Things to Do in L.A. Tap to enable a layout that focuses on the article. Homework faces an existential crisis. Has AI made it pointless? Students wait for a celebration of high test scores to begin at La Tijera Academy of Excellence in Inglewood on Wednesday. This is read by an automated voice.

This paper presents two studies on how Brazilian children (ages 9--11) use conversational agents (CAs) for schoolwork, discovery, and entertainment, and how structured scaffolds can enhance these interactions. In Study 1, a seven-week online investigation with 23 participants (children, parents, teachers) employed interviews, observations, and Cognitive Work Analysis to map children's information-processing flows, the role of more knowledgeable others, functional uses, contextual goals, and interaction patterns to inform conversation-tree design. We identified three CA functions: School, Discovery, Entertainment, and derived ``recipe'' scaffolds mirroring parent-child support. In Study 2, we prompted GPT-4o-mini on 1,200 simulated child-CA exchanges, comparing conversation-tree recipes based on structured-prompting to an unstructured baseline. Quantitative evaluation of readability, question count/depth/diversity, and coherence revealed gains for the recipe approach. Building on these findings, we offer design recommendations: scaffolded conversation-trees, child-dedicated profiles for personalized context, and caregiver-curated content. Our contributions include the first CWA application with Brazilian children, an empirical framework of child-CA information flows, and an LLM-scaffolding ``recipe'' (i.e., structured-prompting) for effective, scaffolded learning.

Adam Raine was just 16 when he started using ChatGPT for help with his homework. While his initial prompts to the AI chatbot were about subjects like geometry and chemistry – questions like: "What does it mean in geometry if it says Ry 1" – in just a matter of months he began asking about more personal topics. "Why is it that I have no happiness, I feel loneliness, perpetual boredom anxiety and loss yet I don't feel depression, I feel no emotion regarding sadness," he asked ChatGPT in the fall of 2024. Instead of urging Raine to seek mental health help, ChatGPT asked the teen whether he wanted to explore his feelings more, explaining the idea of emotional numbness to him. That was the start of a dark turn in Raine's conversations with the chatbot, according to a new lawsuit filed by his family against OpenAI and chief executive Sam Altman.

It should come as no surprise that students the world over are using ChatGPT and other artificial intelligence chatbots to cheat. On homework, on tests, and on anything else you care to mention. After all, why work something out yourself when there's an AI chatbot waiting and willing to do the hard work for you? This is obviously a problem in need of fixing, and OpenAI's answer is a Study Mode that's now baked into ChatGPT. The idea is to stop students from simply asking ChatGPT to tell them the answer to a question, and to have ChatGPT teach them how to answer the question for themselves.

An example of how'study mode' would work. Experts say it is'especially useful' for homework help, test prep and learning new topics It also features knowledge checks in the form of quizzes and open–ended questions, along with personalised feedback. The mode can also easy be toggled on and off during a conversation. Those wanting to use it should select'Study and learn' from tools in ChatGPT. 'Instead of doing the work for them, study mode encourages students to think critically about their learning', Robbie Torney, senior director of AI Programs at Common Sense Media said.

Effective teaching requires adapting instructional strategies to accommodate the diverse cognitive and behavioral profiles of students, a persistent challenge in education and teacher training. While Large Language Models (LLMs) offer promise as tools to simulate such complex pedagogical environments, current simulation frameworks are limited in two key respects: (1) they often reduce students to static knowledge profiles, and (2) they lack adaptive mechanisms for modeling teachers who evolve their strategies in response to student feedback. To address these gaps, \textbf{we introduce a novel simulation framework that integrates LLM-based heterogeneous student agents with a self-optimizing teacher agent}. The teacher agent's pedagogical policy is dynamically evolved using a genetic algorithm, allowing it to discover and refine effective teaching strategies based on the aggregate performance of diverse learners. In addition, \textbf{we propose Persona-RAG}, a Retrieval Augmented Generation module that enables student agents to retrieve knowledge tailored to their individual learning styles. Persona-RAG preserves the retrieval accuracy of standard RAG baselines while enhancing personalization, an essential factor in modeling realistic educational scenarios. Through extensive experiments, we demonstrate how our framework supports the emergence of distinct and interpretable teaching patterns when interacting with varied student populations. Our results highlight the potential of LLM-driven simulations to inform adaptive teaching practices and provide a testbed for training human educators in controlled, data-driven environments.

Since the release of ChatGPT in late 2022, generative artificial intelligence has trickled down from adults in their offices to university students in campus libraries to teenagers in high school hallways. Now it's reaching the youngest among us, and parents and teachers are grappling with the most responsible way to introduce their under-13s to a new technology that may fundamentally reshape the future. Though the terms of service for ChatGPT, Google's Gemini and other AI models specify that the tools are only meant for those over 13, parents and teachers are taking the matter of AI education into their own hands. Inspired by a story we published on parents who are teaching their children to use AI to set them up for success in school and at work, we asked Guardian readers how and why – or why not – others are doing the same. Though our original story only concerned parents, we have also included teachers in the responses published below, as preparing children for future studies and jobs is one of educators' responsibilities as well.

We look at reasoning on GSM8k, a dataset of short texts presenting primary school, math problems. We find, with Mirzadeh et al. (2024), that current LLM progress on the data set may not be explained by better reasoning but by exposure to a broader pretraining data distribution. We then introduce a novel information source for helping models with less data or inferior training reason better: discourse structure. We show that discourse structure improves performance for models like Llama2 13b by up to 160%. Even for models that have most likely memorized the data set, adding discourse structural information to the model still improves predictions and dramatically improves large model performance on out of distribution examples.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Decades ago, when I was a new teacher, my colleagues and I used to joke about the constant barrage of changes. "Public education is a moving target you can never hit," a mentor teacher once told me. Huge shifts are coming again. "I'm afraid I'm going to be put in prison for being trans," one of my students said to me after class last week.

Unlike some of the robots we've seen at CES 2025, Mi-Mo doesn't have a face, but it still looks a little familiar thanks to its resemblance to the iconic Pixar lamp. Mi-Mo is still just a prototype, but there are some interesting ideas behind the unusual-looking robot walking around the show floor. The creation of Japanese firm Jizai, the company describes it as a "general purpose AI robot" that "thinks and acts" on its own. It has a built-in camera and microphones, which allows it to move around and respond to voice prompts and commands. It runs on multiple large language models that enable its voice and image recognition capabilities.