The emergence of generative AI, particularly large language models (LLMs), has opened the door for student-centered and active learning methods like project-based learning (PBL). However, PBL poses practical implementation challenges for educators around project design and management, assessment, and balancing student guidance with student autonomy. The following research documents a co-design process with interdisciplinary K-12 teachers to explore and address the current PBL challenges they face. Through teacher-driven interviews, collaborative workshops, and iterative design of wireframes, we gathered evidence for ways LLMs can support teachers in implementing high-quality PBL pedagogy by automating routine tasks and enhancing personalized learning. Teachers in the study advocated for supporting their professional growth and augmenting their current roles without replacing them. They also identified affordances and challenges around classroom integration, including resource requirements and constraints, ethical concerns, and potential immediate and long-term impacts. Drawing on these, we propose design guidelines for future deployment of LLM tools in PBL.

We present DreamLLM-3D, a composite multimodal AI system behind an immersive art installation for dream re-experiencing. It enables automated dream content analysis for immersive dream-reliving, by integrating a Large Language Model (LLM) with text-to-3D Generative AI. The LLM processes voiced dream reports to identify key dream entities (characters and objects), social interaction, and dream sentiment. The extracted entities are visualized as dynamic 3D point clouds, with emotional data influencing the color and soundscapes of the virtual dream environment. Additionally, we propose an experiential AI-Dreamworker Hybrid paradigm. Our system and paradigm could potentially facilitate a more emotionally engaging dream-reliving experience, enhancing personal insights and creativity.

The variational autoencoder (VAE) is a simple and efficient generative artificial intelligence method for modeling complex probability distributions of various types of data, such as images and texts. However, it suffers some main shortcomings, such as lack of interpretability in the latent variables, difficulties in tuning hyperparameters while training, producing blurry, unrealistic downstream outputs or loss of information due to how it calculates loss functions and recovers data distributions, overfitting, and origin gravity effect for small data sets, among other issues. These and other limitations have caused unsatisfactory generation effects for the data with complex distributions. In this work, we proposed and developed a polynomial hierarchical variational autoencoder (PH-VAE), in which we used a polynomial hierarchical date format to generate or to reconstruct the data distributions. In doing so, we also proposed a novel Polynomial Divergence in the loss function to replace or generalize the Kullback-Leibler (KL) divergence, which results in systematic and drastic improvements in both accuracy and reproducibility of the re-constructed distribution function as well as the quality of re-constructed data images while keeping the dataset size the same but capturing fine resolution of the data. Moreover, we showed that the proposed PH-VAE has some form of disentangled representation learning ability.

The rapid evolution of large language models (LLMs) has transformed human-computer interaction (HCI), but the interaction with LLMs is currently mainly focused on text-based interactions, while other multi-model approaches remain under-explored. This paper introduces VTutor, an open-source Software Development Kit (SDK) that combines generative AI with advanced animation technologies to create engaging, adaptable, and realistic APAs for human-AI multi-media interactions. VTutor leverages LLMs for real-time personalized feedback, advanced lip synchronization for natural speech alignment, and WebGL rendering for seamless web integration. Supporting various 2D and 3D character models, VTutor enables researchers and developers to design emotionally resonant, contextually adaptive learning agents. This toolkit enhances learner engagement, feedback receptivity, and human-AI interaction while promoting trustworthy AI principles in education. VTutor sets a new standard for next-generation APAs, offering an accessible, scalable solution for fostering meaningful and immersive human-AI interaction experiences. The VTutor project is open-sourced and welcomes community-driven contributions and showcases.

Generative artificial intelligence (AI) models, such as diffusion models and OpenAI's ChatGPT, are transforming medicine by enhancing diagnostic accuracy and automating clinical workflows. The field has advanced rapidly, evolving from text-only large language models for tasks such as clinical documentation and decision support to multimodal AI systems capable of integrating diverse data modalities, including imaging, text, and structured data, within a single model. The diverse landscape of these technologies, along with rising interest, highlights the need for a comprehensive review of their applications and potential. This scoping review explores the evolution of multimodal AI, highlighting its methods, applications, datasets, and evaluation in clinical settings. Adhering to PRISMA-ScR guidelines, we systematically queried PubMed, IEEE Xplore, and Web of Science, prioritizing recent studies published up to the end of 2024. After rigorous screening, 144 papers were included, revealing key trends and challenges in this dynamic field. Our findings underscore a shift from unimodal to multimodal approaches, driving innovations in diagnostic support, medical report generation, drug discovery, and conversational AI. However, critical challenges remain, including the integration of heterogeneous data types, improving model interpretability, addressing ethical concerns, and validating AI systems in real-world clinical settings. This review summarizes the current state of the art, identifies critical gaps, and provides insights to guide the development of scalable, trustworthy, and clinically impactful multimodal AI solutions in healthcare.

OpenAI's upcoming GPT-5 release will integrate its o3 reasoning model and be available to free users, CEO Sam Altman revealed in a roadmap he shared on X. He said the company is also working to simplify how users interact with ChatGPT. "We want AI to'just work' for you; we realize how complicated our model and product offerings have gotten," Altman wrote. "We hate the model picker as much as you do and want to return to magic unified intelligence." In its current iteration, forcing ChatGPT to use a specific model, such as o3-mini, involves either tapping the "Reason" button in the prompt bar or one of the options present in the model picker, which appears after the chatbot answers a question.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. On Monday, Elon Musk and a group of investors made an unsolicited offer to buy ChatGPT parent company OpenAI for 97.4 billion. This ticked off OpenAI CEO Sam Altman not only because the company isn't for sale, but because the offer is insultingly low. OpenAI is reportedly in talks to raise new money in a funding round led by SoftBank at a 300 billion valuation, which would make it the most valuable privately held company in the world. Altman's return fire was equally petty, with him refusing via tweet before offering to buy Musk's social media company X for a decimal-sliding 9.74 billion.

Adobe said today that it's bringing AI-generated video, aka the Firefly Video Model, to Adobe Premiere Pro plus its Firefly generative art service. Unlike its generative AI image capabilities, however, it won't be free. AI-generated video has been available for months. In December, OpenAI released Sora, its ability to craft AI video clips of several seconds from a text prompt. What Adobe is offering is authenticity.

According to a new report from Reuters (spotted by Thurrott), OpenAI could finalize the design of its first 3nm AI chip in the coming months, with the goal of starting mass production at TSMC in 2026. The chip is being developed by a team of 40 OpenAI employees in collaboration with Broadcom. The project is being led by Richard Ho, OpenAI's new head of hardware, who previously worked on solutions for Google's infrastructure and cloud services. According to Reuters, OpenAI's chip will be able to both train and run AI models, but initially it'll be used mainly for inference (running AI models) and to a limited extent within the company's infrastructure. Demand for Nvidia's AI chips remains extremely high right now, with companies like OpenAI, Microsoft, Meta, and Google investing billions in AI data centers.

Gladstone A.I. co-founders and CEOs Edouard Harris and Jeremie Harris explain the major role that A.I will play in national security and warfare on'The Will Cain Show.' Vice President JD Vance will attend an AI summit in Paris, France, a French official said anonymously. FREE FROM BIAS: Vice President JD Vance told world leaders in Paris on Tuesday that the United States intends to remain the dominant force in artificial intelligence and warned that the European Union's far tougher regulatory approach to the technology could cripple it. 'TRYING TO SLOW US DOWN': OpenAI CEO Sam Altman said Elon Musk is "probably just trying to slow us down" with his bid to purchase the company, insisting on Tuesday that it is not for sale. 'MASS SURVEILLANCE': OpenAI CEO Sam Altman predicts that artificial general intelligence will lead to lower costs for many goods, but has also warned that AI could be leveraged by authoritarian governments aiming to control people. TRANSLATED TRUTH: Whether you have an iPhone or an Android, these apps have got you covered with features like live speech translation, text input and even AI-powered sign and menu translation.