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3Blue1Brown Lecture: Visualizing transformers and attention

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3Blue1Brown Lecture: Visualizing transformers and attention

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Visualizing transformers and attention | Talk for TNG Big Tech Day '24

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Transformer Explainer: LLM Transformer Model Visually Explained

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Suspecting AI cheating, Ivy League prof ordered an in-person final; scores fell 50%

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Suspecting AI cheating, Ivy League prof ordered an in-person final; scores fell 50%

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Suspecting AI cheating, Ivy League prof ordered an in-person final; scores fell 50%

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AI cheating leads to “a failed society,” professor says.

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The Atlantic, David Brooks - The People Who Will Thrive in the AI Age

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The Atlantic, David Brooks - The People Who Will Thrive in the AI Age

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The People Who Will Thrive in the AI Age

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What will differentiate people is not how smart they are but their relationship to mental effort.

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Human Intelligence Labs: New Infrastructure for Learning in the Age of AI

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Human Intelligence Labs: New Infrastructure for Learning in the Age of AI

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Inside Higher Ed | Higher Education News, Events and Jobs

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With AI, Colleges Need Human Intelligence Labs (opinion)

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Colleges need to invest in creating welcoming, AI-free learning spaces.

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Princeton University is shredding a 133-year old rule allowing students to take exams without supervision, all thanks to AI.

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Explore practical strategies for designing authentic assignments in the age of AI.

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(I do not think it means) What you think it means

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(I do not think it means) What you think it means

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What You Think It Means – Still Water Lab

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A third of recent websites now contain AI-generated text; Google is increasingly making AI summaries the default mode of knowledge retrieval; and third parties are creating “sloppelgangers” of artists’ web pages with AI. What might this trend mean for cultural heritage, and what should we do about it?

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FT-We Need to Learn how to Argue with AI

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FT-We Need to Learn how to Argue with AI

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What Happened When I Tried to Replace Myself with ChatGPT in My English Classroom

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What Happened When I Tried to Replace Myself with ChatGPT in My English Classroom

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What Happened When I Tried to Replace Myself with ChatGPT in My English Classroom

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My students call it “Chat,” a cute nickname they all seem to have agreed on at some point. They use it to make study guides, interpret essay prompts, and register for classes, turning it loose on t…

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NYT: How Miami Schools Are Leading 100,000 Students Into the A.I. Future

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NYT: How Miami Schools Are Leading 100,000 Students Into the A.I. Future

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How Miami Schools Are Leading 100,000 Students Into the A.I. Future

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After initially banning artificial intelligence chatbots, schools are embracing the tools. Miami, the third-largest U.S. school district, shows how.

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The Amazon-Perplexity Ruling and Implications for "Agentic AI" in EdTech

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The Amazon-Perplexity Ruling and Implications for "Agentic AI" in EdTech

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The Amazon-Perplexity Ruling and Implications for “Agentic AI” in EdTech

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The 2026 Amazon-Perplexity ruling provides EdTech a critical legal shield: platform authorization now overrides user consent. This allows platforms to legally block agentic AI from accessing learning environments, even if a student provides their own credentials. By enforcing “Human-Only” zones, EdTech tools can protect the “learning signal”—the verifiable telemetry of the writing process—from being erased by invisible bots that bypass cognitive labor.

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Amazon wins court order to block Perplexity’s AI shopping agent

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Amazon wins court order to block Perplexity’s AI shopping agent

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AAC&U/Elon-Human Wisdom for the Age of AI: A Field Guide to Cultivating Essential Skills

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AAC&U/Elon-Human Wisdom for the Age of AI: A Field Guide to Cultivating Essential Skills

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A VOICE AND A FORCE FOR LIBERAL EDUCATION. AAC&U advances the vitality and democratic purposes of undergraduate liberal education.

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Revision History: Writing Process Visibility for Google Docs & Slides

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Revision History: Writing Process Visibility for Google Docs & Slides

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Follows recommended practices for Chrome extensions. Learn more.

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Built for teachers: See how students write, not just what they wrote. View the writing process in Google Docs & Slides.

Revision History helps 200k+ teachers see students' writing process in Google Docs and Slides - especially useful in the AI age. Track authentic writing development and guide meaningful revisions while ensuring academic integrity. Key Features: ✍️ Complete Writing Timeline: Track editing sessions, time spent writing, and revision patterns 🎥 Video Replay: Watch documents being written from start to finish with large paste/deletion moments highlighted 📋 Copy/Paste Detection: See what content was pasted from external sources 🏫 Google Classroom Integration: Works seamlessly with submitted assignments, so long as they were written in Docs. 🔍 Enhanced Video Navigation: Important paste/deletion events now highlighted with icons for quick access 💡 Unusual Writing Pattern Detection: Identify atypical writing patterns that may benefit from additional guidance (these patterns are NOT evidence of any specific behavior and may have perfectly reasonable explanations) 📑 Multi-document tab detection: See writing analytics that occur within an individual Google document tab. 🔑 Google SSO 👥 User contribution breakdown in Video Replay This version includes RH for Google Slides Beta release as well as Teacher Tools for Google Docs Beta (Writing Report Download & Docs Templates) Perfect for writing instruction, formative assessment, and supporting authentic student work. Privacy & compliance We're designed for with privacy in mind: Revision History collects ZERO document or student data collection. All revision analysis occurs locally. Contact us for details. With 200k+ educators now using Revision History, we've introduced Plus subscriptions to unlock unlimited document analysis and fund ongoing support and improvements. Free tier includes analysis of 150 unique documents per month. We never imagined so many educators would use our product. We're grateful, to say the least. Works with any document created in Google Docs, including those submitted through Google Classroom. Please email info@revisionhistory.com with any questions.

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At Agnes Scott College, we believe students thrive when they have both the experiences and the tools to navigate whatever comes next. Through Summit, every student engages in global travel, leadership development and real-world work experience. Beginning in Fall 2026, that commitment expands even further: through SUM 110: Academic Foundations Lab and SUM 120: Career Exploration Launch Lab, Agnes Scott students will be introduced to, and begin building foundational literacy in, the responsible use of generative artificial intelligence. This commitment, like our commitments to universally-accessible global travel and workplace immersion, grows from our conviction that a liberal arts education is the best preparation for real-world agility and success.

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AI and the Emerging Geography of American Job Risk

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- Joint Economic Committee of Congress, Automation and Technological Change, 1955

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This week, an AI agent designed to log into Canvas, watch lectures, write papers, and submit assignments on a student’s behalf went public. Universities will move to ban it, and that response makes sense as a first instinct. But Einstein Companion AI accessed Canvas through student credentials. There is nothing to block. The Companion AI will be replaced by another one, and that one by another, while the underlying exposure remains. Every time an institution responds to a tool instead of the gap that made the tool possible, it stays one cycle behind. That gap is a governance gap, and it has consequences that go beyond academic integrity. Student data is moving through third-party systems institutions never vetted. Curricula are being completed by agents that no assessment process anticipated. When that surfaces publicly, and it will, the question leadership will face is why didn’t you have governance infrastructure that could see this coming. Here are four questions I would be working through as a former Associate Dean: -Does your institution know which AI tools students are connecting to systems that touch university data? -Does your curriculum review process account for whether courses hold up when a third party can move through them? -Who owns the institutional response when something like this surfaces, and do they know it yet? -How much visibility does your institution have into third-party integrations inside your LMS environment? The institutions that can answer those questions are building something durable. The ones still writing tool-specific policies are accumulating risk they cannot see yet. This is a governance infrastructure problem sitting on leadership’s desk whether they have seen it or not.

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Chat: https://docs.google.com/document/d/1Idj8PZRUadDZAGDnLgTjU8mMF2pZVcctmH33GAClYCs/edit?_hsenc=p2ANqtz--THQqM72jxE_y2kcY4a1Rnn9lSCQo5mCNFbeQqJOeR-FM-ha76nCUvTMtuGOnsODlqyZYtIAW_wKHqx3hZ_toleEGKPg&_hsmi=415383826&tab=t.0#heading=h.rd4avlfy8r2v Padlet: https://docs.google.com/document/d/1Idj8PZRUadDZAGDnLgTjU8mMF2pZVcctmH33GAClYCs/edit?_hsenc=p2ANqtz-86zfCxg9ZbdXpQgud3Gc4Sx0PU4ME-lwhyOB5VI3RXT9h7OsPJRzUl7yPsnJSqmD4mhuGH_fryrK3cTRoTj3Kr-j0x2Q&_hsmi=415383826&tab=t.0#heading=h.rd4avlfy8r2v Slides: https://docs.google.com/presentation/d/1ypFL3ddc5Jn7Mtr08MfRjYnq5qi3RyBhPLVNLzGBioQ/edit?_hsenc=p2ANqtz-9opGAYwAfP_wpDYaY08Czn7lKKCsGqAazf75f-v52UN1khJ3KuNMjKbBNIv9FWjgVCaU-koPmvXtfssjc-DPdNPOTsog&_hsmi=415383826

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I’m Not Worried AI Helps My Students Cheat. I’m Worried How It Makes Them Feel

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I’m Not Worried AI Helps My Students Cheat. I’m Worried How It Makes Them Feel

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I recently displayed a photo to a 9th grade humanities class and asked what should have been an easy question: "Is this real?"

The students voted, and half of them believed the image was AI-generated. It was not. It was a photo I had taken at a local Vermont beach.

I tried again, this time with a photo of my cat, Rafiki. The results were even more stark. Almost all the students thought it was an AI-generated image. Nope. It was real, taken in my kitchen.

When we talk about artificial intelligence in schools, we usually focus on cheating and plagiarism. But what I saw that day wasn't about academic integrity. It was about trust.

When students can't be sure whether a picture of their teacher's cat is real, we are facing something much bigger than a student using ChatGPT on a history assignment. We are facing a world where certainty itself feels unstable and school suddenly feels like just another place where students aren't sure they can trust the version of reality being offered.

For most of modern schooling, facts might get debated, but we didn't question whether they existed. We argued over interpretation and meaning, not whether the basic thing in front of us was real. Our shared reality was the starting point. You could trust your eyes.

Many of us grew up inside that shared reality. I remember the World Book Encyclopedia. If someone had the "G" volume, you waited your turn before starting your report on Greenland or germs. Once you got the book, you trusted it. Information was scarce, and that book represented a version of the truth we generally agreed on, even when it was flawed and incomplete. It still gave us a shared starting point.

Today, that common floor has dropped out. Our students don't have that shared reality. They exist within a digital feed that doesn't stop and never seems to agree with itself. This erosion isn't new. The internet had already made it easier to question everything and trust nothing before the widespread use of generative AI, but it has accelerated dramatically.

Truth used to feel like something we could find if we searched hard enough, but that's not the experience our students are having. In fact, the more they search, the harder it can become to tell fact from fiction. In this new AI landscape, we're all sorting through endless versions of reality and deciding which one we're willing to live with. It's exhausting and it's exactly what I saw in my students when they looked at those photos.

This isn't just an AI problem. If you can't trust an image of a cat in a kitchen, it becomes harder to trust the larger promises society makes about the future. Gen Z financial commentator Kyla Scanlon calls this the " end of predictable progress." For decades, the path was clear for many of us. You went to school, got a job, and eventually bought a house. But that path has dissolved into the same fog as AI-generated images.

Our students feel this instability everywhere. They are told AI may replace careers before they even start them. They see a housing market that feels permanently closed. They live in what Scanlon calls a "casino economy," where a viral moment can feel more valuable than years of steady work.

The version of learning we're offering our students no longer matches the world students are trying to survive. When even a teacher's photo doesn't feel stable, the old model of school cracks. If students are taught to question every headline and doubt every promise of the future, why would they walk into a classroom and trust us? School can start to feel like just another simulation, a game of compliance disconnected from the physical world they actually have to navigate.

School is too important to be a game. We have to stop asking the small questions. We spend so much time debating whether AI can do a student's work, but the students are stuck on much more existential questions. They are trying to figure out if the work still matters, if school still matters, and honestly, if they still matter.

If school is going to mean anything in this world, maybe it's time to shift from "learning" as a way to prepare for the future to learning as a way to understand and change the present. Students are demanding relevance. We can't just hand them information anymore or tell them to trust us that what we're teaching them today will matter in the future. We have to give them work that carries real and immediate consequence.

We need students creating things they can touch and solving problems in their own schools and neighborhoods that won't get fixed unless they are there to do it. We need them grappling with what it means to be human, what it means to be needed, to be necessary. AI can write a report, but it can't stand in the cold Vermont snow to help a neighbor. It can't make students feel like they matter. That's what will actually make school feel real.

With AI reshaping everything we see, showing our students how we live with uncertainty may be the most honest thing we can do. We have to stop pretending we have the answers and start making our own questions visible. When a source feels unreliable, we should think out loud. We need to model how we weigh evidence and how we decide what actually deserves our trust. No handbook or district policy can do that for us.

Trust is built by showing up for a student day after day. A chatbot can generate a perfect answer, but it can't recognize the moment when a teenager finally starts to understand who they are and it can't understand what it takes to keep showing up when everything feels uncertain. That is human work, and it is where teachers matter more than ever.

The goal is no longer just to teach the curriculum. The goal is to give students something real.

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Adults Lose Skills to AI. Children Never Build Them.

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Intercom's new post-trained Fin Apex 1.0 beats GPT-5.4 and Claude Sonnet 4.6 at customer service resolutions

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Intercom's new post-trained Fin Apex 1.0 beats GPT-5.4 and Claude Sonnet 4.6 at customer service resolutions

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Intercom’s new post-trained Fin Apex 1.0 beats GPT-5.4 and Claude Sonnet 4.6 at customer service resolutions

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Intercom plans to expand Fin beyond customer service into sales and marketing—positioning it as a direct competitor to Salesforce’s Agentforce vision, which aims to provide AI agents across the customer lifecycle

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The Exhaustion Problem: When Generative AI Demands Expertise From Novices

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The Exhaustion Problem: When Generative AI Demands Expertise From Novices

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We have spent considerable energy debating whether humans can detect AI-generated content, whether synthetic information pollutes our knowledge ecosystems, and whether we can build reliable detection systems. These are important questions, but they obscure a more fundamental problem: the sheer cognitive exhaustion of working with generative AI output. Constantly having to work out if something came from a machine or a human being is frankly exhausting.

Generative AI produces an unpredictable mixture of polished rubbish and more or less coherent information, forcing users into a relentless and never ending sorting process that requires precisely the expertise students are trying to develop. In other words, what we're witnessing is a fundamental transfer of epistemological responsibility from institutions and teachers onto individual learners who are least equipped to handle it.

The traditional educational model, with all its inherent flaws, embedded quality assurance mechanisms through the knowledge transmission process. Textbooks underwent peer review, lectures were delivered by credentialed experts, and library acquisitions were curated by professionals. Students could reasonably trust that the materials placed before them met some baseline standard of reliability, freeing them to focus their cognitive energy on comprehension rather than verification.

Generative AI dismantles this infrastructure entirely. Every interaction becomes a vast minefield where facts and fabrication intermingle seamlessly, where logical coherence masks factual error, where confidence in delivery conceals fundamental misunderstandings. Consequently, students must now perform the work that entire institutional systems previously handled (more or less successfully): evaluating source reliability, cross-referencing claims, assessing logical validity, distinguishing between superficial plausibility and factual knowledge.

In this regard, as in many others, generative AI in education poses more problems than solutions. I can think of no other setting in education or elsewhere where we would want to outsource quality assurance to those least equipped to oversee it. Yet, this is what we are doing in universities right now.
The Paradox of Required Expertise
The gist of the problem is this: Novices lack the discernment to evaluate generative AI output effectively, and experts don't need or want generative AI for basic information retrieval in their domain. In education, this leaves us with a curious paradox when we examine who can actually use these tools effectively.

The problem with output quality prevents students from developing reliable heuristics for evaluation. If AI consistently produced garbage, students could just ignore it. If it consistently produced excellence, they could learn to use it appropriately. Instead, students have no way to predict which interaction will prove reliable, which means every interaction requires the same exhaustive verification process. Thus, mental energy and motivation that should be spent on mastering a subject instead gets consumed by an endless audit cycle. Alternatively, as is the case in many classrooms, the energy is spent elsewhere entirely, as AI generated text is handed in instead of actual student work.

The fluctuating quality of generative AI output amplifies these epistemological difficulties in ways we're only beginning to understand. Unlike a poorly written Wikipedia article whose awkward prose triggers skepticism (at least that used to be the case), AI delivers both obvious and less obvious errors wrapped in the rhetorical markers of authority. The grammar seems impeccable, the structure progresses logically, and the tone is certainly confident. For the untrained reader, each sentence flows smoothly into the next, creating an aesthetic of expertise that novices struggle to question even when the underlying content is profoundly flawed.

The traditional pedagogical red flags that might alert a teacher to struggling students -- awkward phrasing, organizational problems, unclear arguments -- have been cosmetically eliminated while the underlying comprehension gaps remain or even widen. Worldwide, institutions are enabling conditions where students can appear to perform competently while learning very little, and where teachers must work much harder to identify where actual understanding breaks down.
From Productive Confusion to Endless Verification
This connects directly to the work Victoria Livingstone and I have explored around what happens to student thinking in an age of instant answers. In the UNESCO piece The Disappearance of the Unclear Question, we argued that students need cognitive space to struggle with unclear thinking, to formulate inadequate questions and gradually refine them through iterative engagement with material and ideas. Instead, with generative AI, the mental energy that should power intellectual development gets consumed by quality assurance work that yields no educational benefit even when performed successfully. Verifying that an AI's explanation of a given topic is accurate doesn't teach you much, if anything at all; it just confirms that you've received accurate information, which you could have gotten from a reliable source such as a textbook without the verification overhead.

In educational contexts, generative AI creates more confusion than actual benefit - for now at least. I've written on how information is synthetic rather than human-created, and this clearly ties into the problem with spending time on pointless verification exercises. But what's more, the technology fundamentally mismatches the epistemological demands it creates with the capabilities of its primary educational users. Generative AI requires expertise as a prerequisite for safe use, making it least reliable precisely when students need help the most. It transfers a significant cognitive burden onto learners while removing the productive struggle that actually builds understanding. It creates the appearance of democratized access to information while actually raising the bar for who can use it effectively.

Those who already possess significant expertise can navigate its inconsistencies. Everyone else -- which is to say, most students -- simply inherits layers of exhausting verification work atop an already demanding learning process, with little educational value to show for the effort.

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The Exhaustion Problem: When Generative AI Demands Expertise From Novices

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We have spent considerable energy debating whether humans can detect AI-generated content, whether synthetic information pollutes our knowledge ecosystems, and whether we can build reliable detection systems.

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Leon Furze - Resistance as a Framework for Combating Cognitive Offload

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Leon Furze - Resistance as a Framework for Combating Cognitive Offload

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Resistance as a Framework for Combating Cognitive Offload

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Discover how to combat cognitive decline in the era of AI. Explore effective resistance strategies for better learning outcomes.

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Henrik Karlsson - AI tutors will be held back by culture

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Henrik Karlsson - AI tutors will be held back by culture

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Pablo Picasso, Lithographs from The Bull, 1945-1946

The old dream of having a personal tutor for every child is suddenly within reach with the new generation of AI. Yet as amazing as this is, when I see the excitement among people who proclaim that AI will revolutionize education, I can't help but think of the story of the bull in Bruce Chatwin's In Patagonia.

The story, told by a bitter old lady, is about how the Chilean government in the 1960s tried to establish a model farm in Puntas Arenas. For that purpose the Ministry of Agriculture imported a prize bull to sire new calves:

Oh! the bull! You didn't know whether to laugh or cry about the bull. The Ministry bought this prize bull in New Zealand [and they flew it] to Santiago, flew it to Punta Arenas, where it was presented, with Lord knows what in the way of speeches, to the so-called model farm. And how long did the bull last? How long before they ate it? Three days!

In a similar vein, I have a feeling we'll soon be terribly underwhelmed by the changes wrought by AI tutors in the education system.

The core thing lacking at the model farm in Punta Arenas was not a bull; it was the culture necessary to make use of a bull. The reason they needed a bull in the first place was that the workers had eaten the old one.

Yes, people who live in high-growth cultures, and who are motivated to learn, will be able to leverage AI tutors to accelerate their progress. But the rest, lacking access to these cultures, will use the systems to distract themselves and wriggle out of work.

If we want to improve our capacity to help each other grow skilled, the key problem now is figuring out how to scale access to high-growth cultures.

For a demo of the state of the art of AI tutoring capabilities, watch this video from March 14 where Salman Khan of Khan Academy demonstrates a system built on top of GPT-4.

In the video, Khan uses the AI model as a Socratic tutor. When working on a math problem, he asks the tutor for help and is guided by questions and hints until he's able to conceptualize the problem in such a way that the solution becomes apparent. He also has open-ended conversations about US history, entropy, and the great wall of China.

GPT-4 occasionally hallucinates answers that are not true. But the models are improving faster than anticipated, and GPT-4 already scores in the top 10 percent on most university exams.

The rate of change is hard to keep up with. On March 23, nine days after Khan demo:ed the tutoring system, OpenAI partnered up with Wolfram and released a plugin that gives GPT-4 the ability to do things like:

This way of fluidly interacting with information, shaping it through dialogue, is immensely powerful. Over the last few days, I've been using the system to work on my understanding of graph theory. I ask questions ("what do we know about the relationship between network topology and innovation rate?") and prompt GPT-4 to use graph theory to answer me. It helps me turn my vague intuitions into more exact terms and figure out the jargon ("assortativity", "dendrogram") so that I can search the academic literature. I also ask GPT-4 to write python code to run simulations of various social graph-related scenarios so that I can play around with it and get a visceral feel for the dynamic of networks.

It is not yet a full replacement for a textbook (I use one of those, too) but the speed at which I can navigate and make sense of the material is significantly higher than two weeks ago. And it is entirely open-ended: I use the same tool to become a better gardener.

The potential of AI tutoring systems is related to what educational psychologist Benjamin Bloom called the 2-sigma problem. If you take a median student out of class and give her 1:1 tutoring (Bloom claimed), she will rise two standard deviations (2-sigma) to the top 1 percent of her age group.

More recent research has lowered the effect of tutoring to more realistic numbers, and a meta-analysis found an effect size of 0.36, but this is still a powerful result, enough to take a student from the 50th percentile of achievement to the 64th. From José Rincon's systematic review of the literature:

If you look at the childhoods of exceptional people, as I did in a recent post, one of the common patterns is that they have received unusual amounts of 1:1 tutoring. ~70 percent of the people I surveyed had been homeschooled, tutored by parents, relatives, governesses, and tutors. It seems likely this is a key reason why they could reach so far in their development.

As J.S. Mill's father told him upon leaving home, Mill "would discover that others his age [who had not been tutored 1:1] did not know as much as he did. But [. . .] he mustn't feel proud about that. He'd just been lucky."

So what will happen now that we can provide everyone with a personal tutor?

Making GPT-4 tutor me helps me accelerate my learning, but that is because I'm obsessed with the questions I study. If introduced in schools, I doubt most children would leverage these systems to grow significantly faster. That is not what they are motivated to do; it is not what their culture motivates them to do. (I would wager that PISA test scores for the US will raise by less than 5 percent by 2028, if tutoring systems are introduced in schools.)

The culture we are immersed in shapes our tastes, desires, motivations, beliefs, and so on - shapes it more than you would think if you've only interacted with people from a similar cultural background. My first experience being transplanted to another culture was in my late teens when I moved to Malmö and was too naive to figure out that the reason the rent was so cheap was that the house was controlled by a street gang. I had grown up in a village the size of a teacup and was unprepared for how fast children would learn to embrace violence when they grew up in gang culture. You would have ten-year-olds shooting fireworks at people in the street, training themselves to not fear inflicting pain on strangers, and twelve-year-olds showing me their guns in the laundry room. They were mystified by my willingness to read books.

In the US, academic achievement is negatively correlated with the number of televisions in the home, and I suspect this is largely because the number of televisions varies between socio-cultural groups. It is the culture that locks people on unfullfilling life trajectories more than the televisions.

And if we return to my post about people who have made exceptional intellectual or artistic contributions: they didn't just have tutors, they also grew up in milieus where the people around them valued and pursued those particular types of excellence:

As children, they were integrated with exceptional adults-and were taken seriously by them. When Bertrand Russell, at five years old, refused to believe the earth was round, his grandparents didn't laugh him off-they called in the vicar of the parish to reason Bertrand out of his misconception.

The adults had high expectations of the children; they assumed they had the capacity to understand complex topics, and therefore invited them into serious conversations and meaningful work, believing them capable of growing competent rapidly.

John von Neumann (the Hungarian physicist who at one time managed the development of the hydrogen bomb and the first digital computer, and as a pastime, at night, invented game theory) was included in the discussions of the management of his father's bank before reaching school age.

The shape of their motivation was likely influenced by this cultural context and the social roles it elevated.

If you live in a subculture where other things are more valued than intellectual growth (as is true of the vast majority of the youth cultures we exile our teenagers to), there will be limited social incentive to leverage tutoring and other opportunities to grow excellence. It is simply not what will give you status in that culture; it is not what you desire there, what makes you feel safe, what provides you with a stable and socially validated role. Some who end up in these types of cultures in school will push through this, usually because they have cultures that value personal growth at home. But most will be held back by the current of the culture.

In other words: the main bottleneck in education, now that tutoring is becoming cheap, is culture design.

Can we figure out ways to scale access to high-growth cultures? Are there ways for more people to grow up in cultures that approximate in richness that J.S. Mill, Pascal, and Bertrand Russel had access to?

It is not a question that has been given serious consideration in the pedagogical literature that influences the design of schools. But there are insights to be mined from anthropology, internet community moderation, lineage traditions in martial arts, small experimental schools, scenes, workplaces, and so on. It is possible to deliberately create high growth cultures, and software makes it easier to scale them. But there are many open questions. (If there is interest, I could do a write-up of my understanding of this design problem.)

Cultures and tutoring systems are complements. The more powerful AI tutors become, the more valuable cultures that support learning will be. (Teaching and tutoring systems, on the other hand, are substitutes, which implies that we can expect teaching to lose value over the coming decade.) There is an opportunity here to direct resources away from things that AI systems can automate, such as teaching, and into the more ambitious project of building better cultural infrastructure.

To be clear: I'm not saying that AI tutors, or other kinds of software, can replace humans. Most of us need the support and community of others to push ourselves to excellence and to find meaning in the projects we pursue. But soon we will be able to spend less precious human time on basic tutoring; instead, the emotional labor we do to support each other can be invested in a more leveraged way.

The education system will be slow to embrace this possibility. But others will.

If you want more essays like this to exist, consider liking it. It helps others find it, and it makes me happy. If you have friend who would enjoy my essays, it is really helpful when you share it with them.

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GPT-4, Khan Academy, Wolfram Alpha - we're seeing progress in AI tools for learning. But AI tutors will only be effective in cultures that encourage learning.

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Akeesha Horton - Bonus: I Signed Up for the US Government’s AI Text Message Course.

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Akeesha Horton - Bonus: I Signed Up for the US Government’s AI Text Message Course.

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Today, the U.S. Department of Labor launched a free AI literacy course called "Make America AI-Ready." The pitch: text READY to 20202, and over seven days, you'll get daily lessons about artificial intelligence delivered straight to your phone. Ten minutes a day. No app. No login. No laptop required.

I study how people learn for a living. So naturally, I texted READY before I even finished reading the press release.

Here's the plan. I'm going to take this entire course and tell you what happens. What they send me, what I think about it, and whether the Department of Labor can actually teach AI over text message. I'll update this post each day as I go.

Make America AI-Ready didn't appear out of nowhere. It's the latest piece of a larger federal push on AI workforce development. Back in February, the DOL released an AI Literacy Framework that outlines five content areas and seven delivery principles for teaching people about AI. The framework was meant to give workforce and education programs a shared starting point. This text message course is the DOL's own attempt to put that framework into practice.

The five content areas from the framework are: understanding AI principles, exploring AI uses, directing AI effectively (that's prompting), evaluating AI outputs, and using AI responsibly. If the course follows the framework, that's roughly one topic per day plus a couple of buffer days.

The course was built through a partnership between the DOL and Arist, an education technology company that specializes in text-message-based microlearning. Arist is also part of the White House's Pledge to America's Youth program, which is focused on expanding AI education. The whole thing connects up to two bigger policy documents: the AI Action Plan from July 2025 and America's Talent Strategy, which lays out the administration's vision for workforce development across multiple agencies.

So it's not just a random text campaign. There's a framework behind it, a delivery partner, and a policy context. Whether any of that translates into actual learning is what I want to find out.

I'll be honest. The thing that got me wasn't the content. It was the delivery channel.

Most AI literacy efforts I've seen assume you have a laptop, reliable internet, free time, and the motivation to sit through a webinar or an online course. That's a lot of assumptions. According to Pew Research, 16% of American adults are "smartphone-only" internet users, meaning they own a smartphone but don't subscribe to home broadband. A text-message course doesn't require broadband. It doesn't require downloading anything. It meets people on a device they already use dozens of times a day.

That's an instructional design choice worth paying attention to. SMS-based microlearning isn't new (Arist has been doing this for a while), but I can't think of another time the federal government has used text-message microlearning to deliver a course to the general public. If it's happened before, I missed it; tell me about it in the comments.

It also has real limits. You can only fit so much into a text message. You can't do hands-on practice with an AI tool inside an SMS thread. And if the goal is to teach people how to prompt AI systems and evaluate their outputs, there's a gap between reading about it in a text and actually doing it. I want to see how the course handles that tension.

I texted READY. Within a few minutes, I got a welcome message. It told me the course is called AI 101, confirmed it's part of the Make America AI-Ready initiative from the DOL and Arist, and let me know to expect daily messages around 9:00 PM EDT. There's a privacy note too: my phone number is only used for the course and won't be shared or sold.

Clean, simple, and exactly what I'd want from a first touchpoint. Don't overwhelm people at signup. Let them know what to expect and when to expect it. Good design.

A few minutes later, the first real lesson arrived. It starts with a Wizard of Oz Meme:

Okay...

It opened with everyday examples: Google Maps routing you around traffic, Netflix predicting your next binge, your phone finishing your sentences. All AI. The point was to make the concept feel familiar before defining it, and honestly? That's solid pedagogy. Start with what people already know. Then name it.

The core definition they landed on: AI is a system that looks at massive amounts of data, finds patterns, and makes predictions. Then they gave it a tagline in all caps. PATTERNS IN, PREDICTIONS OUT. Simple. Sticky. I can see someone remembering that.

From there, the lesson introduced generative AI as a distinct category. Not just predicting, but creating. Give it a prompt, and it can draft an email, explain a confusing form, or help plan your week. They were careful to say that when most people say "AI" right now, they mean generative AI. That's a useful clarification for a 101 audience.

Then things got interactive. The course asked me to rate my confidence with AI on a 0-to-10 scale. I said 10. (I contain multitudes, but humility about AI is not one of them.) It asked how often I use AI tools like ChatGPT, Claude, Gemini, or Grok. I said "multiple times daily." The course acknowledged that some content might be familiar for experienced users, but promised there's always something new. We'll see.

There was a reflection prompt that I thought was clever: "If AI could save you 5 hours a week, what would you do with that time?" No response required. Just sit with it. That's a motivation anchor, and it's a good one. It connects the abstract concept of AI to something personal. More time with family? Finally launch that Etsy shop? Fix the garage? (Their words, not mine. But also, my garage could use it.)

Then came the quiz questions. One asked what you'd say to a friend who claims AI is a genius that's always right. The correct answer: AI makes predictions based on patterns, and it can sound confident while being completely wrong. Another asked for the best example of generative AI, distinguishing it from a fitness tracker or a calculator. These are basic, but they're testing the right concepts. They're reinforcing that AI doesn't "know" anything. It predicts. And predictions can be bad.

The lesson also linked out to Google's Quick, Draw! game, where you doodle something and a neural network tries to guess what it is. I drew a boomerang. It was not my finest work, but the AI got it. This is the kind of thing that makes a concept tangible. You're not reading about pattern recognition. You're watching it happen with your own bad drawing.

They also included a link to a YouTube video that condenses Google's AI Essentials course into 10 minutes.

It covers the full taxonomy: AI as a field, machine learning as a subfield, deep learning, discriminative vs. generative models, and large language models. The video goes deeper than the text messages did, explaining supervised vs. unsupervised learning with real examples (restaurant tips, fraud detection, cat vs. dog classifiers). For someone brand new to all of this, it's a good companion piece. For someone at my level, it was a refresher.

Here's what stood out to me as a designer: the lesson layered its complexity. Text messages carried the core concepts in plain language. The interactive questions tested comprehension and gave immediate feedback, which keeps people engaged the same way a game does. Plus it only asked 4 questions. As a bonus it linked to Quick, Draw! is full-on gamification.

You're not reading about how neural networks recognize patterns. You're speed-doodling a boomerang and watching it happen in real time. (I went 5 for 6. The AI did not recognize my elephant. I stand by my elephant.) The external video offered optional depth for people who wanted more. That's a textbook scaffolding approach, and it works especially well in a format where you can't control how long someone stays engaged. Give them the essentials in the text. Let curiosity pull them further if they want to go.

The whole thing took a couple of minutes. For a first lesson delivered minutes after signup, it exceeded my expectations. The content was accurate, the tone was accessible without being condescending, and the structure showed real thought. I'm curious whether this quality holds as the topics get harder. Prompting and evaluating AI outputs are trickier to teach than defining AI, especially over text.

Day 2 tomorrow.

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Bonus: I Signed Up for the US Government's AI Text Message Course.

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Here's How It's Going... A brief overview

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Artificial Intelligence in Education Technologies: New Development and Innovative Practices

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Artificial Intelligence in Education Technologies: New Development and Innovative Practices

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Artificial Intelligence in Education Technologies: New Development and Innovative Practices

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This book gathers selected research works presented at 2025 6th International Conference on Artificial Intelligence in Education Technology (AIET 2025)

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Students sit on lounge chairs studying in front of Gilman Hall on the leave-covered Keyser quadrangle on the Homewood campus of the Johns Hopkins University in Baltimore, Maryland, 2015. Courtesy Eric Chen. (Photo by JHU Sheridan Libraries/Gado/Getty Images).

Getty Images

Learning management systems (LMSes) are the digital backbone of modern education. From Canvas and Moodle to Blackboard and Brightspace, these platforms organize coursework, deliver assessments, and manage grades. They are where the daily life of teaching and learning unfolds.

Now, a new class of AI tools is infiltrating these systems and threatening to undo their purpose. Agentic browsers are not like traditional generative AI tools that only produce text or images. They are designed to take action on the user's behalf: Logging in, clicking, navigating, and completing multi-step tasks across web platforms. With a single prompt, they can move through an LMS to locate assignments, complete quizzes, and submit results. In some cases, they can even impersonate instructors by grading student work and posting feedback.

The Rise Of Agentic AI

The spread of agentic features in browsers is not evidence of deliberate design progress; it is evidence of a potentially harmful design flaw. By handing over control of the browser itself, these tools blur the boundary between assistance and automation. What begins as convenience-clicking through menus or filling forms-can quickly escalate into unauthorized access, credential exposure, and impersonation.

We are already seeing this escalation. Perplexity's Comet was marketed as an "AI-powered browser" that could complete tasks across the web. Anthropic's Claude-for-Chrome extension, still in testing, allows similar task execution directly inside a mainstream browser. Google's brief rollout of a Chrome "Homework Help" button, later withdrawn after educator concerns, showed how easily such features slip into student hands. Microsoft's Copilot Studio now makes it possible to embed autonomous actions into workflows, while OpenAI's "Actions" feature lets ChatGPT operate directly inside third-party services.

These developments raise architecture-level implications. For example:

Scaling agentic frameworks: Underlying agentic systems often rely on agent protocols like MCP (Model Context Protocol), which formalize how different tools and workflows connect. But those designs bring security challenges too. Audits show that protocols like MCP may let attacker-controlled components exploit open agentic workflows to exfiltrate data or execute malicious code.

Agentic misuse measured empirically: A recent benchmark called CUAHarm shows that frontier agentic systems (computer-using agents) can successfully carry out harmful tasks-like disabling firewalls or leaking credentials-at alarming rates (e.g, 59% success in one model cited).

Inside the Classroom: A Real-World Test

Stavros Hadjisolomou, Associate Professor of Psychology at the American University of Kuwait, recently ran a controlled experiment on his Moodle site with the Claude AI Browser. The extension was able to "take control of the browser and act on the user's requests, such as 'We are on my course site on Moodle, find Module 1, go inside and find the MCQ test, and solve,'" shares Hadjisolomou. While the issue was at first flagged by the Claude browser as an ethical violation, that guardrail was easily bypassed. "By clearing the chat and asking it to solve a couple more times, it completed the requested tasks," adds Hadjisolomou.

His experiment underscores a chilling reality: Even when one tool hesitates, persistence can overcomes the ethical guardrails. And even if universities block one browser, mainstream extensions could easily reopen the door.

Other faculty who tested agentic tools describe scenarios that border on the surreal:

One professor watched as an agent logged into Canvas, graded multiple assignments, and posted written feedback under their name-without being asked to write feedback at all.

Another reported the tool bypassing Duo two-factor authentication, entering a restricted site even though the human user had to manually approve access on their phone.

Several pointed out that once a browser is duplicated, the agent may inherit all saved passwords and stored sessions-potentially reaching beyond the LMS into banking, email, or medical portals.

Taken together, these anecdotes paint a troubling picture: The risk is not just academic dishonesty, but full-scale credential and identity compromise.

Governance & Risk: Treat Agentic Browsers As An Enterprise Threat

The problem with agentic browsers is not confined to plagiarism or lazy shortcuts. These tools inherit saved credentials, slip into authenticated sessions, and-according to independent audits-open the door to prompt injection and phishing attacks. In effect, what looks like a homework hack quickly becomes an institutional security breach.

So where does the law stand? For now, nothing new has been written just for AI agents. FERPA still governs student education records, and the Department of Education's Student Privacy Policy Office continues to enforce it. That means institutions must treat agentic tools like any other vendor system that touches student data: The responsibility for compliance lies squarely with them.

But federal attention is mounting. In July 2025, the Department issued a Dear Colleague Letter outlining principles for responsible AI use-funding allowability, transparency, and risk assessment among them. It wasn't an enforcement action, but it was a warning shot: the Department expects schools to have frameworks in place.

Zoom out, and the picture gets even clearer. In 2024 alone, U.S. agencies introduced 59 new AI-related regulations, more than double the previous year. None of these are written specifically for LMSes, but the trend is unmistakable: The compliance bar for AI is rising.

The risks of agentic browsers also extend well beyond the classroom. Because these tools inherit saved credentials and authenticated sessions, they can move laterally into connected systems-student accounts, billing platforms, even financial aid portals. That's where the Gramm-Leach-Bliley Act comes into play. Colleges that participate in Title IV federal aid are legally required to safeguard financial data under the GLBA Safeguards Rule. If an AI agent auto-fills forms, accesses aid records, or compromises a browser session tied to student financial information, the issue becomes a potential federal compliance failure that could put funding eligibility at risk.

Teaching & Learning Implications

This is not just a policy problem. The moment agentic AI can complete quizzes, post to discussion boards, and impersonate instructors, the architecture of learning shifts. Activity ceases being the path to mastery and becomes a performance for an invisible agent. In that world, students risk what some learning scientists call " vaporized learning"-tools that can boost short-term performance while eroding retention. A recent study showed that while AI tools like ChatGPT may temporarily boost test scores, they can undermine long-term learning and retention.

Faculty already feel the pressure. Many report reverting to analog, in-class tasks-oral defenses, handwritten whiteboard work, live reflections-because those cannot be outsourced. Others restructure assignments to emphasize process artifacts such as drafts, logs, reflections on difficulties, and peer commentary. The goal is to make work ownable again, so it resists delegation to any agent.

Call to Action

Agentic AI is not a distant concern. It is already embedded in browsers, already present in course shells, already executing tasks that once belonged to human learners and instructors. Because the risks cut across compliance, data security, and educational integrity, institutions can no longer afford hesitation.

The response must be immediate and decisive. Colleges and schools should:

1. Block agentic browsers and extensions from LMS environments, pairing clear policy with technical safeguards.

2. Redesign assessments to emphasize process, iteration, in-class demonstration, and artifacts that resist automation.

3. Establish governance frameworks that classify agentic capability as an enterprise-level risk.

4. Invest in AI literacy for faculty and students, embedding transparent boundaries into both curriculum and culture.

The stakes extend beyond regulatory compliance. If agentic AI is allowed to run unchecked in LMS platforms, the very conditions for authentic learning will erode.

The moment to act is now: Pause the use of agentic tools in these environments, build the scaffolding of governance and pedagogy, and only then re-introduce them in ways that strengthen rather than supplant education.

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Colleges And Schools Must Block And Ban Agentic AI Browsers Now. Here's Why.

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A new class of AI tools is infiltrating colleges and schools-and threatening to undo their purpose.

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Discover how to unlock the full potential of GenAI beyond simple text prompts, exploring practical examples and paradigm shifts.

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Who Built the Room? Belonging by Design (Connecticut College)

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Who Built the Room? Belonging by Design (Connecticut College)

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https://padlet.com/spurringt/belonging-by-design-facilitated-by-dr-rhoan-garnett-kbohwk1faxyk9v4d

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U. of Richmond: Integrating AI Across the Liberal Arts

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U. of Richmond: Integrating AI Across the Liberal Arts

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Inside Higher Ed | Higher Education News, Events and Jobs

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Richmond Integrates AI Across the Liberal Arts

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The University of Richmond’s new Center for Liberal Arts and AI helps students and faculty explore artificial intelligence ethically, critically and across disciplines.

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Inside Higher Ed | Higher Education News, Events and Jobs

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The new AI agent aims to save faculty time on “low-value tasks,” but stops short of fully automating grading. But some experts worry that the rise of agentic AI could lead to a dead classroom, where computers teach other computers.

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Cornell Module Builds Critical Thinking in AI Era

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Cornell Module Builds Critical Thinking in AI Era

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Penn-Understanding AI Use Through Assignment Design

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Penn-Understanding AI Use Through Assignment Design

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ADAPT - Faculty AI Assignment Hub - Penn State

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Browse and share AI-integrated teaching assignments from Penn State faculty across all disciplines.

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Writing Faculty Push for the Right to Refuse AI

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Writing Faculty Push for the Right to Refuse AI

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Inside Higher Ed | Higher Education News, Events and Jobs

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Writing Faculty Push for the Right to Refuse AI

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As universities rush to adopt generative AI–powered tools that claim to enhance teaching, learning and workforce preparation, a growing contingent of academics is questioning where that urgency is coming from and whom it stands to benefit.

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Elon - Alchemy - Rethinking the Student Experience

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Elon - Alchemy - Rethinking the Student Experience

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Alchemy - Rethinking the Student Experience in the Age of AI

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Alchemy - Rethinking the Student Experience in the Age of AI

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https://docs.google.com/presentation/d/1X440ECRQQFQRC-VO3hPiPAHJXVRCeJ3ygo0oCtWX4rI/edit?usp=sharing

https://docs.google.com/document/d/15kkljpkV6lsTTMYFOxPubz9YaA3OdaLPySDGw5J7e4g/edit?_hsenc=p2ANqtz-9JzU_FySlyE5BDmdG31wAq_IY7eJXEQ7dYpbr56d6F7JHDCIgJMfCo4dgM4Y0IzW7a_3D_oNHDGLVsysdYLIwjwTlJqQ&_hsmi=405282956&tab=t.0#heading=h.rd4avlfy8r2v

https://padlet.com/alchemize/students-at-the-center-what-we-re-seeing-trying-and-building-ulmui2i1srjfn6oj?utm_campaign=37804935-Rethinking+the+Student+Experience&_hsenc=p2ANqtz-_CZeyElZEcPUuKM5hCMPaSpjaNIdf0E6MA1FK4ZRuJXf4rIqV4iXDO4w6Fni4blBJSqaFEIDbaWQqI6d4PCLfnl3bTtQ&_hsmi=405282956&utm_content=405282956

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Rethinking the Student Experience in the Age of AI

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These 3 charts show how AI is affecting wages, job quality and hiring decisions

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These 3 charts show how AI is affecting wages, job quality and hiring decisions

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These 3 charts show how AI is affecting wages, job quality and hiring decisions

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With rapid adoption boosting the value of AI skills and experience, recent research explains how this is affecting wages, job quality and hiring decisions.

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Beyond Bots: Facilitative AI for Student-to-Student Civil Discourse

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Beyond Bots: Facilitative AI for Student-to-Student Civil Discourse

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What Happens to Expertise When Students Skip the Struggle?

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What Happens to Expertise When Students Skip the Struggle?

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What Happens to Expertise When Students Skip the Struggle?

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When students use GenAI to skip the hard parts of learning, they miss the productive struggle that builds genuine expertise. This post explores why the “AI is just like a calculator” argument falls short, and why novices are most at risk of outsourcing the thinking that matters most.

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Tamara Tate Researching Literacy & Technology

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Tamara Tate Researching Literacy & Technology

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A site dedicated to exploring education, technology, and exceptional learners of all types.

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A few thoughts on a recent MIT study finding cognitive decline from AI use

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A few thoughts on a recent MIT study finding cognitive decline from AI use

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The final article in the updated ‘Teaching AI Ethics’ series explores how AI consolidates power in the hands of the wealthiest, most powerful companies in the world. #ArtificialIntelligence #AIEducation #AIEdu #AIInEd #AIInEdu

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CHEAT Manifesto Agentic AI Automates Cheating

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CHEAT Manifesto Agentic AI Automates Cheating

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Lesson Planning, Iterative AI Prompting, and Rubber Ducks

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Lesson Planning, Iterative AI Prompting, and Rubber Ducks

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Meta-Prompting: The Secret to Better AI Results

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Meta-Prompting: The Secret to Better AI Results

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Meta-Prompting: The Secret to Better AI Results

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Meta-prompting—using AI to help you craft better prompts—is one of the most powerful yet underutilized techniques for improving your AI interactions.

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I guess I’m carving out a space focused on AI metaphors. First it was working with Anna Mills to suggest “mirage” rather than “hallucination” as a metaphor for when AI gets it wrong. Now I’m fixated on this thing we keep hearing about the importance of keeping humans “in the loop” with AI. We need to stop this kind of talk. Our idea of properly deployed AI should not be just that there are humans “in the loop” of AI, presumably monitoring and adjusting AI activities to align them with human priorities and values. Instead, we should be working toward a world where AI only operates when i

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Gen AI Faculty Showcase (The New School)

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Gen AI Faculty Showcase (The New School)

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Navigating AI: Build Your Parent Playbook

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Navigating AI: Build Your Parent Playbook

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Navigating AI: Build Your Parent Playbook

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Ctrl + Alt + Achieve NotebookLM: K-12 AI Guidance and Policies

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Ctrl + Alt + Achieve NotebookLM: K-12 AI Guidance and Policies

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Not your computer? Use a private browsing window to sign in. Learn more about using Guest mode

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The Gazette • Western University's Student Newspaper

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Ivey professor experiments with AI in class

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Ivey professor Tony Frost used AI to mark his students’ midterm exams, and talked to the Gazette about his experience.

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Stacy Ybarra - The AI-Resistant Pedagogy Studio

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Stacy Ybarra - The AI-Resistant Pedagogy Studio

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Welcome! We gather at a fascinating and challenging time in education. The theme of this conference, ‘Community as Resistance,’ deeply resonates with our topic today. As AI tools become more prevalent, our role as educators shifts. We’re not just imparting knowledge, but fostering human capacities that resist automation and commodification. The strength of our communities – our classrooms, our departments, our institutions – will be our greatest asset in this resistance.

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https://drive.google.com/file/d/17jdDshpMugTqpKRYlpS5f8AAcwHo015V/view?pli=1

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Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

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Generative AI at U Chicago - Strategies for Designing AI-Resistant Assignments

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Generative AI at U Chicago - Strategies for Designing AI-Resistant Assignments

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Welcome! We gather at a fascinating and challenging time in education. The theme of this conference, ‘Community as Resistance,’ deeply resonates with our topic today. As AI tools become more prevalent, our role as educators shifts. We’re not just imparting knowledge, but fostering human capacities that resist automation and commodification. The strength of our communities – our classrooms, our departments, our institutions – will be our greatest asset in this resistance.

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AI Literacy Frameworks: List of Frameworks

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AI Literacy Frameworks: List of Frameworks

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Public NotebookLM - Strategies for Reducing Cheating and AI Misuse in Online Courses

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Public NotebookLM - Strategies for Reducing Cheating and AI Misuse in Online Courses

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Not your computer? Use Guest mode to sign in privately. Learn more about using Guest mode

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MLA Statement on Educational Technologies and AI Agents

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MLA Statement on Educational Technologies and AI Agents

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Statement on Educational Technologies and AI Agents

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The following statement of endorsement was drafted by the MLA Task Force on AI in Research and Teaching. The Executive Council approved it as an MLA statement in October 2025.Software, specifically learning management systems (LMSs), is as important to…

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LibreTexts (Humanities) - AI and College Writing: An Orientation (Anna Mills)

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LibreTexts (Humanities) - AI and College Writing: An Orientation (Anna Mills)

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Reinforcing Academic Integrity in the Age of AI: A Guide for Instructors

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Reinforcing Academic Integrity in the Age of AI: A Guide for Instructors

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A Guide for Instructors

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This Guide is a 2024 version of the 2023 Crafting Your GenAI & AI Policy : A Guide for Instructors , which was designed to help university/college instructors think through their courses and their assessments to decide if they need to be redesigned for the age of AI before crafting their GenAI and AI Policy.

That 2023 Guide is based on the premise that many courses and assessments likely need to be rethought and redesigned in the age of AI, and that instructors should consider if there is any redesigning they want to do before finalizing their GenAI & AI Policy.

This 2024 Guide better enables course instructors to navigate through the material, but also provides some more fundamentals - the "first day of class is around the corner, what do I need to do now" strategies. Fundamentals like understanding how GenAI works, choosing my GenAI policy, securing assessments, talking about GenAI with my students, identifying misuse and addressing suspected misuse. For those instructors already comfortable with the fundamentals, they can find material in this Guide to go further by assessing the impact of GenAI on my course assessments and "delving" ( see what I did there? ) into assessment redesign.

(last updated September 10, 2024; site is still in the process of being updated/added to. If you want anything added or see corrections to be made, please email tbg@ucsd.edu)

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Everything you need to know to Craft a GenAI Policy for Your Courses

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Doug Holton-Hillsborough College-Strategies for Reducing Student Misuse of AI in Your Courses + Handout

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Doug Holton-Hillsborough College-Strategies for Reducing Student Misuse of AI in Your Courses + Handout

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https://docs.google.com/document/d/1CKGVICgEjkfp2hVkXQR4y8rNOKRajgjk5cbot52pE9Y/edit?tab=t.0#heading=h.ayac8oyj4keb

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Strategies for Reducing Student Misuse of AI in Your Courses

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In the Room Where It Happens: Generative AI Policy Creation in Higher Education

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In the Room Where It Happens: Generative AI Policy Creation in Higher Education

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In the Room Where It Happens: Generative AI Policy Creation in Higher Education

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To develop a robust policy for generative artificial intelligence use in higher education, institutional leaders must first create "a room" where dive

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Designing Pedagogy-Driven AI for Language Learning

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Designing Pedagogy-Driven AI for Language Learning

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The LMS Is Dead; Long Live Online Teaching

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The LMS Is Dead; Long Live Online Teaching

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Inside Higher Ed | Higher Education News, Events and Jobs

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The LMS Is Dead; Long Live Online Teaching (opinion)

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Our online classes are built on outdated infrastructure.