Generative artificial intelligence (GenAI) is transforming education, redefining the role of trainers and coaches in learning environments. In our study, we explore how AI integrates into the design process of learning materials, assessing its impact on efficiency, pedagogical quality, and the evolving role of human trainers and coaches. Through qualitative interviews with professionals in education and corporate training, we identify the following key topics: trainers and coaches increasingly act as facilitators and content moderators rather than primary creators, efficiency gains allow for a stronger strategic focus but at the same time the new tools require new skills. Additionally, we analyze how the anthropomorphism of AI shapes user trust and expectations. From these insights, we derive how tools based on GenAI can successfully be implemented for trainers and coaches on an individual, organizational, systemic, and strategic level.

Virtual Reality simulators offer a powerful tool for teacher training, yet the integration of AI-powered student avatars presents a critical challenge: determining the optimal level of avatar realism for effective pedagogy. This literature review examines the evolution of avatar realism in VR teacher training, synthesizes its theoretical implications, and proposes a new pedagogical framework to guide future design. Through a systematic review, this paper traces the progression from human-controlled avatars to generative AI prototypes. Applying learning theories like Cognitive Load Theory, we argue that hyper-realism is not always optimal, as high-fidelity avatars can impose excessive extraneous cognitive load on novices, a stance supported by recent empirical findings. A significant gap exists between the technological drive for photorealism and the pedagogical need for scaffolded learning. To address this gap, we propose Graduated Realism, a framework advocating for starting trainees with lower-fidelity avatars and progressively increasing behavioral complexity as skills develop. To make this computationally feasible, we outline a novel single-call architecture, Crazy Slots, which uses a probabilistic engine and a Retrieval-Augmented Generation database to generate authentic, real-time responses without the latency and cost of multi-step reasoning models. This review provides evidence-based principles for designing the next generation of AI simulators, arguing that a pedagogically grounded approach to realism is essential for creating scalable and effective teacher education tools.

Large Language Model (LLM)-based multi-agent systems show promise for automating real-world tasks but struggle to transfer across domains due to their domain-specific nature. Current approaches face two critical shortcomings: they require complete architectural redesign and full retraining of all components when applied to new domains. We introduce Workforce, a hierarchical multi-agent framework that decouples strategic planning from specialized execution through a modular architecture comprising: (i) a domain-agnostic Planner for task decomposition, (ii) a Coordinator for subtask management, and (iii) specialized Workers with domain-specific tool-calling capabilities. This decoupling enables cross-domain transferability during both inference and training phases: During inference, Workforce seamlessly adapts to new domains by adding or modifying worker agents; For training, we introduce Optimized Workforce Learning (OWL), which improves generalization across domains by optimizing a domain-agnostic planner with reinforcement learning from real-world feedback. To validate our approach, we evaluate Workforce on the GAIA benchmark, covering various realistic, multi-domain agentic tasks. Experimental results demonstrate Workforce achieves open-source state-of-the-art performance (69.70%), outperforming commercial systems like OpenAI's Deep Research by 2.34%. More notably, our OWL-trained 32B model achieves 52.73% accuracy (+16.37%) and demonstrates performance comparable to GPT-4o on challenging tasks. To summarize, by enabling scalable generalization and modular domain transfer, our work establishes a foundation for the next generation of general-purpose AI assistants.

When it comes to AI at Adobe, my team has taken a comprehensive approach that includes investment in foundational AI, strategic adoption, an AI ethics framework, legal considerations, security, and content authentication. The rollout follows a phased approach, starting with pilot groups and building communities around AI. This approach includes experimenting with and documenting use cases like writing and editing, data analysis, presentations and employee onboarding, corporate training, employee portals, and improved personalization across HR channels. The rollouts are accompanied by training podcasts and other resources to educate and empower employees to use AI in ways that improve their work and keep them more engaged. While there are innumerable ways that CIOs can leverage generative AI to help surface value at scale for knowledge workers, I'd like to focus on digital documents--a space in which Adobe has been a leader for over 30 years.

Companies clamor to use Remie Michelle Clarke's voice. An award-winning vocal artist, her smooth, Irish accent backs ads for Mazda and Mastercard and is the sound of Microsoft's search engine, Bing, in Ireland. But in January, her sound engineer told Michelle Clarke he'd found a voice that sounded uncannily like hers someplace unexpected: on Revoicer.com, For a modest monthly fee, Revoicer customers can access hundreds of different voices and, through an artificial intelligence-backed tool, morph them to say anything -- to voice commercials, recite corporate trainings or narrate books. Revoicer advertised "Olivia" with a photo of a gray-haired woman, who appeared to be of Asian descent, and a blurb: "A deep, calm and kind voice. A 38-year-old brunette, Michelle Clarke looked nothing like "Olivia." But when she hit play, she was greeted with the jarring sound of what could only be her own voice: "Hello my dear ones, my name is Olivia," it said. "I have a soft and caring voice."

Over these past couple of months, ChatGPT has been all over the news. Many businesses are already leveraging the technology to get ahead of the competition. The Complete ChatGPT Artificial Intelligence OpenAI Training Bundle helps you follow suit, with four courses that showcase the hidden power of this AI platform. The training is worth a total of $800, but you can grab all four courses today for only $19.97 in a special price drop at TechRepublic Academy. Although ChatGPT has only just exploded onto the scene, the technology has had a massive impact.

We're putting together a few valuable insights for company executives in this episode, but it's so packed that, in the end, there's just something in it for everyone. This is because Afke Schouten, our special guest, has much to share with us! Paulo Nunes, your host and CEO at Two Impulse, knows this very well and lays down an open road to keep it all coming. Afke's mission is to help organizations generate true value with AI. She is the Head of Data & AI Strategy at Xebia Data, focusing on corporate training and consulting. Her previous background as a consultant, as well as a data scientist, AI strategist and analytics team lead at companies such as EY, Swiss Re, SwissQuant and AI Bridge allows Afke to help executives build AI strategies and become data-driven.

The world has been changing literally before our eyes. The pandemic, which represented the opening salvo to our entrance into the fourth Industrial Revolution, triggered a wave of disruptive transformation, of which we are only scratching the surface. The integration of newly instrumented physical, biological and digital worlds has given rise to an unprecedented number of'big bang disruptions,' the breadth and depth of which will herald the transformation of entire systems, creating and destroying product lines, markets and ecosystems. We are also entering the third wave of artificial intelligence (AI). In this era, we imbue human perception capabilities onto virtual assistants that deliver personalized experiences spanning multiple worlds.

A version of this post was originally published in Entrepreneur on February 1, 2022. I've been in the education business for decades as a senior lecturer, trainer and CEO. When people ask me about the biggest challenge that learners face, the first thing that comes to mind is that learners see training as something they "have to do." Now, let's think for a moment about this. How did we get here? Why aren't we talking about "want to do" or "happy to have the opportunity to do?"

Matt Harrison runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage. He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences. The structure and content of his books are based on first-hand experience teaching Python to many individuals.