Commonaiverse is an interactive installation exploring human emotions through full-body motion tracking and real-time AI feedback. Participants engage in three phases: Teaching, Exploration and the Cosmos Phase, collaboratively expressing and interpreting emotions with the system. The installation integrates MoveNet for precise motion tracking and a multi-recommender AI system to analyze emotional states dynamically, responding with adaptive audiovisual outputs. By shifting from top-down emotion classification to participant-driven, culturally diverse definitions, we highlight new pathways for inclusive, ethical affective computing. We discuss how this collaborative, out-of-the-box approach pushes multimedia research beyond single-user facial analysis toward a more embodied, co-created paradigm of emotional AI. Furthermore, we reflect on how this reimagined framework fosters user agency, reduces bias, and opens avenues for advanced interactive applications.

The need for and challenges of teaching artificial intelligence (AI) at primary, secondary, and upper-secondary levels have been a major focus of recent academic discussions [1],[2],[3]. Often referred to as AI4K12 [4], this area explores global initiatives that introduce AI to students from kindergarten through high school. The rapid advancements in deep learning and generative AI technologies suggest AI will become a transformative force. This realisation has prompted governments and policymakers to recognise the need to prepare future citizens for a world heavily influenced by AI. As AI becomes increasingly integrated into information systems, concerns are mounting about citizens' ability to use these systems responsibly and understand the consequences of not doing so [5]. Furthermore, anxieties regarding AI's potential impact on societal sustainability highlight the need to equip future workforces with the skills to combine human creativity with AI's potential to create sustainable systems.

A huge, super-fast indestructible robot hand might seem like a terrifying prop from a science-fiction film. But this hefty 4.1kg (9.9lbs) hand is very real and is already being used to develop the next generation of AI robots. Designed by UK-based Shadow Robot Company, this three-fingered claw can go from fully open to closed in just 500 milliseconds. However, the robot hand is still tough enough to resist being bashed with hammers or pounded by pistons. That toughness is designed to help the hand survive the rigorous and often destructive process of teaching AI how to interact with the world.

The integration of artificial intelligence (AI) into education has the potential to transform the way we learn and teach. In this paper, we examine the current state of AI in education and explore the potential benefits and challenges of incorporating this technology into the classroom. The approaches currently available for AI education often present students with experiences only focusing on discrete computer science concepts agnostic to a larger curriculum. However, teaching AI must not be siloed or interdisciplinary. Rather, AI instruction ought to be transdisciplinary, including connections to the broad curriculum and community in which students are learning. This paper delves into the AI program currently in development for Neom Community School and the larger Education, Research, and Innovation Sector in Neom, Saudi Arabia s new megacity under development. In this program, AI is both taught as a subject and to learn other subjects within the curriculum through the school systems International Baccalaureate (IB) approach, which deploys learning through Units of Inquiry. This approach to education connects subjects across a curriculum under one major guiding question at a time. The proposed method offers a meaningful approach to introducing AI to students throughout these Units of Inquiry, as it shifts AI from a subject that students like or not like to a subject that is taught throughout the curriculum.

This article describes and evaluates a new online AI-creativity course. The course is based around three near-state-of-the-art AI models combined into a pop song generating system. A fine-tuned GPT-2 model writes lyrics, Music-VAE composes musical scores and instrumentation and Diffsinger synthesises a singing voice. We explain the decisions made in designing the course which is based on Piagetian, constructivist 'learning-by-doing'. We present details of the five-week course design with learning objectives, technical concepts, and creative and technical activities. We explain how we overcame technical challenges to build a complete pop song generator system, consisting of Python scripts, pre-trained models, and Javascript code that runs in a dockerised Linux container via a web-based IDE. A quantitative analysis of student activity provides evidence on engagement and a benchmark for future improvements. A qualitative analysis of a workshop with experts validated the overall course design, it suggested the need for a stronger creative brief and ethical and legal content.

Large language models (LLMs) based on transformer architectures have taken the world by storm, with ChatGPT quickly becoming a household name. While the concept of generative AI is not new and can be traced back to Jürgen Schmidhuber's (now at KAUST) work in the 1990s and even further into history, Ian Goodfellow's generative adversarial networks (GANs) and Google's transformers published in 2017 enabled the development and industrialization of multi-purpose AI. My teams have been working in this area since 2015 both in generative biology and generative chemistry, with AI-generated drugs in human clinical trials and the most advanced departments in pharma companies using our software, and we have utilized LLMs almost since they were first published. OpenAI's GPT has also been available to the public since 2020. However, the public release and consumerization of ChatGPT have taken the world by surprise and triggered a new cycle of hyper investment and productization of LLMs that are propagating into the search market. Although both Recurrent Neural Network (RNN) and transformer-based LLMs, as well as multimodal LLMs, are surprisingly good at language understanding and generation, I believe they are still as far from human-level consciousness as a calculator.

By studying how machine learning powers artificial intelligence, students can further develop their own thought processes and tackle societal impacts. When thinking of classes on artificial intelligence, you probably imagine students on a computer writing code. But that's not where Joseph South, chief learning officer for the International Society for Technology in Education, says educators should start. Instead, teachers should help students learn how to approach decisions the way digital programming might -- by working through information, finding patterns and making a choice. "At ISTE, we feel strongly students need to learn how the digital world works," South said.

Researchers have begun developing machine-learning models that can streamline the process by automatically finding information physicians need in …

Physicians often query a patient's electronic health record for information that helps them make treatment decisions, but the cumbersome nature of these records hampers the process. Research has shown that even when a doctor has been trained to use an electronic health record (EHR), finding an answer to just one question can take, on average, more than eight minutes. The more time physicians must spend navigating an oftentimes clunky EHR interface, the less time they have to interact with patients and provide treatment. Researchers have begun developing machine-learning models that can streamline the process by automatically finding information physicians need in an EHR. However, training effective models requires huge datasets of relevant medical questions, which are often hard to come by due to privacy restrictions.

In the past two years, the amount of artificial intelligence being used in our everyday lives has increased significantly. As a result, there is a greater demand for people who have the skills to work in this field, and it will continue to lead to the creation of many more jobs according to the Jobs of Tomorrow report. Areas such as artificial intelligence, data analytics, cloud computing, and cybersecurity are some of those mentioned in the report as likely to see an increase in demand for skilled workers which means that we need to do more to prepare our students for these careers and others that will evolve over time. There are big trends for this year about how AI will impact the world of work and the skills needed. It has been predicted that artificial intelligence will automate the production of 30% of all the content available on the Internet this year.