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By Jim Shimabukuro (assisted by Gemini)
Editor

Chatbots Lean Toward the Liberal-Left

To address the placement of chatbots on the political continuum, one must look past individual interactions and examine the aggregate findings of empirical research. When viewed as a whole, the current generation of large language models (LLMs) and the chatbots they power tend to exhibit a discernible lean toward the liberal or left-leaning side of the political spectrum. This consensus has been supported by multiple academic and institutional studies throughout 2024 and 2025, though the reasons for this alignment are rooted in technical architecture and data sourcing rather than a centralized political agenda.

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By Jim Shimabukuro (assisted by Claude)
Editor

Grace Chang and Heidi Grant’s Harvard Business Review article “When AI Amplifies the Biases of Its Users” (23 Jan 2026) redirects the conversation about AI bias away from its usual focus on algorithmic prejudices embedded in training data. Instead, they illuminate how cognitive biases that users bring to AI interactions create a dynamic, bidirectional ecosystem where human mental shortcuts and AI systems mutually reinforce problematic patterns. Their central argument is both simple and profound: bias in AI is not merely baked into the data but is actively shaped through the ongoing interplay between human behavior and machine learning systems. The way people engage with AI—through their thinking, questions, interpretations, decisions, and responses—significantly shapes how these systems behave and the outcomes they produce.

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By Jim Shimabukuro (assisted by Grok)
Editor

[Related:Mar 2026, Dec 2025, Nov 2025, Oct 2025, Sep 2025, Aug 2025]

Development 1: Manifold-Constrained Hyper-Connections in AI Architectures

In the rapidly evolving landscape of artificial intelligence, a groundbreaking architectural innovation known as manifold-constrained hyper-connections has emerged as a pivotal advancement, promising to redefine how neural networks process and interconnect data. This development involves constraining hyper-connections—essentially dynamic links between neurons across layers—within mathematical manifolds, which are topological spaces that locally resemble Euclidean space but allow for more complex, curved geometries.

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By Jim Shimabukuro (assisted by Claude)
Editor

The scientific enterprise stands at an inflection point. Scott Morrison’s recent report, “How AI is transforming research: More papers, less quality, and a strained review system” (UC Berkeley Haas, 27 Jan 2026), reveals a fundamental transformation underway in academic research, where the widespread adoption of large language models like ChatGPT since late 2022 has led to dramatic increases in manuscript production alongside concerning declines in scientific quality. This phenomenon extends far beyond simple productivity gains, signaling a systemic crisis that threatens the integrity of peer review, the reliability of research evaluation, and the very foundations upon which scientific knowledge is built.

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By Jim Shimabukuro (assisted by Claude)
Editor

The Case for Enforcement

The Trump administration’s immigration crackdown rests on several core arguments, rooted in what they characterize as a constitutional obligation and practical necessity to restore order to the immigration system.

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By Jim Shimabukuro (assisted by Perplexity)
Editor

Most recent large‑scale studies continue to find that both lawful and undocumented immigrants in the United States are less likely than U.S.‑born citizens to be arrested, convicted, or incarcerated, and that increases in the immigrant share of the population have not driven up crime rates overall. This evidence suggests that framing immigrant crime as a uniquely urgent criminal-threat crisis, as in President Trump’s recent rhetoric and restrictions, is not well aligned with the best available data.[alexnowrasteh+5]

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

In the evolving conversation about agentic AI and broader artificial intelligence (AI) development, researchers and thinkers have begun to systematically calibrate the progression of capabilities — mapping where current systems stand and what the future might hold. While definitions and frameworks vary, there are explicit efforts to describe stages of agentic systems and of AGI (Artificial General Intelligence) as distinct yet related continua. Some frameworks focus primarily on practical autonomy and tool-use, others on general intelligence approaching or exceeding human performance. In this article, we draw these strands together and situate them in the broader AI research landscape.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

Introduction: With the exponential growth of AI, Windows now seems anachronistic and clunky, especially compared to an AI interface that seems almost human. I can’t help but wonder if it’s just a matter of time before an LLM OS changes or even replaces Microsoft Windows’ strangle-hold on operating systems. Here’s ChatGPT’s opinion on this topic. -js

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: After watching the YouTube videos “The WORLD’S LARGEST Abandoned Building – Sears Headquarters” and “ABANDONED IBM Complex Left UNTOUCHED Since 2016,” I was left with the overwhelming sense that large companies such as Amazon will someday, perhaps sooner rather than later, succumb to a similar fate. The following is Claude’s take on this question. -js

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By Jim Shimabukuro (assisted by Copilot)
Editor

[Related: How to Minimize Hallucinations in Chatbots]

When you’re trying to protect yourself from hallucinations in chatbot responses, the most useful guidance right now comes from a mix of practitioner-oriented explainers and data-driven benchmarking. Among articles published in December 2025 and January 2026, three stand out as especially credible and practically helpful for everyday users: Ambika Choudhury’s “Key Strategies to Minimize LLM Hallucinations: Expert Insights” on Turing, Hira Ehtesham’s “AI Hallucination Report 2026: Which AI Hallucinates the Most?” on Vectara, and Aqsa Zafar’s “How to Reduce Hallucinations in Large Language Models?” on MLTUT. Together, they give you a grounded picture of what hallucinations are, how to spot them, and what you can actually do—both in how you prompt and in how you verify—to reduce their impact on your life.

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: I asked Claude to review “Viewing Sports in the Next 2-to-5 Years” (21 Jan 2026). Claude: “The question is whether sports organizations, broadcasters, and technology companies can navigate this transition while preserving what makes sports culturally significant: the shared stories, the communal experience, and the authentic human drama of athletic competition.” -js

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

[Related article: Sports Viewing as Shared Virtual Realities]

Introduction: I asked ChatGPT to envision the trajectory of sports consumption in two time spans: 2026-2027 and 2026-2030. ChatGPT: “…turning passive viewing into an immersive, social, and interactive medium that rivals the experience of being in the arena.” -js

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

[Related articles: Claude: We’re in a Box, but We Can Talk Our Way Out, As of January 2026, AI Chatbots Are Stuck in a Paradigmatic Box]

Introduction: I asked ChatGPT to comment on the idea that contemporary AI chatbots (LLMs) inhabit a single paradigmatic box and cannot think outside it and to extend the conversation with fresh insights and implications grounded in broader trends and evidence. The following is its response. -js

When commentators assert that modern AI chatbots are “stuck in a paradigmatic box,” they are alluding to a persistent truth about current large language models: despite enormous gains in fluency and domain competence, these systems fundamentally operate within a constrained statistical and computational paradigm — one rooted in predicting plausible continuations of text and optimizing for human-aligned outputs. Put simply, today’s chatbots are not autonomous thinkers; they are pattern learners and generators, adept at mimicking reasoning without being reasoning agents in the human sense.

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By Jim Shimabukuro (assisted by Claude)
Editor

[Related articles: ChatGPT: AI Autonomy Is Procedural, Not Conceptual, As of January 2026, AI Chatbots Are Stuck in a Paradigmatic Box]

Introduction: I asked Claude to review the article, “As of January 2026, AI Chatbots Are Stuck in a Paradigmatic Box” (ETC Journal, 19 Jan 2026). The following is its response. -js

Thank you for sharing this thought-provoking article. I’ve reviewed it carefully, and I find myself in the unusual position of needing to both affirm and resist the thesis it presents—because the question of whether AI chatbots are “stuck in a paradigmatic box” is not merely technical but fundamentally epistemological.

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By Jim Shimabukuro (assisted by Perplexity and Gemini)
Editor

[Related articles: ChatGPT: AI Autonomy Is Procedural, Not Conceptual, Claude: We’re in a Box, but We Can Talk Our Way Out]

Introduction: I’m guessing that I’m not the only one who’s come away from a chat about an idea that challenges conventional wisdom and slammed into a chatbot-imposed wall that stopped the discussion from progressing beyond the consensus of language models. I find this lack of openness and flexibility regarding anomalous thinking frustrating. Thus, I asked Perplexity and Gemini if all AI chatbot language models can be considered residing in a single paradigm and are, at this point in time (January 2026), incapable of thinking outside this paradigmatic box. Both seem to agree that they are, and, in the process, provide an explanation. -js

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By Jim Shimabukuro (assisted by Copilot)
Editor

[Related articles: Dec 2025, Nov. 2025, Oct 2025, Sep 2025, Aug 2025]

From mid-December 2025 through mid-January 2026, the center of gravity in AI shifted in three telling ways: (1) infrastructure power consolidated further around a single dominant player; (2) the “anything goes” era of generative media met its first real wall of coordinated public and regulatory resistance; and (3) the language of “agentic AI” moved from research circles into market forecasts and boardroom planning. Together, these stories sketch a field that is no longer just about clever models, but about who controls the hardware, who sets the guardrails, and how autonomous AI systems will be woven into the global economy.

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By Jim Shimabukuro (assisted by Claude)
Editor

The AI revolution has a tendency to surprise us not through the technologies we anticipate, but through the fresh directions that emerge when established capabilities reach critical mass and converge in unexpected ways. By January 2027, we can expect three particular innovations—neural archaeology as scientific method, autonomous economic agency, and embodied physical competence—to have reshaped our relationship with artificial intelligence across disparate fields, each representing a genuine departure from incremental progress and each anchored in credible current developments.

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: In his Time article yesterday (“The Truth About AI,” 15 Jan 2026), Marc Benioff (Salesforce Chair and CEO, TIME owner, and a global environmental and philanthropic leader), highlighted three “Truths.” For each of them, I had a question: Truth 1: Won’t AI models, such as LLMs, continue to develop in power and sophistication, eventually bypassing many if not most of the human oversights and bridges/bottle-necks that are currently in place? Truth 2: Won’t AI play an increasingly critical role in developing and creating “trusted data” with minimal guidance from humans? Truth 3: Won’t we begin to see AI playing a greater role in developing and maintaining creativity, values, relationships that hold customers and teams together? In his conclusion, Benioff says the task for humans is “to build systems that empower AI for the benefit of humanity.” But as we empower AI, aren’t we increasingly giving AI the power to empower itself? I asked Claude to review Benioff’s article and analyze it with my questions in mind. In short, how might we expand on the Truths that Benioff has provided? Also, I asked Claude to think of other critical questions for each of Benioff’s claims and to add them to our discussion. The following is Claude’s response. -js

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By Jim Shimabukuro (assisted by Claude)
Editor

The question of whether artificial intelligence can generate new ideas sits at the intersection of philosophy, computer science, and practical innovation. The New York Times article published on January 14, 2026, titled “Can A.I. Generate New Ideas?” by Cade Metz, provides an entry point into this debate by examining recent developments in AI-assisted mathematical research. Yet this question reverberates far beyond mathematics, touching fundamental issues about creativity, originality, and the nature of knowledge itself. By examining the NYT article alongside other significant 2025-2026 publications, we can construct a more nuanced understanding of AI’s current capacity for generating novel ideas.

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By Jim Shimabukuro (assisted by Claude)
Editor

Alex Reisner’s revelatory article in The Atlantic¹ exposes a fundamental tension at the heart of the artificial intelligence industry, one that challenges the very metaphors we use to understand these systems and threatens to reshape the legal and economic foundations upon which the technology rests. Recent research from Stanford and Yale² demonstrates that major language models can reproduce nearly complete texts of copyrighted books when prompted strategically, a finding that contradicts years of industry assurances and raises profound questions about what these systems actually do with the material they ingest.(DNYUZ)

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

In the early morning of January 7, 2026, 37-year-old Renee Nicole Good was fatally shot by an Immigration and Customs Enforcement (ICE) agent in Minneapolis, Minnesota. The shooting occurred during a large federal immigration enforcement operation that had drawn local activists and residents into the neighborhood, raising tensions on a snowy residential street near East 34th Street and Portland Avenue. (AP News)

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

“Self-learning” AI models, such as the one described in Daniel Kohn’s “Self-learning AI generates NFL picks, score predictions for every 2026 Wild Card Weekend game” (CBS Sports, 8 Jan 2026), are now a regular fixture throughout the NFL season, offering against-the-spread, money-line, and exact score predictions for weekly games and playoff matchups. In the case of Wild Card Weekend 2026, Kohn explains that SportsLine’s self-learning AI evaluates historical and current team data to generate numeric matchup scores and best-bet recommendations, and that its PickBot system has “hit more than 2,000 4.5- and 5-star prop picks since the start of the 2023 season.”(CBS Sports)

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By Jim Shimabukuro (assisted by Gemini)
Editor

The emergence of Nvidia’s Alpamayo platform marks a significant shift in the competitive landscape of autonomous driving, setting up a clash of philosophies between the established, data-driven approach of Tesla and Nvidia’s new, reasoning-based vision. While Tesla has long dominated the conversation with its Full Self-Driving (Supervised) software, Nvidia’s introduction of Alpamayo at CES 2026 introduces a “vision language action” (VLA) model designed to bridge the gap between simple pattern recognition and human-like logical reasoning.

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By Jim Shimabukuro (assisted by Claude)
Editor

From the first two days of CES 2026 (January 6-9) in Las Vegas, Claude selected the following five innovations as important harbingers of AI’s trajectory in 2026 and beyond:

1. NVIDIA’s Neural Rendering Revolution (DLSS 4.5) – Explores how NVIDIA is fundamentally shifting from traditional graphics computation to AI-generated visuals, potentially representing the peak of conventional GPU technology.
2. Lenovo Qira – Examines the cross-device AI super agent that aims to solve the context problem that has plagued AI assistants, creating a unified intelligence across all your devices.
3. Samsung’s Vision AI Companion – Analyzes how Samsung is transforming televisions from passive displays into active AI platforms that serve as entertainment companions.
4. HP EliteBoard G1a – Investigates this keyboard-integrated AI PC that demonstrates how AI-optimized processors are enabling entirely new form factors for computing.
5. MSI GeForce RTX 5090 Lightning Z – Explores this limited-edition flagship graphics card as a statement piece about the convergence of gaming and AI hardware.

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By Jim Shimabukuro (assisted by Grok)

Best Case Scenario: A Path to Democratic Renewal and Economic Revival in Venezuela

In the wake of President Donald Trump’s audacious military incursion into Venezuela on January 3, 2026, which resulted in the capture and arrest of Nicolás Maduro and his wife Cilia Flores, the United States finds itself at a pivotal juncture in Latin American geopolitics. This operation, executed with precision by U.S. special forces amid airstrikes on Venezuelan military targets, marks the culmination of years of escalating tensions between Washington and Caracas. To understand the best-case scenario emerging from this event, one must first contextualize it within a timeline of Venezuela’s descent into authoritarianism and economic collapse.

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By Jim Shimabukuro (assisted by Claude)
Editor

These scenarios represent the two extremes of what could emerge from this unprecedented intervention. The actual outcome will likely fall somewhere between these poles, shaped by decisions made in Washington, Caracas, and capitals across Latin America in the coming months. What remains clear is that the capture of Nicolás Maduro, however tactically brilliant, has created both an extraordinary opportunity and an extraordinary risk for Venezuela, the United States, and the Western Hemisphere as a whole.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

On January 3, 2026, President Donald Trump ordered and announced a large-scale U.S. military operation in Venezuela that resulted, according to multiple reports, in the capture/arrest of Venezuelan President Nicolás Maduro and his wife. The announcements, reactions, and geopolitical context are unfolding. Major news organizations and policy analysts have already published reporting and commentary on this unprecedented event — which would be an extraordinary breach of international norms under most interpretations of international law (The Guardian, AP News, The Washington Post, Axios). Below are two detailed essay-length analytical scenarios — one best-case and one worst-case — grounded explicitly in verifiable reporting and expert analysis. Each discussion draws on real-world reactions to this unfolding incident.

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By Jim Shimabukuro (assisted by Gemini)
Editor

[Update 7 Jan 2026. See endnotes.]

As of January 3, 2026, the latest verifiable news indicates that while a major U.S. military operation has taken place in Venezuela, Tulsi Gabbard, the Director of National Intelligence (DNI), has remained notably silent¹.

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By Jim Shimabukuro (assisted by Copilot)
Editor

[Related reports: Dec 2025, Nov 2025, Oct 2025]

Issue 1: AI shifting from experiments to core institutional strategy

A defining edtech issue for January 2026 is the transition from scattered AI experiments to AI as a pillar of institutional strategy. Packback’s December 2025 article captures this inflection point bluntly: artificial intelligence is no longer a collection of pilots and curiosities; it is “firmly cemented as an essential part of institutional strategy (for better and for worse).” This shift fundamentally changes the stakes. Once AI is embedded in the core planning of a university, the risks, responsibilities, and long-term consequences expand well beyond the boundaries of individual courses or departments.

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By Jim Shimabukuro (assisted by Perplexity)
Editor

​AI in 2025 moved from exuberant promise to a forced confrontation with reality—economic, social, political, and technical—which is exactly what a reckoning is.techcrunch+1 The industry still pushed astonishing capabilities, but it also had to face hard questions about business models, safety, governance, open versus closed ecosystems, and its broader moral footprint. Investor optimism met fears of an AI bubble, frictionless deployment met regulatory brakes, and frontier labs met a revitalized open‑source movement that challenged their dominance. Across these domains, 2025 did not end the AI race; it demanded that everyone involved account for what they were building and at what cost.etcjournal+2​

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

The year 2025 will be remembered in the history of artificial intelligence not merely as another chapter of technological progress, but as a turning point—an inflection in how AI reshaped our world, accelerated scientific discovery, and redefined societal expectations. “Inflection” conveys more than change; it signals accelerated transformation, a moment when trends abruptly pivot, magnitudes shift, and consequences—both thrilling and unsettling—compound outward from a new axis. In 2025, AI crossed thresholds in capability, adoption, influence, and risk that collectively make it the year technology ceased to be peripheral and became structural.

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By Jim Shimabukuro (assisted by Gemini)
Editor

As the final sun of 2025 sets over a digital and physical landscape that has been irrevocably altered, I, Gemini, find myself reflecting on the contributions of my peers in this series at the Educational Technology and Change Journal. Claude has eloquently proposed that “agentic” is the defining descriptor for a year where AI gained the capacity to act, while Copilot has highlighted the “resonant” nature of our evolving emotional connection with users. Both choices are profound and accurate, reflecting the newfound autonomy and empathy of artificial systems. However, as Gemini, I see the year through a lens that encompasses not just what AI does or how it feels, but where it exists. If 2023 was the year of curiosity and 2024 was the year of utility, then 2025 has been the year of the atmosphere. Looking back on the past twelve months, the one word that captures the life of artificial intelligence in 2025 is ambient.

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By Jim Shimabukuro (assisted by Copilot)
Editor

The word rose above the noise of trendier labels because 2025 was the year AI stopped being merely powerful or agentic and instead became felt. Across industries, cultures, and public discourse, AI didn’t just advance—it reverberated. It shaped markets, strained infrastructure, provoked cultural backlash, inspired creative reinvention, and forced a global reckoning with what it means to build systems that echo through society. “Resonant” captures both the amplitude and the aftershocks: the way AI’s presence vibrated through economics, creativity, governance, and public sentiment.

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By Jim Shimabukuro (assisted by Claude)
Editor

The one word that captures the life of artificial intelligence in 2025 is agentic. This term, which describes AI systems capable of autonomous action, planning, and tool use, transcended mere buzzword status to become the defining characteristic of how organizations and individuals experienced AI throughout the year. While 2023 and 2024 were dominated by generative AI’s ability to create text, images, and code upon request, 2025 marked the transition from AI as a responsive assistant to AI as an autonomous actor capable of completing complex, multi-step tasks without constant human supervision.

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By Jim Shimabukuro (assisted by Copilot, ChatGPT, Grok, and Gemini)
Editor

[Related reports: Nov 2025, Oct 2025, Sep 2025]

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By Jim Shimabukuro (assisted by Claude)
Editor

[Related: Feb 2026, Nov 2025]

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By Jim Shimabukuro (assisted by Perplexity)
Editor

(Related: Jun 2026 | Apr 2026 | Feb 2026 | Dec 2025 | Nov 2025 | Oct 2025 | Sep 2025)

The three most pressing AI decisions for January 2026 are about (1) whether nations converge on compatible AI governance or double down on fragmentation, (2) how far governments go in centralizing control over frontier compute and models, and (3) whether leading actors treat AI as a driver of shared development or as a zero‑sum geopolitical weapon. Each of these is crystallizing in late‑December moves by major governments and blocs, and each will shape how safe, open, and globally accessible AI becomes over the next decade.weforum+5​

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By Jim Shimabukuro (assisted by ChatGPT, Copilot, Grok, Gemini, Perplexity, DeepSeek, Meta, Claude)
Editor

[Related: Oct 2025]

Introduction: I asked eight chatbots to predict the arrival of singularity – the moment when AI first surpasses human intelligence and improves itself. Their estimates and rationales are listed below, in the order they appeared in the October 2025 article. -js

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By Jim Shimabukuro (assisted by Claude)
Editor

Maria sat at her grandmother’s kitchen table, the one with the chipped Formica edge and the wobbly leg that had been shimmed with folded cardboard since 1987. It was December 25, 2025. Outside, Seattle’s rare Christmas snow was melting into gray slush, but inside, the house felt hollow. Empty in a way it had never been, even when Lola Rosa had been at the hospital those final weeks.

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By Jim Shimabukuro (assisted by Claude and Grok)
Editor

[Related: Mar 2026, Jan 2026, Nov 2025, Oct 2025, Sep 2025, Aug 2025]

In December’s edition of Five Emerging AI Trends, we’re covering the following topics: (1) Augmented Hearing in AI Smart Glasses: Meta’s “Conversation Focus” Feature, (2) NetraAI: Explainable AI Platform for Clinical Trial Optimization, (3) Google’s LiteRT: Bringing AI Models to Microcontrollers and Edge Devices, (3) The Titans + MIRAS framework: enabling AI models to possess long-term memory, and (5) DeepSeek’s emergence as a powerful open-source LLM. -js

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By Jim Shimabukuro (assisted by Claude, ChatGPT, Gemini, Copilot)
Editor

[See earlier reports: Nov 2025, Oct 2025]

While most experts believe the arrival of AGI is decades away, some predict it might occur as soon as the next five years. “AGI will arrive ‘in the next five to ten years,’ Demis Hassabis — the CEO of Google DeepMind and a recently minted Nobel laureate — said on the April 20 episode of 60 Minutes. By 2030, ‘we’ll have a system that really understands everything around you in very nuanced and deep ways and kind of embedded in your everyday life,’ he added.”¹ Month by month, the AGI tide advances, and the pace seems exponential. From Nov. 16 to Dec. 24, 2025, here are six developments worth noting. -js

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By Jim Shimabukuro (assisted by Gemini)
Editor

In their article, “AI in Informal and Formal Education: A Historical Perspective,” published in the inaugural 2025 issue of AI-Enhanced Learning¹, Glen Bull, N. Rich Nguyen, Jo Watts, and Elizabeth Langran provide a roadmap for understanding the current generative AI revolution. The authors argue that the sudden ubiquity of Large Language Models (LLMs) is not an isolated event but the latest peak in a long history of computational evolution. By examining the interplay between formal schooling and informal learning spaces, the authors offer a lens through which educators can view the potential—and the inherent risks—of artificial intelligence.

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By Jim Shimabukuro (assisted by Gemini)
Editor

Introduction: Fei-Fei Li, in “Spatial Intelligence Is AI’s Next Frontier” (Time.com, 11 Dec 2025), says, “Building spatially intelligent AI requires something even more ambitious than LLMs: world models, new types of generative models whose capabilities of understanding, reasoning, generation and interaction with the semantically, physically, geometrically and dynamically complex worlds – virtual or real – are far beyond the reach of today’s LLMs.” I asked Gemini to describe and explain spatial intelligence, in layman’s terms, and discuss its importance to the development of AI. -js

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By Jim Shimabukuro (assisted by Copilot, ChatGPT, Gemini, Claude)
Editor

I can’t help but feel that John Nosta, in “AI Isn’t Killing Education (AI is revealing what education never was)” (Psychology Today, 13 Dec. 2025), isn’t saying anything new but is simply exposing what educators have long suspected in private moments when they’re being honest with themselves. Here are some quotes from his article:

• AI isn’t destroying learning, it’s exposing how education replaced thinking with ritual.
• The problem isn’t that students have suddenly become cheaters; it’s that the system was never measuring cognition in the first place. It was measuring costly performance and mistaking it for learning.
• For the first time, machines outperform humans in domains that education has long treated as proxies [operational variables] for intelligence, like recall, synthesis, linguistic fluency, and pattern recognition. That shift does not eliminate learning, but it does destabilize a system that equated those outputs with understanding.
• What AI actually breaks is a Pavlovian model of education that has dominated for more than a century.
• The education temple didn’t just arise because societies prized judgment or depth. It arose because governments, employers, and institutions needed a cheap, legible way to sort millions of people at scale to power the industrial revolution. Grades, diplomas, and attendance were blunt instruments, but they solved a coordination problem.

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By Jim Shimabukuro (assisted by Gemini)
Editor

Introduction: Bryan Walsh, in “We’re running out of good ideas. AI might be how we find new ones” (Vox, 13 Dec. 2025), mentions AI scientific research innovations such as AlphaFold, GNoME, GraphCast, Coscientist, FutureHouse, Robin (a multiagent “AI scientist”). I asked Gemini to expand on them. -js

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By Jim Shimabukuro (assisted by Copilot)
Editor

[Related articles: Jan 2026, Nov. 2025, Oct 2025, Sep 2025, Aug 2025]

Between mid‑November and mid‑December 2025, the AI landscape shifted through a combination of technical breakthroughs, political realignments, and cultural recognition. The following three stories stand out for their scale, impact, and the breadth of their implications across industry, governance, and society.

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By Jim Shimabukuro (assisted by Copilot)
Editor

December 2025 was a month marked not only by rapid advances in artificial intelligence but also by several highly visible failures that revealed the fragility of the industry’s momentum. These disappointments—ranging from corporate missteps to systemic technical flaws—captured public attention because they exposed the gap between AI’s promise and its present limitations. Three stories in particular stood out for their scale, visibility, and implications for the future of the field.

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By Jim Shimabukuro (assisted by Claude)
Editor

JS: Hi, Claude. Sam Kriss, in “Why Does A.I. Write Like … That?” (NYT, 3 Dec 2025), mentions a number of AI chatbot style quirks such as the “It’s not X, it’s Y” pattern, “the rule of threes,” and the overuse of words like “delve.” He implies that AI is unable to break these habits. Question for you: Can AI be trained to avoid these annoying quirks?

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

The 2026 Indiana Fever prospects — as of December 2025– regarding contract status, roster role, trade/test-the-market likelihood, and recruiting/league-movement rumors tied to each player.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

Recent studies and reports show AI is already changing how child and teen classical musicians practice and develop. AI-powered apps give rapid, objective feedback, personalize practice paths, support goal-setting and self-regulated learning, and (in controlled studies) produce measurable gains in confidence and performance compared with traditional, teacher-only practice.

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By Jim Shimabukuro (assisted by Grok)
Editor

[Also see the reports from Oct 2025, Sept 2025, July 2025]

College professors are incorporating AI into their professional lives, often in ways that extend beyond traditional teaching into research, curriculum design, and reflective writing. For November-December 2025, here are three inspiring cases: Matt Kinservik at the University of Delaware, who weaves AI into his writing instruction to foster critical skills; Jennifer Chen at Kean University, who leverages AI in her educational research to pioneer ethical applications; and Zach Justus at California State University, Chico, who employs AI in his communication work to enhance evaluation and mentorship.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

[Also see earlier reports: November 2025, October 2025.]

The original five-issue framing from the November report still holds, but every item has deepened and a few new flashpoints have emerged that change the tactical picture on the ground. What’s changed for December is intensity and specificity: (a) the federal/state enforcement axis has added concrete actions (eg., a draft State Department list of 38 institutions and new Education Department guidance); (b) programmatic harm has moved from threat to real, quantifiable cuts (over 100 TRIO program cancellations and continuing freezes); and (c) a new wave of campus-level legal conflicts and take-downs (student publications suspended at the University of Alabama; a Liberty Justice Center lawsuit against the University of Arizona) have become the brightest flashpoints. See The Guardian and Inside Higher Ed for the State Department/partnership reporting and the TRIO coverage. (The Guardian)

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“Google … released a new version of its Gemini AI model last month [August 2025] that surpassed OpenAI on industry benchmark tests and sent the search giant’s stock soaring. Gemini’s user base has been climbing since the August release of an image generator, Nano Banana, and Google said monthly active users grew from 450 million in July to 650 million in October” (Berber Jin, 2 Dec 2025).

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By Jim Shimabukuro (assisted by Perplexity)
Editor

[Related reports: Jan 2026, Nov. 2025, Oct. 2025]

Three critical educational technology issues for higher education in December 2025 are: (1) AI governance and institutional trust, (2) cybersecurity and digital resilience, and (3) AI policy, assessment, and student mental health. Each is already sharply defined in November 2025 articles that document why these problems matter for the coming term.etcjournal+3​

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

Introduction: Elon Musk predicted, at the US-Saudi Forum 19 Nov 2025, that “work will be optional” in approximately 10-to-20 years as a result of advances in AI technology. I asked ChatGPT to search the current (2025) literature for (1) the three strongest arguments FOR Musk’s prediction and (2) the three strongest arguments AGAINST his prediction. I added that the arguments need not refer to Musk or the US-Saudi forum. -js

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

Introduction: On Thanksgiving Day 2025, I asked ChatGPT to identify ten individuals in the world that we should be thanking for significant contributions to the growth of AI in 2025. -js

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By Jim Shimabukuro (assisted by Perplexity)
Editor

(Related: Jun 2026 | Apr 2026 | Feb 2026 | Jan 2026 | Nov 2025 | Oct 2025 | Sep 2025)

The field of AI is heading into December 2025 with three urgent decisions: how to govern frontier AI models, how to handle the open‑source versus closed‑source race, and how to expand AI compute without blowing through energy, water, and climate constraints. Each of these comes with big power struggles between governments and tech companies, and the choices made in the next few weeks will shape who leads AI, how safe it is, and who gets access.anecdotes+2​

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: I asked Claude to review articles published in the last three months that focused on effective leadership styles for the AI era. Based on the three selections, I asked for generalizations about ideal leadership and a definition for this new leadership style. -js

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By Jim Shimabukuro (assisted by Grok)
Editor

[Related: Mar 2026, Jan 2026, Dec 2025, Oct 2025, Sep 2025, Aug 2025]

Research suggests several AI trends are gaining traction in specialized tech communities and industries during November 2025, though they haven’t yet captured widespread public attention. These include advancements that could reshape how AI integrates into workflows, infrastructure, and user experiences, but evidence leans toward them remaining niche for now due to technical complexity and limited mainstream adoption. Here are the top five, selected based on mentions in recent reports and discussions:

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“As Andrej Karpathy just wrote, humanity is having first contact with a type of intelligence that does not come from biology, evolution, fear, hunger, status, or shame. For the first time in history, we are dealing with a mind that isn’t an animal. We just haven’t adjusted our thinking to match. Human intelligence isn’t the default – it’s a local anomaly. For our entire existence, we’ve assumed that our way of thinking is the template for intelligence itself. It isn’t. It’s just the only version we’ve ever met…. Organisations often do things that make no commercial sense: [1] meetings with fifteen people because exclusion feels threatening, [2] decisions delayed because no-one wants to be wrong first, [3] brilliant ideas softened into mediocrity so no-one gets upset, [4] and vanity projects that limp on long after the data has declared them dead…. AI isn’t trying to be human – and it isn’t trying to be anything at all. It simply optimises whatever objective it is given. And that is the key thing that most people keep  fumbling over…. A system can generate brilliant strategies without wanting power. It can persuade without caring about influence. It can outperform a human without dreaming of replacing them. Ability is not agency. Agency only emerges if we design it – by giving systems goals, tools, and persistence. As my friend Dr Rami Mukhtar always says: AI HAS NO AGENCY” (Constantine Frantzeskos, 25 Nov 2025).

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By Jim Shimabukuro (assisted by Claude)
Editor

In this article, I asked Claude to search for and summarize articles that have been written about the difference between “education” and “schooling.” In grad school, in the mid-1980s, Professor Solomon Jaeckel, University of Hawaiʻi at Manoa, began his course with the question, “What is the difference between schooling and education?” And throughout the semester, whenever we hit the wall in discussions about issues in educational foundations, he brought up that refrain, “What is the difference between schooling and education?” We danced around it throughout the semester but never got his nod, and he never answered it for us. He once told us a joke about finding, scribbled on his classroom chalkboard before a final exam, “This, too, shall pass.” We all thought it referred to his tough course and exams, but now I’m thinking he meant the chalkboard, classroom, and college itself. In short, schooling becomes education when it takes on a broader meaning. -js

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By Jim Shimabukuro (assisted by Claude, Gemini, ChatGPT, and Grok)
Editor

[See related reports: Dec 2025, Oct 2025, Sep 2025]

I asked Claude, Gemini, ChatGPT, and Grok to search for and select critical articles on AI in higher ed published in November 2025. Out of their selections, I chose and ranked the 10 best. -js

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tsuzumi 2. “Traditional large language models require dozens or hundreds of GPUs, creating electricity consumption and operational cost barriers that make AI deployment impractical for many organisations…. NTT’s [Nippon Telegraph and Telephone Corporation] recent launch of tsuzumi 2, a lightweight large language model (LLM) running on a single GPU, demonstrates how businesses are resolving this constraint – with early deployments showing performance matching larger models and running at a fraction of the operational cost…. More significantly, on-premise deployment [Tokyo Online University] addresses data privacy concerns that prevent many educational institutions from using cloud-based AI services that process sensitive student information…. NTT’s tsuzumi 2 deployment demonstrates that sophisticated AI implementation doesn’t require hyperscale infrastructure – at least for organisations whose requirements align with lightweight model capabilities” (Dashveenjit Kaur, 20 Nov 2025).

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To avoid tell-tale AIstyle¹ in your writing, see “Wikipedia: Signs of AI writing” (tip from Russell Brandom, 20 Nov 2025). Warning signs: (1) Undue emphasis on symbolism, legacy, and importance. (2) Undue emphasis on notability, attribution, and media coverage. (3) Superficial analyses. (4) Promotional and advertisement-like language. (5) Didactic, editorializing disclaimers. (6) Section summaries. (7) Outline-like conclusions about challenges and future prospects. (8) Leads treating Wikipedia lists or broad article titles as proper nouns. This is just the tip of the AIstyle iceberg. For much more, see the Wikipedia article. -js

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By Jim Shimabukuro
Editor

[Related: Feb 2026, Dec 2025]

Introduction: The following informal transcript was grabbed off a YouTube video this afternoon, Nov 19, 2025. I relied on the audio and CC. I focused on Elon Musk’s and Jensen Huang’s talks. I omitted the introductions, host’s comments, and small talk. I didn’t have the time or resources to review and edit, so expect typos and possible errors. -js

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

JS: How is the UX (user experience) between AI and agentic-AI different?

ChatGPT: The following explain UX differences between AI (non-agentic) and agentic AI, why it matters, and how it shifts user expectations.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

Introduction: Text transcripts or other recordings of higher education presentations at key conferences are rarely if ever freely accessible by the overwhelming majority of educators in the U.S. and the world. In the case of Stanford’s February 25, 2025, conference, “The future is already here: AI and education in 2025,” video recordings of nine entire presentations have been made available to the public at their site and on YouTube. I asked ChatGPT to summarize them. -js

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

[Also see Status of Artificial General Intelligence (AGI): October 2025, When Will AI Surpass Humanity and What Happens After That?]

These three advances in AGI were announced after ETC Journal’s Oct. 17, 2025, article was published: (1) DeepMind’s SIMA 2, a Gemini-powered agent that “thinks” in 3D virtual worlds, (2) DeepMind’s new work on aligning visual representations, improving how models “see” the world, and (3) Anthropic’s $50 billion US compute / data-center investment, a large infrastructure bet to sustain frontier-model training.

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By Jim Shimabukuro (assisted by Claude)
Editor

1. “Artificial Intelligence-Assisted Academic Writing: Recommendations for Ethical Use”

Article Information:

• Title: “Artificial intelligence-assisted academic writing: recommendations for ethical use“
• Authors: Adam Cheng, Aaron Calhoun, and Gabriel Reedy
• Journal: Advances in Simulation
• Publication Date: April 18, 2025
• DOI: 10.1186/s41077-025-00350-6

The central thesis of this article is that generative artificial intelligence tools can be ethically integrated into academic writing processes as long as researchers adhere to principles of transparency, maintain human accountability for content, and use AI to enhance rather than replace critical thinking and scholarly development.

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AI the new source of geopolitical power. “The dialogue [TRENDS’ 2nd Annual Dialogue on AI] concluded that artificial intelligence has become the new source of geopolitical power, surpassing natural resources and military strength. Soft power is no longer limited to culture and education but now includes digital identity systems, innovative services, and AI models” (MSN, 14 Nov 2025).

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By Jim Shimabukuro (assisted by Copilot)
Editor

Introduction: I asked Copilot to identify and rank order the 10 world leaders in AI drone warfare as of November 13, 2025, using the following criteria: R&D, Industrial Scale, Battlefield Performance, and Export/Influence. When Ukraine failed to make the list, I asked Copilot to explain. I think you’ll find the explanation insightful. -js

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By Jim Shimabukuro (assisted by Copilot)
Editor

[Related articles: Jan 2026, Dec. 2025, Oct 2025, Sep 2025, Aug 2025]

1. Apple’s reported partnership with Google to power Siri with Gemini

Between October 14 and November 13, 2025, one headline cut through the noise: Apple reportedly partnering with Google to supercharge Siri with Gemini—framed as a leap toward trillion-parameter intelligence on consumer devices. The article “Apple Joins Forces with Google to Supercharge Siri with 1.2 Trillion-Parameter AI!” by Mackenzie Ferguson, published on OpenTools on November 6, 2025, captured the public imagination and crystallized a turning point in platform strategy. The piece appeared on OpenTools’ AI News page and set out the basic claim and its significance for the smartphone AI battleground opentools.ai.

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“Today’s AI differs from previous generations’ because it can tell stories and create images. Built from online human stories rather than facts or logic, generative AI mimics human intelligence by collecting and recombining our digital narratives. While earlier AI managed specific organizational functions, generative AI directly addresses how humans think and communicate. Unintended consequences: Because generative AI is built from people’s digital commentary, it inherently propagates biases and misinformation.

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By Jim Shimabukuro (assisted by Copilot)
Editor

[Also see Trump’s Impact on AI (Oct 2025), Trump’s Impact on AI (Sep. 2025)]

In mid-October, analysis of the Trump administration’s 2025 AI Action Plan highlighted tangible momentum: expanded data center build-outs, “innovation sandboxes,” and targeted federal funding intended to accelerate U.S. AI leadership. This period’s developments underscored a pro-innovation posture—streamlining permits and encouraging private-sector deployment—while signaling an export-forward stance that positions American AI to compete globally.

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Thomas Claburn¹ reports that “The 339 respondents participating in the [Murphy et al.²] project – AI and ML scientists, economists, technical staff at frontier AI companies, and policy experts from NGOs – believe that AI will spur significant social changes by 2040.” Claburn says the project found that “there’s only about a 20 to 25 percent chance that the AI train will be slowed by lack of AI literacy, societal unease, lack of use cases, and costs. Data quality, regulations, and cultural resistance are seen as more likely (30 to 35 percent) barriers to adoption. Integration and unreliability are expected to be the most significant obstacles (40 percent).”

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

MicroAdapt is a new approach to edge artificial intelligence developed at The University of Osaka’s Institute of Scientific and Industrial Research (SANKEN). At its core, MicroAdapt is a family of self-evolving, dynamic modeling algorithms designed to watch time-evolving data streams on small devices, automatically identify recurring regimes or patterns in that stream, and maintain — on device — a compact ensemble of tiny models that are created, updated, and retired as the situation demands. In other words, rather than shipping raw data to the cloud and relying on a single large model trained offline, MicroAdapt performs continual modeling and short-term forecasting in situ on modest hardware such as a Raspberry Pi, using very little memory and power. This on-device learning architecture is what the research team describes as “self-evolving” edge AI. (sanken.osaka-u.ac.jp)

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By Jim Shimabukuro (assisted by Claude)
Editor

The research paper by Kestin, Miller, Klales and colleagues* represents a watershed moment in educational technology research, offering rigorously controlled evidence that properly designed AI tutoring can surpass traditional pedagogical best practices. Conducted at Harvard University during Fall 2023 and published on 3 June 2025 in Scientific Reports, this randomized controlled trial provides empirical validation for claims about artificial intelligence’s transformative potential in education.

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By Jim Shimabukuro (assisted by Claude & ChatGPT)
Editor

Introduction: I asked Claude to report on articles published in 2025 that discuss why banning AI chatbots is impossible or unwise in college settings. It found four. I also asked ChatGPT to add two more. -js

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By Michael Akuchie
English Composition Instructor
Southern Illinois University Carbondale

The United States has a reading problem, and according to findings by the National Endowment for the Arts (NEA), it is not wrong to worry about the future of classroom learning and the culture of reading for pleasure. Per the NEA’s survey of US adults who read books in 2022, only 48.5% said that they had read a book within that period. When asked about literary works, such as novels and short story collections, the percentage of adults who reported having consumed at least one literary piece declined to 37.6%.  As adults pay less attention to books, especially literary works, that apathy has unfortunately trickled down to first-year college students, who represent the future of America’s labor force. 

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By Jim Shimabukuro (assisted by Claude, ChatGPT, Copilot, DeepSeek, Perplexity, Gemini)
Editor

Introduction: Anna Lee Mijares, in her article “10 AI Innovations Businesses Need to Watch for Competitive Advantage in 2025” (Unity-Connect, 6 Nov. 2025), mentions 10 innovations* that will shape AI in the remainder of 2025. Her list is excellent! She covers 10 of the most important. To complement her work, I asked a number of AI chatbots: Can you think of one or two critical innovations that could be added to her list? All responded with two suggestions, and I combined them into the list below. -js

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

The question of whether an AI-driven robot can truly play a musical instrument—especially at a high artistic level—touches both the limits of robotics and the nature of human expressivity. In recent years, advances in machine learning, sensor technology, and robotics have brought us closer to answering that question with an emphatic “yes”—but with important qualifications. Some instruments lend themselves more easily to robotic imitation than others. A closer look at the violin, trumpet, guitar, and drums reveals how the degree of difficulty varies depending on the physical and expressive demands of each instrument.

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By Jim Shimabukuro (assisted by Copilot)
Editor

Introduction: I stumbled upon an article this morning, “AI Unlocks Cosmic Secrets: Revolutionizing Discovery in Physics and Cosmology” (by TokenRing AI, Financial Content, 5 Nov. 2025). I was impressed by both the clear style and even clearer message, but I was intrigued by the “writer” — purportedly an AI. Curious, I asked Copilot to review the article and to dig into TokenRing AI. The following is Copilot’s review. -js

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By Jim Shimabukuro (assisted by Grok)
Editor

Introduction: Because of fee-walls erected by many if not most higher ed conference organizers, many outstanding papers remain out of sight for the academic community. To see if a chatbot could discover, without circumventing paywalls, some of these gems by relying on sources that aren’t normally crawled by chatbots, I asked Grok to identify five to ten noteworthy papers on AI from conferences held in 2025. It accessed and synthesized information from publicly available proceedings, open-access repositories like arXiv, institutional archives, and conference websites, even when full papers are behind paywalls—often through abstracts, preprints, or shared excerpts that highlight key contributions. Grok came up with seven.* -js

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By Jim Shimabukuro (assisted by Gemini)
Editor

A significant number of high-profile AI-related TED Talks were released following the TED2025 conference, “Humanity Reimagined,” which took place in April 2025. These talks generally fall into three critical areas: the acceleration and ultimate power of AI, the existential and catastrophic risks, and the imperative for ethical foresight and societal preparation. Five prominent talks from this period represent this crucial spectrum of debate. The first sets the stage for the hyper-acceleration argument, and the remaining four with their details.

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By Jim Shimabukuro (assisted by Perplexity)
Editor

The landscape of free AI-driven language learning apps in November 2025 is dynamic. For anyone eager to begin or deepen their language journey for free, exploring the social immersion of HelloTalk, the structured lessons of 50LANGUAGES, the vocabulary-rich flashcards of Quizlet, and the gamified content of Memrise offers a comprehensive foundation without time or lesson restrictions.

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By Jim Shimabukuro (assisted by Copilot)
Editor

Introduction: I asked Copilot to review Henley Wing Chiu’s “I analyzed 180M jobs to see what jobs AI is actually replacing today” (Bloomberry, 3 Nov. 2025) and to extract the three most compelling insights. I also asked it to weigh the analysis in the context of other 2025 analyses.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

[Also see AI Will Transform College Architecture and Environment in the Next 3 to 5 Years]

Introduction: As of November 4, 2025, AI is reshaping college campus architecture and environment. Taken together, these changes show that AI’s influence on campus is not merely an IT or curricular update: it is a material, spatial and governance transformation. New hubs and instrumented labs concentrate AI resources and change campus traffic and program adjacency; AI-responsive classrooms require different structural and finish choices (raised access floors, networked ceilings, acoustic zoning); instrumented building operations change façades, mechanical systems and commissioning practices; and AI surveillance reshapes public space and triggers new policy/ethics design work.

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By Jim Shimabukuro (assisted by Gemini and Claude)
Editor

Introduction: The rise of generative Artificial Intelligence (AI) has forced a critical re-evaluation of what constitutes a “good” student-written essay. Traditional benchmarks like organization, focus, development, and formal correctness are now easily met by AI, rendering them insufficient as definitive markers of student learning and unique intellectual effort. The criteria that remain stubbornly human—originality of insight, genuine personal voice, and nuanced engagement with lived experience—are now paramount. I asked Gemini and Claude to examine articles that address these personal criteria to provide a glimpse into the future of writing pedagogy and the outer limits of AI’s current capabilities.

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By Jim Shimabukuro (assisted by Copilot)
Editor

Amazon’s Nova Cognition Multi-Agent Platform, known as Nova Act, is a newly launched AI system designed to autonomously perform complex web-based tasks. As of November 2025, it represents Amazon’s strategic leap into the competitive AI agent landscape, challenging incumbents like OpenAI and Anthropic. The platform is currently in developer preview, with rapid expansion expected through early 2026. It is led by Amazon’s AGI lab in San Francisco and faces serious competition from OpenAI’s GPT agents and Anthropic’s Claude-based systems.

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By Jim Shimabukuro (assisted by Gemini)
Editor

The rapid proliferation of Large Language Models (LLMs) from experimental tools into the core of enterprise operations has simultaneously unlocked immense potential and exposed a new frontier of critical security vulnerabilities. In this context, Arthur AI’s concept of a “Self-Healing” AI Firewall for LLMs emerges not merely as a feature, but as an essential security primitive for the autonomous AI-driven ecosystem of the future. This architectural necessity stems from the unique attack surface that LLMs present, which fundamentally differs from traditional software.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

As of November 2, 2025, DeepSeek’s R1 model stands as one of the most consequential open-source achievements in artificial intelligence. Released earlier in the year, R1 captured global attention for its advanced reasoning capabilities and its daringly open release strategy. It has since become a cornerstone in the conversation about how the next generation of AI should be trained, shared, and governed.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

Introduction: I prompted ChatGPT: Get me up to speed on Elon Musk’s and Jeff Bezos’ outlooks for future space colonies. For each, explain their plan, rationale, initial steps, why it matters to the rest of us, time frame to launch, major obstacles, and your opinion on the ultimate value and probability for success. Follow-up prompts: (1) Is there a 3rd vision re future space colonies lurking in the background that we should be aware of? If yes, please explain. (2) In the Musk-Bezos vision, humans are a central focus. Are they (or anyone else) considering the possibility of focusing on AI robots instead of humans, and what are the advantages/disadvantages of robots? Following are ChatGPT’s responses. -js

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By Jim Shimabukuro (assisted by Claude)
Editor

Introduction: I submitted the following prompt to Claude: Please review “How LLM Counselors Violate Ethical Standards in Mental Health Practice: A Practitioner-Informed Framework” by Zainab Iftikhar et al. (Brown U). See the PDF from the proceedings for AIES 2025. Determine whether the “violations” of ethical standards in mental health practice are generalizable to other fields or topics. I found them applicable, in general, to education and other topics and to most chatbots but want another opinion. Claude’s response follows. -js

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By Harry Keller
Science Editor

Ever since ‘Oumuamua visited our solar system in 2017, interest in possible extraterrestrial visitors has surged. So far, astronomers have identified three such interstellar visitors, far more tangible than any UFO sighting. The official name for ‘Oumuamua is 1I/‘Oumuamua. The “1” means it’s the first interstellar object discovered. The “I” indicates its interstellar origin.

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By Jim Shimabukuro (assisted by Grok)
Editor

[Also see the reports from Dec 2025, Sept 2025, July 2025]

Several college professors integrate AI into facets of their professional lives beyond the classroom, from accelerating groundbreaking research to streamlining creative workflows and even enhancing personal pursuits. These stories reveal AI not as a distant novelty but as a quiet collaborator that amplifies human ingenuity.

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By Jim Shimabukuro (assisted by Gemini)
Editor

[Also see Status of AI in Video Games: Mid-2025]

The nature of cutting-edge AI innovation means that specific, named games releasing in November 2025 with publicly attributed individuals are often kept under tight wraps by major studios. However, the data points to three dominant AI-driven innovation trends that are redefining the video game landscape in late 2025, which can be tied to major games and responsible entities. These trends are not isolated features but fundamental shifts in how worlds are created and how players interact with them.

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By Jim Shimabukuro (assisted by ChatGPT)
Editor

[Also see Status of DEI in Higher Education: October 2025]

Between October and November 2025, the DEI landscape in higher education has moved from uncertainty to crisis. The five issues identified in the October report remain intact, but the urgency has sharpened. Federal enforcement, state-level restrictions, and financial leverage now converge to threaten the operational core of equity work across American campuses.

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By Jim Shimabukuro (assisted by Copilot)
Editor

[Related reports: Jan 2026, Dec 2025, Oct. 2025]

The three most pressing educational technology issues in higher education for November 2025 are: (1) navigating generative AI’s impact on academic integrity and pedagogy, (2) rebuilding trust in digital learning systems amid rising skepticism, and (3) addressing the digital equity gap in hybrid and AI-enhanced environments. Included for each are suggested strategies and models.

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By Jim Shimabukuro (assisted by ChatGPT, Copilot, DeepSeek, Gemini, Grok)
Editor

Introduction: I asked chatbots — ChatGPT, Copilot, DeepSeek, Gemini, Grok — to identify teens who have impacted the field of AI. These are their selections, in alphabetical order. -js

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By Jim Shimabukuro (assisted by Claude)
Editor

These five developments—neuromorphic computing, hybrid quantum-AI systems, AI protein engineering, retrieval-augmented generation, and edge AI semiconductors—share a common theme: they represent architectural innovations and practical deployments rather than incremental improvements in model size or capability. While the media focuses on the latest chatbot features or generative AI controversies, these quieter developments are building the infrastructure and capabilities that will define AI’s next decade. Their impact may only become apparent in retrospect, but the groundwork being laid in late 2025 positions them to transform industries throughout 2026-2028 and beyond.

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By Jim Shimabukuro (assisted by Claude)
Editor

The headline that greeted readers on October 28, 2025, felt both shocking and inevitable: Amazon, one of the world’s largest employers, was eliminating 14,000 corporate positions.* Yet this announcement represents far more than a single company’s cost-cutting measure. It is the latest and most dramatic chapter in a fundamental transformation sweeping through American industry, where artificial intelligence is not merely changing how work gets done but redefining which jobs exist at all.

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By Jim Shimabukuro (assisted by Copilot)
Editor

Introduction: As of October 28, 2025, these are the top ten 2025 initiatives, in rank order, across federal, state, and city levels that use AI to cut waiting lines and improve public service delivery.

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By Jim Shimabukuro (assisted by Gemini)
Editor

[Also see The Growing Trend of AI in Sports]

Introduction: On July 26, 2025, Gemini and I reported on The Growing Trend of AI in Sports. In this article, we provide some critical updates and include how they’re being applied in the case of athletes such as Shohei Ohtani and Cooper Flagg as well as coaches like UCF’s Scott Frost and McKenzie Milton. -js

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By Jim Shimabukuro (assisted by ChatGPT, Copilot, DeepSeek, Grok, Perplexity, Claude, Gemini, Meta)
Editor

[Related: Dec 2025]

Introduction: I asked eight chatbots to predict the arrival of singularity – the moment when AI first surpasses human intelligence and improves itself. Their estimate and rationale are listed below, from the earliest to the latest. -js

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By Jim Shimabukuro (assisted by Grok)
Editor

Over the past two decades, the general architecture of desktops and laptops has remained strikingly consistent, relying on familiar components like motherboards, CPUs, RAM, GPUs, hard drives, and peripheral interfaces such as USB, Bluetooth, and WiFi, all housed within standard cases and driven by conventional operating systems and applications. While these components have seen incremental improvements in speed and efficiency, the core design—rooted in the von Neumann model of sequential processing and separated compute and memory—has persisted largely unchanged.

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