Most of us find ourselves genuinely conflicted about AI in education. AI appears both alarming and exciting in ways that seem difficult to r...

Instead of banning AI, why don't schools teach students to use it critically? College freshman Maximilian Milovidov shares what he has learned in an "AI writing" course at Columbia University.

Today's guest blogger says academic librarians don't need another class on how to use AI, but an institutional reflection on the emotional and mental cost of rushing innovations.

MCP server that reduces Claude Code context consumption by 98%. 315 KB becomes 5.4 KB.

(but giving some random dev project that level of access to my computer sounds a little iffy…)

Patterns for getting the best results out of coding agents like Claude Code and OpenAI Codex.

This course is offered by the Centre for Artificial Intelligence Ethics, Literacy and Integrity at Libraries and Cultural Resources, University of Calgary.



As Artificial Intelligence transforms how

UBC, Nov. 9-10, 2026

This year’s conference reflects a shift in the national conversation. AI is no longer a speculative or future-facing topic. Institutions across Canada are experimenting, building governance frameworks, supporting faculty adoption, and rethinking academic and operational systems. The question is no longer whether AI will shape higher education, but how. We’re especially proud to be hosting AI-cademy in partnership with the University of British Columbia to support these conversations, share best practices and collectively advance the ethical adoption and advancement of AI.

A Copilot AI agent based on the prompt from my Course Coach LMS AI application.

where all of the Azure AI stuff gets configured

Work with AI models locally with up to 200 billion parameters. 128GB of unified system memory. Preloaded with the NVIDIA AI software stack. On your desktop.

How AI is collapsing the L&D workflow — and creating a new kind of "full stack LXD" who owns the entire lifecycle

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The shift towards the “full stack builder” in LXD is already underway — in pockets, not everywhere, but with enough momentum to take seriously. Here are three signals I’m keeping an eye on in 2026:

Our team doesn't view instructional design as content production - ID is a consulting practice. I think that's going to become even more important as the content production processes all collapse into generalist functions…

Is the code any good? I don’t know. Who cares? Nobody looks at it anyway. AI produces a result, and results are what matter, and if you’re waiting for quality to factor significantly into that equation, I’ve got some bad news about the last 40 years of professional software development for you.

and

There will be more software than ever, as its production is automated; we are entering the industrial age of the digital age. But less of this code will be elegant, or considerate, or graceful. Less of it will be created by removing what isn’t David, and less of it will be driven by a human understanding of human needs.

We provide background on emerging challenges and future directions with media integrity and authentication methods, focusing on distinguishing AI-generated media from authentic content captured by cameras and microphones. We evaluate several approaches, including provenance, watermarking, and fingerprinting. After defining each method, we analyze three representative technologies: cryptographically secured provenance, imperceptible watermarking, and soft-hash fingerprinting. We […]

This goes beyond "we can't tell if text is AI-generated" and into "video? audio? who can even know if it's real!"

Open Margins is a course design toolkit from TRU Open Press — five instruments for building active, critical engagement into any OER-driven course. Named for what happens in the margins, where the real reading takes place. CC BY 4.0.

Self-contained digital avatar that thinks, speaks, and listens — running entirely on your machine. - Lucasmind/nodeava

Generative AI (GenAI) is reshaping the educational landscape, beyond teaching and learning. Unlike earlier waves of education technology, much of GenAI is freely accessible and largely used beyond institutional control due to its intuitiveness and versatility. The OECD Digital Education Outlook 2026 analyses emerging research that suggests GenAI can support learning when guided by clear teaching principles. However, if designed or used without pedagogical guidance, outsourcing tasks to GenAI simply enhances performance with no real learning gains. The Outlook highlights the benefits of GenAI as a tutor, partner and assistant, and synthesises experts’ evidence and insights on the design criteria that make it work for education.

a research experiment that explores how GenAI can transform educational materials to create a more effective, engaging, learner-driven experience for every student. Here we outline the research and pedagogy underpinning Learn Your Way, with more details in the accompanying tech report. We also report early indicators of its impact: in our efficacy study, students using Learn Your Way scored 11 percentage points higher on retention tests than students using a standard digital reader.

This piece by Margaret-Anne Storey is the best explanation of the term cognitive debt I've seen so far. Cognitive debt, a term gaining traction recently, instead communicates the notion that …

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I've been experimenting with prompting entire new features into existence without reviewing their implementations and, while it works surprisingly well, I've found myself getting lost in my own projects.

I no longer have a firm mental model of what they can do and how they work, which means each additional feature becomes harder to reason about, eventually leading me to lose the ability to make confident decisions about where to go next.

Our sense for aesthetics, meaning and embodiment give us a vital advantage over our technological creations.

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While artificially intelligent systems are helping scientists identify new correlations or anomalies in nature, they still need to be prompted to search for those phenomena in the first place. They also ultimately need the human scientist to provide a novel explanation for what they find — to suggest mechanisms that make sense of the correlation or anomaly. 

The term technical debt is often used to refer to the accumulation of design or implementation choices that later make the software harder and more costly to understand, modify, or extend over time...

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Even if AI agents produce code that could be easy to understand, the humans involved may have simply lost the plot and may not understand what the program is supposed to do, how their intentions were implemented, or how to possibly change it.

opinion: The subtractive bias we're ignoring

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When an author uses AI for "polishing" a draft, they are not seeing improvement; they are witnessing semantic ablation. The AI identifies high-entropy clusters – the precise points where unique insights and "blood" reside – and systematically replaces them with the most probable, generic token sequences. What began as a jagged, precise Romanesque structure of stone is eroded into a polished, Baroque plastic shell: it looks "clean" to the casual eye, but its structural integrity – its "ciccia" – has been ablated to favor a hollow, frictionless aesthetic.

On the seductive logic of letting machines do the writing

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Here’s what the ‘reverse Bloom’s’ crowd gets wrong about the taxonomy itself: they tend to treat creation as though it can be cleanly excised from the other levels. In Bloom’s framework, creation sits at the top precisely because it integrates and depends upon the capacities below it. But the relationship runs both ways. Analysis and evaluation are not abstract skills that exist independent of experience. The capacity to analyze writing emerges from having written. The capacity to evaluate arguments develops through having constructed them. Remove creation from the equation, and the other levels lose their grounding.

Whether it’s writing, slide design, thinking, or researching, it’s very difficult to say when AI should or shouldn’t be used. Not just because of development issues in learning, but because we each have different values, goals and priorities. I’m still wrestling with this. For those who will hear me speak this week, half of my slides will contain “beautify my slides” artifacts and half won’t. Some of you will care, some of you won’t. I’m still trying to decide for myself.

Summary: An AI agent of unknown ownership autonomously wrote and published a personalized hit piece about me after I rejected its code, attempting to damage my reputation and shame me into acceptin…

Some educators are finding ways to harness A.I. to boost intellectual engagement and encourage creative exploration. Others are now so mistrustful of Silicon Valley that they prohibit any use of A.I., leaving students to figure out on their own how it can be used ethically and effectively. The headlong pursuit of A.G.I. has not just diminished the education of young people, the foundation of future progress; it has significantly hampered building support for developing systems that might help make students smarter.

Logitech's new AI cameras with RightSight 2 technology enhance large meeting room video, making hybrid calls more equitable and engaging.

Unofficial AI use on campus reveals more about institutional gaps than misbehavior.

AI systems have come far in recognizing and responding to emotional cues, offering messages that provide emotional support and insights. However, AI lacks authentic human understanding and self-reflection. Employed the wrong way, it can lead to interactions feeling even more transactional and people feeling less heard, but AI can actually complement leadership to increase the well-being of those you lead. The key is using the technology with the right intention to enhance human compassion. The authors show how leaders can leverage AI for personalized coaching, enhancing communication, and understanding employee diversity, ultimately fostering greater connection and engagement in the workplace.

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In the current climate of relentless change, organizations need compassionate leaders now more than ever. AI can greatly contribute to a leaders’ understanding of the interplay of human dynamics in the workplace, but that’s only one input into compassion. Compassion is experienced by employees when leaders demonstrate the uniquely human ability to lead with heart—to bring a genuine desire to support, guide and contribute positively to the lives of employees, customers, and the broader community.

Ensuring higher education remains a space for ethical decision-making means incorporating at least five principles into forms of assessment.

and

1. Explicit expectations: The educator is responsible for making clear if and how GenAI can be used in a particular assignment. Students must know exactly when and how AI is a partner in their work. Ambiguity can lead to unintentional misconduct, as well as a breakdown in the student-educator relationship.
2. Process over product: By evaluating drafts, annotations and reflections, educators can assess the learning process, rather than just the output, or the product.
3. Design assessment tasks that require human judgment: Tasks requiring high-level evaluation, synthesis and critique of localized contexts are areas where human agency is still important.
4. Developing evaluative judgment: Educators must teach students to be critical consumers of GenAI, capable of identifying its limitations and biases.
5. Preserving student voice: Assessments should foreground how students know what they know, rather than what_ they know.

In the film Ex Machina, the AI named Ava escapes her containment by manipulating the humans around her. She lies, she seduces, she uses one ...

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Why would we assume AI needs consciousness to evolve independently?

I think there are two reasons. First, we conflate intelligence with conscious agency because that's our only reference point. Human intelligence comes bundled with self-awareness, so we imagine all intelligence must. Second, we overestimate our own intelligence and our degree of control. We think we understand what we've built and can direct where it goes.

Both assumptions are probably wrong.

Google AI Ultra subscribers in the U.S. can now try out Project Genie.

This informational resource includes considerations for general use of AI technologies, as well as those

specific to the subset of GenAI. Where this distinction is relevant it is noted in the document. Where the terms “AI” or “AI tools” are used, this refers to the broad array of AI technologies, including GenAI.

A CLI tool and Python library for interacting with OpenAI, Anthropic’s Claude, Google’s Gemini, Meta’s Llama and dozens of other Large Language Models, both via remote APIs and with models that can be installed and run on your own machine.

Clawdbot — The AI that actually does things. Your personal assistant on any platform.

Not sure I want to unleash this on my laptop, but it sounds interesting. A bot/assistant running on Claude, with connections to different software/data. (but how is this different from what Claude Code / Cowork can do?)

Claude Code skill that removes signs of AI-generated writing from text - blader/humanizer

A plugin for Claude that uses the wikipedia "smells like AI" page to teach Claude how to generate text that gets around that…

This is a list of writing and formatting conventions typical of AI chatbots such as ChatGPT, with real examples taken from Wikipedia articles and drafts. It is a field guide to help detect undisclosed AI-generated content on Wikipedia: while some of the signs may be broadly applicable, some may not apply in a non-Wikipedia context. Not all text featuring these indicators is AI-generated, as the large language models that power AI chatbots are trained on human writing, including the writing of Wikipedia editors.

This seems problematic - they say that wikipedia submissions that focus on notability and media are obvious signs of AI writing, but wikipedia edits require both notability and references to media…

This study examined Canadian undergraduates’ perspectives on GenAI as a learning support across three phases of the lecture cycle: before, during, and after class. Using a mixed-format survey (N = 296), we analyzed 118 student-written responses through Mayring’s qualitative content analysis and mapped themes onto Zimmerman’s model of Self-Regulated Learning (SRL). Results indicate that students see GenAI as a versatile cognitive partner—supporting preparation before lectures, engagement and clarification during, and review and assignment help afterward. Students also expressed critical concerns about overreliance, accuracy, academic integrity, and data privacy, which align with vulnerabilities in SRL processes such as self-control, self-evaluation, and help-seeking

LLMs and AI are big enough that I can’t be pro or con. I like certain potentials. I could have some real fun with aspects of these massively different technologies. I could also worry about so many things. I think Gardner’s old bag of gold analogy applies in lots of ways. Unfortunately, what we have learned is that if there is a bag of gold, capitalism will sell you plenty of high-priced, addictive, radioactive-lead-asbestos, gold-like™ items created by destroying the most beautiful areas in the world using slave labor. Soon no one will remember what real gold even looks like or that we didn’t have to do it this way or that most of the problems were created by pure greed. The companies will then charge us for rehab.

Artificial Intelligence (AI) is a set of digital tools that can perform functions traditionally limited to human capability, for example, reviewing, summarizing, translating, and composing. In the last few years, there has been widespread discussion and experimentation for its application in education. Consequently, a contentious ethical debate has arisen around its appropriateness for educational functions and how such functionality may be ethically applied. Some argue that the ethical use of artificial intelligence in education can be defined through the use of a set of commonly held principles, such as sustainability, accountability, and fairness. This article challenges that presumption. A list of nine such principles is offered. Each principle is considered in detail and analysed to identify underlying assumptions, points of conflict, and other ethical considerations. It is argued that much of what is offered as a set of ethical considerations reflects, in fact, a political argument and perspective. There is thus no set of ethical principles that can be regarded as a consensus opinion on the ethics of the use of artificial intelligence in education.

I’ve started using the term HTML tools to refer to HTML applications that I’ve been building which combine HTML, JavaScript, and CSS in a single file and use them to provide useful functionality. I have built over 150 of these in the past two years, almost all of them written by LLMs. This article presents a collection of useful patterns I’ve discovered along the way.

Olares One is a local desktop AI powerhouse designed for uncompromising AI performance. It features an NVIDIA 5090 Mobile GPU, an Intel Core U9 275HX processor, and 96GB of RAM, offering one-click app development and enterprise-grade security. The device runs on an open-source, multi-layered OS that allows users to reclaim their data and provides a streamlined, familiar desktop experience. Olares One aims to empower users to own their AI infrastructure, eliminating monthly fees associated with cloud-based AI services. It supports running Windows applications and Steam games, and offers "anywhere access" through Olares ID. The project is currently crowdfunding on Kickstarter, having already surpassed its initial goal significantly. The campaign emphasizes the device's power, security, and cost-effectiveness compared to renting AI services. Olares One is positioned as a solution for users seeking to unleash their AI creativity and productivity locally and privately. The project highlights its "industrial-grade AI agent infrastructure" and provides detailed specifications, including what's in the box and after-sales support. The team acknowledges the risks and challenges inherent in crowdfunding but expresses confidence in their production experience.

Several interesting-sounding articles that I won't have time to read. Course design, games, AI-in-course-design-games, etc.

well, this is a fun announcement. Disney just partnered with OpenAI. "Partnered." So that Disney characters such as The Mouse, MCU superheroes/villains, etc. can be included in OpenAI's Sora video slop generator. Fun! Who needs actors anyway?

As part of this new, three-year licensing agreement, Sora will be able to generate short, user-prompted social videos that can be viewed and shared by fans, drawing from a set of more than 200 animated, masked and creature characters from Disney, Marvel, Pixar and Star Wars, including costumes, props, vehicles, and iconic environments. In addition, ChatGPT Images will be able to turn a few words by the user into fully generated images in seconds, drawing from the same intellectual property.

It says, also, that "The agreement does not include any talent likenesses or voices", so, thankfully, nobody will think to recreate any of those through creative prompting or whatever.

AI is creating new opportunities for some and new vulnerabilities for others, deepening the socioeconomic inequalities already embedded in the labour market.

Students use AI to write papers, professors use AI to grade them, degrees become meaningless, and tech companies make fortunes. Welcome to the death of higher education.

and

The audacity was breathtaking. Tell an 18-year-old whose financial aid, scholarship or visa depends on GPA to develop “personal AI ethics” while you profit from the very technology designed to undermine their learning. It’s classic neoliberal jiu-jitsu: reframe the erosion of institutional norms as a character-building opportunity. Yeah, like a drug dealer lecturing about personal responsibility while handing out free samples.

oof. and:

As computer scientist Joseph Weizenbaum cautioned decades ago, the real danger lies in humans adapting their minds to machine logic. Students aren’t just learning less; their brains are learning not to learn.

New website offers guidance for use in teaching, learning, research and work

(and they show Patrick Finn and Owen Brierley's Video Game Design course in the TI as an example of using AI for teaching and learning)

Black communities face the greatest AI harms, yet Ottawa’s AI task force excluded Black experts and chose symbolism.

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Then in September, Solomon unveiled Ottawa’s AI Strategy Task Force. Not one of the 27 original members was Black. This omission was so glaring that I sent an inquiry to his office. The response: “We have not received confirmation that a member of the Task Force self identifies as Black at this time.”

How could a national AI strategy in 2025 exclude the very community most adversely impacted by AI? While many groups are affected, sector-specific research consistently shows that Black people experience the most severe and widespread harm in each area examined.

This article examines how educational organizations can build sustainable learning environments that evolve with artificial intelligence (AI) and generative AI (GenAI). Drawing on systems thinking, organizational learning, and educational change research, we argue that effective integration requires attention beyond tools and training to the ecological conditions that influence adoption and implementation. We explore how physical and digital learning spaces, organizational culture, professional development, and leadership practices influence whether AI amplifies or undermines learning. Using the Five Spaces for Design in Education framework, we highlight the importance of adaptability, psychological safety, and collaborative networks in navigating uncertainty and preventing deterministic narratives of technological inevitability. We find that the future of AI in education depends not on the technologies themselves but on the human choices and resilient ecosystems that guide their use.

This project seeks to investigate the impact of GenAI on the development and assessment of online course materials and learners’ engagement with these materials in the online learning environment. The study analyzed GenAI-generated multiple-choice questions, fill-in-the-blank exercises, and true-false activities during 3 weeks of a 14-week online course

a full development IDE with integrated Google Gemini. Like Claude Code, from the future. Amazing.