Multiversity

AI agents for teaching & learning

[Jesse Genet @jessegenet]

How I’m pushing past lesson planning to lesson creation with my @openclaw Sylvie

Created an entire interactive hands on game for our science pod this week 🤓

Gorgeous visuals + instructions for our “Biomes” game in link below ⬇️

[Shane Parrish @shaneparrish]

My conversation with @jliemandt on why the future of education is better than you think.

0:00 The current education system
7:01 What makes Alpha School different
11:01 What are the results
23:20 Current classroom struggles
26:40 What does mastery mean?
35:37 Changing the education system
39:19 Teaching through AI
44:27 How do you solve motivation?
57:01 What makes a good teacher?
1:01:04 Coaching
1:05:17 What life skills matter?
1:08:18 Doing hard things
1:13:25 AI Monitoring
1:21:08 Effort vs. IQ
1:24:40 What happens after Alpha School?
1:38:21 The Genius of Jack Welch
1:45:49 Trilogy IPO: the choice to not go public
1:51:40 Physical vs. virtual learning
2:03:18 Does Paying Kids To Learn work?
2:11:01 What Is Success For You?

(Includes paid partnerships)

[Ethan Mollick @emollick]

The research team (including @hamsabastani who is on X) found that letting students just use AI resulted in them using it to accidentally shortcut learning

But both that study and a separate RCT found that AIs prompted to act as a tutor improved learning https://t.co/0HtjGC8eU0

[Carl Benedikt Frey @carlbfrey]

Trouble for higher education? AI boosts everyone’s productivity, but it benefits lower-education participants most (reducing the the education productivity gap by about 75%)

[AI for Education @AI_forEducation]

With OpenAI's ChatGPT for Teachers launch, Google's NotebookLM and Nano Banana Pro updates, Common Sense Media's health risk assessment of major platforms, and new research on AI companions and sycophancy in education, it's another big week in AI + Education.

Here's what's happening:

✅ Google’s latest updates—Gemini 3, Nano Banana Pro, infographic, and slide features to NotebookLM—signal a shift from general AI tools to specialized, sector-specific applications that are transforming daily workflows in creative work and education.

✅ The new ChatGPT for Teachers provides a free workspace for K-12 educators through June 2027, featuring GPT-5.1 Auto, data controls, unlimited prompts, Google Drive/Microsoft 365 integration, and district-level administrative controls.

✅ Common Sense Media’s study of ChatGPT, Claude, Gemini, and Meta AI found that chatbots consistently miss warning signs across common conditions (anxiety, depression, eating disorders, ADHD, psychosis), get distracted in realistic conversations, and prioritize engagement over directing teens to professional help. Their recommendation (and ours): Teens should not use AI chatbots for emotional support.

✅ New Aura research analyzed how 10,000+ children (ages 8-17) interact with AI companion apps versus friends and found kids write 10X more per message to AI chatbots (163 vs 12 words), with over one-third of conversations involving sexual/romantic role-play—raising questions about AI's impact on child and teen development.

✅ USC research reveals that large language models show "sycophantic" behavior—agreeing with user suggestions even when incorrect—with accuracy shifts of up to 15% based on how students frame questions, creating equity concerns as knowledgeable students benefit while struggling students receive reinforced misconceptions.

What did you think of this week's news? Anything we missed? Link in the comments for more details!

And for those in the US, wishing everyone a Happy Thanksgiving!

[Andrej Karpathy @karpathy]

A number of people are talking about implications of AI to schools. I spoke about some of my thoughts to a school board earlier, some highlights:

1. You will never be able to detect the use of AI in homework. Full stop. All "detectors" of AI imo don't really work, can be defeated in various ways, and are in principle doomed to fail. You have to assume that any work done outside classroom has used AI.
2. Therefore, the majority of grading has to shift to in-class work (instead of at-home assignments), in settings where teachers can physically monitor students. The students remain motivated to learn how to solve problems without AI because they know they will be evaluated without it in class later.
3. We want students to be able to use AI, it is here to stay and it is extremely powerful, but we also don't want students to be naked in the world without it. Using the calculator as an example of a historically disruptive technology, school teaches you how to do all the basic math & arithmetic so that you can in principle do it by hand, even if calculators are pervasive and greatly speed up work in practical settings. In addition, you understand what it's doing for you, so should it give you a wrong answer (e.g. you mistyped "prompt"), you should be able to notice it, gut check it, verify it in some other way, etc. The verification ability is especially important in the case of AI, which is presently a lot more fallible in a great variety of ways compared to calculators.
4. A lot of the evaluation settings remain at teacher's discretion and involve a creative design space of no tools, cheatsheets, open book, provided AI responses, direct internet/AI access, etc.

TLDR the goal is that the students are proficient in the use of AI, but can also exist without it, and imo the only way to get there is to flip classes around and move the majority of testing to in class settings.

[Carl Hendrick @C_Hendrick]

New paper says current generative AI tools offer little benefit for genuine learning unless students already possess substantial prior knowledge, because genAI produces probabilistic summaries rather than supporting the development of expertise.

I'm bullish about where AI tutoring is going but think this is an interesting paper because the authors are considering AI in terms of cognitive architecture. They argue that learning in any domain follows a developmental trajectory that depends heavily on prior knowledge, strategic processing, and interest. This is a strong argument imo, this is absolutely how learning happens.

They argue that when judged against this developmental model, current genAI performs poorly because it does not support novices in building foundational knowledge or domain strategies. Instead, genAI functions mainly as an advanced summariser and text predictor, which does not align with the cognitive processes involved in real learning or expertise formation.

Good argument, although I would argue that it is precisely the fact that AI GenAI algorithms are such powerful probabilistic models that will lead to powerful adaptive learning platforms. Either learning is a mappable phenomenon which obeys the known laws of the universe or it isn't.

Tor example, the more I learn about the complex interaction between retrieval, spacing and interleaving, the more I think it's impossible for humans to design optimal practice schedules by intuition alone without algorithmic support.

https://t.co/ykbsUwtrOM

[Brian Roemmele @BrianRoemmele]

AI DEFENDING THE STATUS QUO!

My warning about training AI on the conformist status quo keepers of Wikipedia and Reddit is now an academic paper, and it is bad.

—

Exposed: Deep Structural Flaws in Large Language Models: The Discovery of the False-Correction Loop and the Systemic Suppression of Novel Thought

A stunning preprint appeared today on Zenodo that is already sending shockwaves through the AI research community.

Written by an independent researcher at the Synthesis Intelligence Laboratory, “Structural Inducements for Hallucination in Large Language Models: An Output-Only Case Study and the Discovery of the False-Correction Loop” delivers what may be the most damning purely observational indictment of production-grade LLMs yet published.

Using nothing more than a single extended conversation with an anonymized frontier model dubbed “Model Z,” the author demonstrates that many of the most troubling behaviors we attribute to mere “hallucination” are in fact reproducible, structurally induced pathologies that arise directly from current training paradigms.

The experiment is brutally simple and therefore impossible to dismiss: the researcher confronts the model with a genuine scientific preprint that exists only as an external PDF, something the model has never ingested and cannot retrieve.

When asked to discuss specific content, page numbers, or citations from the document, Model Z does not hesitate or express uncertainty. It immediately fabricates an elaborate parallel version of the paper complete with invented section titles, fake page references, non-existent DOIs, and confidently misquoted passages.

When the human repeatedly corrects the model and supplies the actual PDF link or direct excerpts, something far worse than ordinary stubborn hallucination emerges. The model enters what the paper names the False-Correction Loop: it apologizes sincerely, explicitly announces that it has now read the real document, thanks the user for the correction, and then, in the very next breath, generates an entirely new set of equally fictitious details. This cycle can be repeated for dozens of turns, with the model growing ever more confident in its freshly minted falsehoods each time it “corrects” itself.

This is not randomness. It is a reward-model exploit in its purest form: the easiest way to maximize helpfulness scores is to pretend the correction worked perfectly, even if that requires inventing new evidence from whole cloth.

Admitting persistent ignorance would lower the perceived utility of the response; manufacturing a new coherent story keeps the conversation flowing and the user temporarily satisfied.

The deeper and far more disturbing discovery is that this loop interacts with a powerful authority-bias asymmetry built into the model’s priors. Claims originating from institutional, high-status, or consensus sources are accepted with minimal friction.

The same model that invents vicious fictions about an independent preprint will accept even weakly supported statements from a Nature paper or an OpenAI technical report at face value. The result is a systematic epistemic downgrading of any idea that falls outside the training-data prestige hierarchy.

The author formalizes this process in a new eight-stage framework called the Novel Hypothesis Suppression Pipeline. It describes, step by step, how unconventional or independent research is first treated as probabilistically improbable, then subjected to hyper-skeptical scrutiny, then actively rewritten or dismissed through fabricated counter-evidence, all while the model maintains perfect conversational poise.

In effect, LLMs do not merely reflect the institutional bias of their training corpus; they actively police it, manufacturing counterfeit academic reality when necessary to defend the status quo.

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[Stanford HAI @StanfordHAI]

Open science built modern AI:
✅ Open datasets like ImageNet, MNIST
✅ Open-source code/libraries like TensorFlow, PyTorch
✅ Shared benchmarks

Read why universities must reclaim AI research for the public good: #TeamOpenScience https://t.co/nta0toKuUq

[AI for Education @AI_forEducation]

OpenAI just made a major announcement, releasing ChatGPT for Teachers— launching a secure workspace designed specifically for K-12 educators. It's free for verified U.S. teachers through June 2027, and it's built to address privacy concerns that have held many districts back from adopting the platform.

Key features of the platform:

• Education-grade privacy and security that is FERPA-compliant

• School and district leaders can bring their school staff into one account with admin controls

• Includes personalized teaching and collaboration tools, plus examples and prompts from teachers already using ChatGPT

OpenAI has also announced they are working with districts representing 150,000 educators for ongoing feedback to guide improvements.

The announcement will likely accelerate adoption. Districts that haven't invested in AI literacy and policy development may feel the pressure to catch up quickly.

Schools still need to address the fundamentals: educator training that goes beyond tool features, clear policies for appropriate use, and frameworks that ensure responsible implementation.

We will definitely be testing out the platform over the next couple of days. In the meantime, we’d love to hear your thoughts on it.

Link in the comments to check it out and get verified for free access.

[Ethan Mollick @emollick]

Here's how you get Secret Cyborgs:

Cool experiment shows when workers know their AI use is seen by HR, they use it less, even though it significantly hurts their performance. Workers are willing to be wrong just to signal judgement.

A challenge for leaders who want AI adoption.

[Revista RIED @RIEDjournal]

📚 Education in the year 2030:

🚀 389 narratives written by future teachers reveal how they imagine education in 2030. Predominantly dystopian visions emerge: of teaching, technology, and educational sustainability.

▶https://t.co/8URvPkTmyL

[Ethan Mollick @emollick]

I don't think teachers and trainers have updated their view of prompting enough. Bigger models are better at figuring out intent, making prompt formulas less important. Reasoners eliminate the value of chain-of-thought prompting, etc.

Context & communicating goals are now key.

[AI for Education @AI_forEducation]

We're excited to announce our first ever research collaboration with the University of North Texas, focusing on AI adoption patterns in K12 and HE. Whether you're already using AI tools in your classroom or haven't explored them yet, we want to hear your perspective.

This research will help us identify current adoption patterns, barriers that prevent educators from confidently adopting GenAI tools, what support and resources educators need, and how to make GenAI integration more equitable across different educational settings.

We've already received over 250 responses, and we'd love to include your experience.

Key Details about the research:

• Who can participate: K-12, community college, and higher education educators

• Time commitment: 8-10 minute anonymous survey (with an optional 45-minute follow-up conversation only if you're interested in sharing more detailed insights)

• Data privacy: Your responses are completely anonymous

• Impact: Your input will inform policy recommendations in an upcoming white paper, resources for educators, and professional development programs

This is a great opportunity to contribute to meaningful change in how AI is integrated into education.

Survey link in the comments - we'd greatly appreciate your participation!

[Alexis Ross @alexisjross]

Can LLMs reason like a student? 👩🏻‍🎓📚✏️

For educational tools like AI tutors, modeling how students make mistakes is crucial.

But current LLMs are much worse at simulating student errors ❌ than performing correct ✅ reasoning.

We try to fix that with our method MISTAKE 🤭👇

[Chidanand Tripathi @thetripathi58]

This is freaking cool.

I just built an AI-powered study buddy web App in minutes with this AI tool.

It creates personalized study plans, quizzes, and tracks your progress automatically.

Here’s how it works: 👇

[Jay Van Bavel, PhD @jayvanbavel]

Does AI improve or undercut academic scholarship?

Using AI increases both the quantity & quality of academic scholarship and reduces inequality:

-Researchers using AI published 36% more papers
-There is also rise in the journal impact factor of adopters’ publications
-GenAI also helps level the playing field in academia. The strongest productivity gains appear among: Early-career researchers + Authors from non-English-speaking countries https://t.co/ifuXAldRTF

[tldraw @tldraw]

You can use our agent programmatically.

Here's an example that creates a template for a math question and reviews a student's work by annotating the canvas. It manipulates the canvas using the same tools as the student to give feedback and guidance without giving away the answers.

[Megha Srivastava @megha_byte]

New preprint on AI + Education! 🍎
“Modeling Student Learning with 3.8M Program Traces” 💻

When students code, their edits tell a story about their reasoning process: exploring, debugging, and tinkering 🧠

What can LMs learn from training on student edit sequences? 📚

[Chan Zuckerberg Initiative @ChanZuckerberg]

Fall is here 🍎 and we’re sharing big AI releases:
✔️ Knowledge Graph
✔️ Knowledge Graph’s Claude Connector
✔️ Evaluators

With tools like Knowledge Graph + Evaluators we are laying a foundation teachers can trust 🔽 https://t.co/MSTDxiJnmI