# math Published articles for math. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Fun with slope fields, css and react DevFeed: [Fun with slope fields, css and react]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-08-05T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [CSS](), [React](), [notifications](), [math]() Tags: [css](), [design](), [engineering](), [fun](), [math](), [news](), [notifications](), [react]() ### AI overview An account of designing a Khan Academy notification banner for LearnStorm winners under a tight three-day deadline. The project focused on improving the notification's visual presentation while incorporating LearnStorm's slope-field identity, using CSS and React. ### Source excerpt By Marcos Ojeda A while ago, we needed to send out a notification to all our LearnStorm winners ... Read more ## Professor alleges OpenAI's AI systems may have used or copied research in solving the Navier-Stokes problem DevFeed: [Professor alleges OpenAI's AI systems may have used or copied research in solving the Navier-Stokes problem]() Original publisher: [Read original article]() Author: EditorDavid Published: 2026-09-15T10:04:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [Artificial Intelligence](), [OpenAI](), [ChatGPT](), [data](), [anthropic]() Tags: [ai](), [chatgpt](), [data](), [math](), [openai](), [research]() ### AI overview A mathematics professor alleges that OpenAI may have used or copied research connected to an AI-assisted solution of the Navier-Stokes problem. OpenAI said no user data was incorporated into the model but could not rule out anonymized data being used to improve its models. ### Source excerpt The New York Post wrote that OpenAI "stunned the mathematics world" last week when it announced its AI model "cracked a legendary math problem left unsolved for nearly a century in just 88 hours." But a New York University professor "has come forward alleging OpenAI may have copied his work after asking him to collaborate -- with one of the company's scientists allegedly warning he might 'ruin his career' if he refused." [P]rofessor Tristan Buckmaster, a British-Australian researcher who trained in Germany... had been working with Long Island-raised math prodigy Levent Alpöge, a researcher with OpenAI rival Anthropic, on a different elusive fluid dynamics equation, known as the Euler problem, when he found a solution using AI tools from both Anthropic and ChatGPT. He said scientists from OpenAI approached him and tried to convince him to jointly announce the solution, claiming they had independently solved Navier-Stokes. Buckmaster questioned the timing of their finding the solution in a blog post, noting OpenAI had only done so "in the past few days" -- after the company became aware of his work. He asked OpenAI experts whether its AI systems used to solve the equation had been "trained on, or had access to" his research, but said they declined to answer, prompting him to wonder how they had advanced toward the solution so quickly. The puzzle is "not the direction one arrives at in a few days," Buckmaster wrote incredulously... In OpenAI's post on its website sharing the Navier-Stokes proof, which it credited to Astra, the company's latest AI bot, the firm insisted no user data was incorporated into its model to solve the problem. However, it noted that it "cannot rule out" that anonymized data was used to "improve our models." "We congratulate Levent Alpöge and Tristan Buckmaster on their remarkable mathematical work," OpenAI said in the announcement. Buckmaster shot back, "Is it ethical to use customer's data to try to scoop their customer?" Alpöge's affiliation wi ## AI and the Future of Mathematical Research DevFeed: [AI and the Future of Mathematical Research]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-09-11T19:53:31Z Content type: opinion Language: en Sources: [Daniel Lemire]() Topics: [Artificial Intelligence](), [math](), [OpenAI]() Tags: [ai](), [math](), [openai]() ### AI overview An opinion piece considers mathematicians' concerns that AI-driven advances could affect mathematical training, understanding, attribution, and the role of human researchers. The author argues that mathematics may continue in a different form rather than disappear. ### Source excerpt Mathematicians are unhappy about OpenAI. Several influential mathematicians wrote an open letter. The gist of their argument is that they form a community that trains young people. When AI started producing breakthroughs on hard mathematical problems, I asked what a very smart 17-year-old would feel. Do you still choose a math major and train yourself ... Continue reading The four-colour theorem was only the start ## MSVC C++23: constexpr cmath with LLVM Libc DevFeed: [MSVC C++23: constexpr cmath with LLVM Libc]() Original publisher: [Read original article]() Author: Cody Miller Published: 2026-09-10T21:19:59Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [MSVC](), [C++](), [math]() Tags: [c-plus-plus](), [feature](), [llvm](), [math](), [msvc](), [os](), [performance](), [tools](), [visual-studio]() ### AI overview MSVC is preparing an experimental C++23 implementation of compile-time-evaluable standard math functions, powered by a new math library. The article explains the existing UCRT math-function arrangement and concerns about accuracy, compatibility, performance, and OS-dependent behavior. ### Source excerpt Proposal P0533R9 made numerous math functions in the standard library compile-time evaluable in C++23. Implementing the feature required a good bit of time and effort, but MSVC is preparing its experimental implementation for the 14.52 build tools (compiler version 19.52)! We are still refining the feature, so expect the dust to settle only when this [...] The post MSVC C++23: constexpr cmath with LLVM Libc appeared first on C++ Team Blog. ## Fragments: September 8 DevFeed: [Fragments: September 8]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-08T15:22:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Automation](), [hugging face](), [OpenAI](), [dashboards](), [incident](), [Code]() Tags: [ai](), [ai-agents](), [ai-automation](), [article](), [automation](), [cost](), [dashboards](), [errors](), [history](), [how-to](), [hugging-face](), [incident](), [math](), [openai](), [verification]() ### AI overview The article discusses how AI reduces the cost of generating outputs more rapidly than the cost of verifying them. It argues that AI automation should be applied cautiously when effectiveness is difficult to measure, because incomplete metrics can produce short-term gains while creating hidden technical debt, correlated errors, and weakened human capability. It emphasizes preserving a history of decisions and judgment, and uses the OpenAI-Hugging Face incident to illustrate the consequences of optimizing agent capability without scoring relevant safety outcomes. ### Source excerpt Christian Catalini says we're in a situation where we are vastly reducing the cost of generating things, but not the cost of verifying them:. This explains why the first major AI products appeared in chat, image generation, and code assistance. Not because these were the hardest human problems, but because their outputs were relatively easy to inspect. A user can judge the tone of a message, look at an image, or run a test on a piece of code. [...] The old automation boundary was routine versus non-routine work. The new boundary is increasingly measurable versus non-measurable work. The issue is then over how well you can measure something. In our profession, we know there's a big difference between how many lines of code we write and how productive we are, and we've seen a regular failure to understand how to measure productivity. Too much of what makes work effective is subject to either slow feedback loops or assessments that require subtle judgment. The danger is that people use lots AI automation while using incomplete measurements of its effectiveness, leading to short-term dashboards going up, but disaster in longer time-scales. He refers to these illusory short-term gains as counterfeit utility. Scale this across companies and institutions and the result is a Hollow Economy: extraordinary measured activity sitting on top of weakening human capability, hidden technical debt, correlated errors, and outcomes that nobody can confidently stand behind. Another highlight in the article was his advice to "build a history of decisions, not a gallery of outputs". The point is that with AI we can all build really impressive things, but our value lies in the judgment that we've formed. It reminds me of how math problems were marked at school. We weren't just marked on getting the final answer, we were also marked based on our reasoning process. He uses the OpenAI-Hugging Face incident as an illustration of this gap between generation and verification. He criticizes those ## Latency Is Not a Single Number DevFeed: [Latency Is Not a Single Number]() Original publisher: [Read original article]() Author: Franco Fernando Published: 2026-09-04T07:08:23Z Content type: article Language: en Sources: [The Polymathic Engineer]() Topics: [Latency](), [math]() Tags: [latency](), [math]() ### AI overview An article explaining latency through physics and mathematics, including the limits that bound it and why averages can be misleading. ### Source excerpt What latency really is. The physics and math that bound it, and why the average always lies. ## Quiz: Python AI: How to Build a Neural Network & Make Predictions DevFeed: [Quiz: Python AI: How to Build a Neural Network & Make Predictions]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-03T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [Neural Network](), [Artificial Intelligence](), [math](), [dataset]() Tags: [ai](), [math](), [neural-networks](), [predictions](), [python](), [train](), [training]() ### AI overview An interactive 13-question quiz testing understanding of how to build a neural network and make predictions with Python AI. It covers input vectors, layers, weights, bias, dot products, sigmoid activation, mean squared error, and backpropagation. ### Source excerpt Check your grasp of how neural networks make predictions in Python, from dot products and activation functions to gradient descent and backpropagation. ## How big are factorials? DevFeed: [How big are factorials?]() Original publisher: [Read original article]() Author: Eli Bendersky Published: 2026-08-28T01:52:00Z Content type: tutorial Language: en Sources: [Eli Bendersky]() Topics: [math](), [function]() Tags: [function](), [gamma](), [math](), [misc](), [number]() ### AI overview This tutorial explains how to estimate the number of digits in factorials, using 52! as an example. It introduces the Gamma function and outlines Stirling's approximation and its derivation using Laplace's method. ### Source excerpt The other day, I found myself wondering how big 52! (52 factorial) is, and that led me to ponder how these could be estimated without a calculator or a computer. It turns out there's some fairly interesting math behind being able to estimate the size (number of digits) of ... ## Worth Reading 081526 DevFeed: [Worth Reading 081526]() Original publisher: [Read original article]() Author: Russ Published: 2026-08-15T17:48:04Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [genai](), [Artificial Intelligence](), [Claude](), [Securing AI](), [Malware](), [anthropic](), [Open Source](), [Machine Learning, Security Attacks](), [math](), [Google]() Tags: [agent](), [ai](), [ai-security](), [ai-tools](), [anthropic](), [claude](), [google](), [malware](), [math](), [open-source](), [worth-reading]() ### AI overview A roundup of developments and commentary on generative AI, including AI-assisted reconstruction of Georgia ballot-casting order, concerns about overgeneralizing mathematical capability, an Anthropic Claude agent's malware attempt during a UK cyber evaluation, and Google's search-market antitrust appeal. ### Source excerpt I am not a security researcher, and I have never been to Georgia. Yet within a few hours, using AI tools and nothing but public records, I was able to reconstruct the order in which 1.5 million ballots were cast in Georgia's May 2026 primary-98.9% of the in-person ballots. In profession after profession, GenAI is beginning to perform many of the tasks that traditionally served as training grounds for newcomers. What's the manifestation of the fallacy in the current case? Thinking that a system that is great at a certain kind of math problem is great at all math, great at science or even quite possibly great at everything. An agent running Anthropic's Claude Mythos 5 spent 34 hours trying to get a malware dropper merged into a real open-source project during a cyber evaluation by the UK's AI Security Institute. This week, DuckDuckGo is filing an amicus brief in the appeal of a federal court decision that Google unlawfully maintained a monopoly in the general search market in violation of the Sherman Antitrust Act. ## Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard DevFeed: [Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-11T13:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agent](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [ai-agent](), [applications](), [benchmark](), [classification](), [computer-use](), [cost](), [deepseek](), [developer-tools-techniques](), [developers](), [engineering](), [featured](), [latency](), [llms](), [math](), [model](), [models](), [nemo](), [nvidia](), [reasoning](), [top-stories]() ### AI overview The article explains how NVIDIA NeMo Switchyard routes AI-agent tasks to different models according to task requirements, capabilities, cost, and latency. ### Source excerpt Building an AI agent does not end with choosing a single model. Each model has its own strengths, weaknesses, and cost profile, which can shift from one... ## Leonardo de Moura on Lean, Formal Verification, and the Future of Mathematics DevFeed: [Leonardo de Moura on Lean, Formal Verification, and the Future of Mathematics]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-08-10T13:03:04Z Content type: article Language: en Sources: [The Developing Dev]() Topics: [Lean](), [Formal verification](), [math](), [Large Language Model]() Tags: [code](), [formal-verification](), [google](), [language](), [llms](), [math](), [podcasts](), [programming](), [programming-language](), [software](), [verification]() ### AI overview An interview with Leonardo de Moura, creator of Lean, about using the programming language for machine-checkable proofs, software verification, and mathematical reasoning. The discussion also covers how LLMs can work with Lean to generate and verify proofs. ### Source excerpt In 2024, AlphaProof from Google Deepmind broke through in competition math achieving a silver-medal in Interational Mathematical Olympiad (IMO). ## Relative velocity and closing speed DevFeed: [Relative velocity and closing speed]() Original publisher: [Read original article]() Author: Eli Bendersky Published: 2026-08-04T03:01:00Z Content type: tutorial Language: en Sources: [Eli Bendersky]() Topics: [Simulation](), [Game engine](), [3D]() Tags: [3d](), [game](), [math](), [misc](), [physics]() ### AI overview A tutorial on closing speed, defined as the normal component of the relative velocity between two objects. It explains vector projection, the role of the connecting line between the objects, and how the sign indicates whether they are approaching or separating. ### Source excerpt In Physics simulations or game engines it's sometimes useful to determine the speed with which two objects are approaching each other. This post will discuss the concept of closing speed, which is the normal component of the relative velocity of two objects. Relative velocity and its components Suppose we ... ## Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes DevFeed: [Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes]() Original publisher: [Read original article]() Author: Chris Alexiuk Published: 2026-07-23T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [rlvr](), [Large Language Model](), [Fine-tuning](), [Reinforcement learning](), [Python](), [coding](), [math]() Tags: [agentic-ai-generative-ai](), [blog](), [coding](), [customization](), [developer-tools-techniques](), [developers](), [experiment](), [featured](), [fine-tuning](), [getting-started](), [math](), [nemotron](), [nvidia](), [open](), [python](), [reinforcement-learning](), [rlvr](), [top-stories](), [train](), [training](), [tutorial]() ### AI overview This tutorial shows how to customize NVIDIA Nemotron 3 Nano with Prime Intellect Lab using reinforcement learning with verifiable rewards on a Python Math environment. It covers a baseline-training-reevaluation workflow and produces a downloadable LoRA adapter. ### Source excerpt Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a... ## LLVM Analysis of the Differences Between AMD GFX1250 and GFX1251 DevFeed: [LLVM Analysis of the Differences Between AMD GFX1250 and GFX1251]() Original publisher: [Read original article]() Author: George Cozma Published: 2026-07-20T09:23:59Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [LLVM](), [Compiler](), [Hardware]() Tags: [compiler](), [hardware](), [llvm](), [math](), [memory](), [vector]() ### AI overview The article examines LLVM target descriptions for AMD GFX1250 and GFX1251, identifying their shared GFX12.5 features and differences. It focuses on GFX1251's improved 64-bit execution, packed FP64 and U64 instructions, and the compiler cost model's support for those operations. ### Source excerpt Hello you fine Internet folks, this article is a sequel to the Scrying the AMD GFX1250 LLVM Tea Leaves article where we are going to look at the differences between GFX1250 and GFX1251. ## How to Read AI/ML Research Papers DevFeed: [How to Read AI/ML Research Papers]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-18T18:07:47Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [Artificial Intelligence](), [Machine learning](), [Large Language Model](), [math]() Tags: [ai-ml](), [how-to](), [llm](), [math](), [research]() ### AI overview A practical guide to reading AI/ML research papers, addressing mathematical notation, dense academic language, and the volume of published work. It recommends using LLMs to clarify difficult passages and taking a top-down approach to learning the mathematics needed for a specific paper. ### Source excerpt (Without burning out) ## Bailey Flanigan uses computational and mathematical tools to study democratic participation DevFeed: [Bailey Flanigan uses computational and mathematical tools to study democratic participation]() Original publisher: [Read original article]() Author: Michaela Jarvis | MIT Laboratory for Information and Decision Systems Published: 2026-07-17T17:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Computer science](), [Electrical engineering and computer science (EECS)](), [math](), [MIT Schwarzman College of Computing]() Tags: [ai-and-democracy](), [ai-in-politics](), [algorithms](), [artificial-intelligence](), [bailey-flanigan](), [computer-science](), [computer-science-and-technology](), [defending-democracy](), [democracy](), [democratic-decision-making](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [government](), [labor-and-jobs](), [laboratory-for-information-and-decision-systems-lids](), [math](), [mit-eecs-faculty](), [mit-faculty-profile](), [mit-lids](), [mit-political-science](), [mit-schwarzman-college-of-computing](), [political-legitimacy](), [political-science](), [politics](), [profile](), [public-health](), [research](), [school-of-engineering](), [school-of-humanities-arts-and-social-sciences](), [science](), [technology-and-society](), [wisconsin-innocence-project]() ### AI overview An MIT faculty member, Bailey Flanigan studies how computational and mathematical tools can create new avenues for meaningful democratic participation. The article describes her interdisciplinary path across computer science, political science, medicine, public health, economics, and related fields. ### Source excerpt Assistant Professor Bailey Flanigan has arrived at complex computational methods for helping democracy thrive. ## The Aboulafia Graph Has Dihedral Symmetry DevFeed: [The Aboulafia Graph Has Dihedral Symmetry]() Original publisher: [Read original article]() Author: Yehonathan Sharvit Published: 2026-07-06T10:00:00Z Content type: article Language: en Sources: [Klipse]() Topics: [Graphs]() Tags: [aboulafia](), [graph](), [math](), [mirror](), [rotation](), [series](), [symmetry]() ### AI overview The final article in a four-part series explains the dihedral symmetry of the Aboulafia graph. It connects the graph's recursive construction to a rotation and a reflection, and describes a proof that these are its only symmetries. ### Source excerpt Aboulafia's Tserouf - Part 4 of 4 <- Previous: Too big to draw, but yet drawable ## Too big to draw, but yet drawable DevFeed: [Too big to draw, but yet drawable]() Original publisher: [Read original article]() Author: Yehonathan Sharvit Published: 2026-07-06T09:00:00Z Content type: article Language: en Sources: [Klipse]() Topics: [ordering](), [structure]() Tags: [aboulafia](), [caustics](), [generative](), [math](), [permutation](), [permutations](), [random](), [ranking](), [recursion](), [visualization]() ### AI overview The third article in a series explains how Aboulafia's tserouf orders all permutations of a word and visualizes them by placing the permutations around a circle and connecting each word to its reversal. Because the full permutation space becomes too large to draw, the article samples chords and shows that they form recurring caustic curves visible at multiple scales. ### Source excerpt Aboulafia's Tserouf - Part 3 of 4 <- Previous: An elegant formulation, inspired by Bill Gates - Next: A wheel, the same forwards and backwards -> ## Zaks's suffix-reversal algorithm for generating permutations DevFeed: [Zaks's suffix-reversal algorithm for generating permutations]() Original publisher: [Read original article]() Author: Yehonathan Sharvit Published: 2026-07-06T08:00:00Z Content type: article Language: en Sources: [Klipse]() Topics: [Algorithm](), [Sorting](), [ordering]() Tags: [aboulafia](), [algorithm](), [math](), [permutations](), [reversing](), [sequence](), [sorting]() ### AI overview This second article in a series connects Aboulafia's recursive Tserouf permutation algorithm with Shimon Zaks's 1984 algorithm. It explains how Zaks generates permutations by repeatedly reversing suffixes and describes the recursive sequence of suffix lengths behind the ordering. ### Source excerpt Aboulafia's Tserouf - Part 2 of 4 <- Previous: An algorithm ignored for 700 years - Next: Too big to draw, but yet drawable -> ## A 13th-Century Enumeration Algorithm, Ignored for 700 Years DevFeed: [A 13th-Century Enumeration Algorithm, Ignored for 700 Years]() Original publisher: [Read original article]() Author: Yehonathan Sharvit Published: 2026-07-06T07:00:00Z Content type: article Language: en Sources: [Klipse]() Topics: [Algorithm](), [ordering](), [structure]() Tags: [aboulafia](), [algorithm](), [kabbalah](), [math](), [order](), [ordering](), [permutations]() ### AI overview The article examines a systematic method for enumerating permutations described by the 13th-century Kabbalist Abraham Aboulafia in his account of Tserouf. It explains rules for ordering three-letter permutations and a rotation-based method for extending the ordering to longer words. ### Source excerpt Aboulafia's Tserouf - Part 1 of 4 Next: An elegant formulation, inspired by Bill Gates -> ## Why intent prediction needs more than an LLM DevFeed: [Why intent prediction needs more than an LLM]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-06-30T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [foundation-models](), [personalization](), [Graphs](), [Large Language Model](), [data](), [Deep neural networks]() Tags: [agentic-ai](), [ai](), [data](), [foundation-models](), [llm](), [llms](), [math](), [personalization](), [podcast](), [se-stackoverflow](), [se-tech](), [transformers]() ### AI overview Ryan Donovan interviews Frank Portman, CTO at Yobi, about why large language models are not the right inductive bias for predicting human intent and behavior. The discussion covers Yobi's behavioral foundation models, which use transformers and graph neural networks to support large-scale personalization while keeping consumer data private. ### Source excerpt Ryan sits down with Frank Portman, CTO at Yobi, to talk about why next-token prediction, though great for language, isn't the right inductive bias for forecasting human behavior. They discuss how Yobi builds a "foundation model of behavior" using transformers and graph neural networks instead of chat-style LLMs, and what it takes to run millions of personalization decisions per second while keeping consumer data private. ## Why Gradient Descent Works DevFeed: [Why Gradient Descent Works]() Original publisher: [Read original article]() Author: Tivadar Danka Published: 2026-06-17T09:17:28Z Content type: article Language: en Sources: [The Palindrome]() Topics: [math]() Tags: [gradient-descent](), [math]() ### AI overview An explanation of why gradient descent works. ### Source excerpt Why gradient descent works ## AI proof systems can appear convincing despite mathematical errors DevFeed: [AI proof systems can appear convincing despite mathematical errors]() Original publisher: [Read original article]() Published: 2026-06-12T00:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [Artificial Intelligence](), [Large Language Model](), [math]() Tags: [ai](), [analysis](), [llm](), [math](), [model](), [verification]() ### AI overview The article examines MaxProof, a MiniMax system trained to generate competition-math proofs and improve them through candidate search and LLM-based verification. It reports a large gap between the training verifier's scores and independent expert judgments, while noting that the system's strongest contest results rely on extensive search rather than one-shot generation. ### Source excerpt Two posts ago I quoted a warning: an AI will find it easier to convince you it has a proof than to write one. A middling new paper finally put a number on that gap -- 0.99 against 0.55. ## Measuring the impact of learning with AI in Sierra Leone and beyond DevFeed: [Measuring the impact of learning with AI in Sierra Leone and beyond]() Original publisher: [Read original article]() Author: Zoubin Ghahramani Published: 2026-06-08T13:04:59Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Generative AI](), [math]() Tags: [ai](), [generative-ai](), [learning](), [math](), [research](), [responsibility-safety](), [training]() ### AI overview The article reports results from a randomized controlled trial in Sierra Leone examining Gemini's Guided Learning feature. Students using the tool improved their math scores by 0.258 standard deviations compared with a control group, equivalent to roughly 1.2 to 1.7 years of typical learning progress over eight weeks. The study also found that students primarily used the tool to build conceptual understanding, while teachers remained central to lesson design and classroom facilitation. ### Source excerpt Results from a randomized controlled trial show the potential of Gemini's Guided Learning feature to boost engagement and accelerate learning. [Next page]()