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Microsoft has made a new 3D-generation system available as open source. This means that researchers, developers, and studios can access the model, its code, and its training information. They can run it locally and adapt it for different projects. The system can create three-dimensional objects from images and export them for use in programs such as Blender and Unreal Engine. The system is also very fast. Microsoft reports that it can create a 5123 3D object (a highly detailed 3D model made using a grid of 512 × 512 × 512 small points, called voxels) in about three seconds when using a powerful NVIDIA H100 computer. Larger and more detailed objects may take between 17 and 60 seconds. However, this technology requires expensive equipment, Linux, and an NVIDIA graphics card with at least 24 GB of memory. For therapists, 3D generation may provide new ways to create visual and interactive materials. An occupational therapist, for example, could create a model of a chair, kitchen tool, wheelchair, or bathroom. This model could help patients understand safety issues, practise daily activities, or discuss changes to their home environment. Three-dimensional models may be especially helpful for patients who learn better through pictures and objects than through spoken explanations. Therapists could use them to demonstrate how to hold an object, move through a room, or use an assistive device. This may support children and people with communication, cognitive, or learning difficulties. The technology could also support education and research. Students and professionals might create virtual homes, workplaces, or community spaces to study accessibility, balance, mobility, attention, and problem-solving. However, a realistic-looking model is not always accurate. AI may create objects with incorrect sizes, shapes, or details, so therapists must check every model before using it. Open-source access may allow researchers to study the system more carefully and explain how it was used. Nevertheless, open source does not mean that the technology is automatically safe or clinically tested. Results depend on the image, instructions, and editing process. Privacy is also important when using patient photographs, home images, or body scans. Therapists remain responsible for all professional decisions. AI should support clinical judgment, not replace it. Professionals should record how each model was created, explain AI use when appropriate, protect patient information, and make sure the final material is understandable and suitable for the patient. Microsoft’s 3D-generation system may become useful in therapy, education, and research. Its value lies in helping professionals create personalised and interactive materials more easily. Safe use will require careful checking, privacy protection, clear documentation, and further research into its benefits and limitations.

Artificial intelligence can now create very realistic images, videos, and written text. In the past, people looked for obvious signs, such as strange hands, unusual faces, spelling mistakes, or robotic writing. These signs may still appear, but they are becoming less reliable. AI systems are improving quickly, and people can also edit AI-generated content before sharing it. Many people believe they are good at identifying AI content. However, research shows that this is often difficult. People may perform only slightly better than chance when deciding whether an image, video, or piece of writing was created by AI. This means that therapists, teachers, and researchers may feel confident about their decision and still be wrong. Important decisions should therefore never be based only on personal impressions. It is useful to understand that unusual content does not always mean that it was made by AI. Writing may sound different because a person is using English as a second language, translating their ideas, or using a formal style. An image may look strange because of poor lighting, low quality, or heavy editing. These possibilities should be considered before making an assumption. Therapists and researchers should be aware of two main risks. The first is trusting AI content too quickly because it looks professional or sounds confident. The second is becoming suspicious of genuine material simply because it looks polished or unusual. A false accusation may harm a client, student, colleague, or research participant, especially when it affects treatment, education, employment, or professional reputation. A safer approach is to ask where the material came from and how it was created. When possible, professionals can review the original file, the date it was produced, and any records showing how it was edited. It may also help to compare the material with clinical notes, previous work, direct observations, or other reliable information. No single clue can prove that something is real or AI-generated. AI detection tools can sometimes be useful, but they should not be treated as final judges. These tools can incorrectly label human writing as AI-generated, especially when the writing is formal, simple, translated, or written by someone who uses English as an additional language. They can also fail to identify AI writing that has been changed by a person. A detector’s result should be treated as a reason to ask further questions, not as proof. This issue is especially important in therapy. A client may use an AI-generated image, story, or character to communicate feelings that are difficult to express directly. The material may not describe a real event, but it may still represent a real emotional experience. However, if the material is being used to document abuse, injury, safeguarding concerns, or a legal matter, the therapist should seek additional evidence and follow professional procedures. AI can also produce clinical notes, summaries, referral letters, and research drafts. Although the writing may be fluent, the information may not be correct. AI can leave out important details, change the meaning of events, or present uncertain information as fact. Professionals should check important information against original records, assessment results, and their own observations before using it. The most important skill is not becoming perfect at spotting AI. Instead, therapists and researchers need to learn how to manage uncertainty. They should consider the purpose of the material, the possible consequences of being wrong, and the quality of the available evidence. Professionals should be transparent about their use of AI tools and remain responsible for their decisions. Careful questioning, respect, and professional judgment are more reliable than quick conclusions.

Scientific research often requires many different tools. Researchers may need to search medical databases, analyse data, write computer code and use powerful computers. Managing these tasks can be difficult and time-consuming. Anthropic’s Claude Science is a new AI workbench designed to bring many of these activities together in one place. Claude Science can help scientists and research teams search the literature, organise information, analyse data and create scientific figures. It supports areas such as genomics, proteomics, neuroscience, drug research and structural biology. In simple terms, it works like a digital research assistant that can support several stages of a scientific project. A key feature is that Claude Science can save the code and computer environment used to create its results. For example, when it produces a graph, it can also save the instructions behind that graph. This allows researchers to check the work and repeat the analysis later. Such transparency is important because scientific findings should be understandable and reproducible. The system can also display complex information, including three-dimensional protein structures, genome data and chemical structures. These visual tools may help researchers understand difficult material. However, attractive figures should not automatically be treated as reliable evidence. Researchers must still check the data, methods and conclusions. Claude Science can also help manage computer resources. It may prepare an analysis plan and send tasks to a laboratory’s computer system or to online GPU services. This could help research teams complete complex analyses more efficiently. However, faster computer processing does not guarantee that the research question, data or statistical method is correct. Early users have reported time savings in drug design, neuroscience and glioma genetics research. These reports are promising, but they mainly come from early users and the company itself. Independent studies are still needed to determine how often the system makes mistakes and whether it improves the quality of scientific work. For clinicians and therapists, Claude Science could help locate research articles, summarise evidence, organise datasets and prepare early drafts of reports. It should support professional reasoning, not replace it. Clinicians must still decide whether the information is accurate, relevant and appropriate for a specific patient or clinical setting. The platform includes a separate reviewer agent that checks citations, calculations and possible errors. This may be useful, but it cannot replace experienced human review. AI may identify a missing reference while failing to recognise problems such as biased sampling or an incorrect interpretation of cause and effect. Privacy is another important concern. Claude Science is designed to keep sensitive research data within the user’s own systems or institutional infrastructure. Even so, organisations must follow privacy laws, research ethics rules and local policies. Confidential patient information should never be entered into an AI system without proper approval and protection. AI systems can produce incorrect information, repeat biases and make uncertain findings appear more definite. Users may also trust a well-written answer without checking the original evidence. Clinicians and researchers therefore need to question AI outputs, verify sources and understand the system’s limitations. Claude Science may help researchers spend less time managing technical tools and more time focusing on important questions. Its value, however, will depend on careful testing, responsible use and continued human oversight. It should be viewed as an assistant, not as an expert making final decisions. Professional judgment, ethical awareness and attention to evidence must remain central.

In inclusive classrooms, teachers and therapists carefully consider how to support each student. Some students need simpler language, more time or help organising information. However, support should also give students opportunities to ask questions, make choices and take an active role in learning. Generative artificial intelligence, or GenAI, is a technology that can create text and respond to questions. ChatGPT is one example. It may help students understand difficult ideas, find information and explore topics that interest them. However, its value depends not only on the answers it provides, but also on whether it helps students become more involved in their learning. A recent study published in the British Journal of Educational Technology by Rappa et al. (2026), titled “Can Generative AI Support the Learning Agency of Students with Disability? A Case Study of an Australian Secondary School,” explored this issue in a secondary science classroom. Three students with disability used ChatGPT while learning about gravity, friction, car brakes and motion. The researchers examined how students used the tool, what choices they made and what challenges they experienced. The study focused on learning agency. In simple terms, this means having the opportunity and ability to take part in decisions about learning. A student may choose to ask a question or request a simpler explanation, but may still need support with communication, attention, memory or planning. The students used ChatGPT in different ways. Arthur used it to learn more about gravity because he was already interested in science. Inez connected science ideas with real-life concerns, especially road safety. Louis asked science questions but also treated ChatGPT as a conversation partner. These examples show that students may use AI to make learning more meaningful and personal. However, choosing to use ChatGPT did not always mean that students could use it independently. Some found it difficult to write clear questions, understand long answers or decide whether information was correct. Teachers therefore modelled how to ask questions, request simpler explanations and ask for examples. This kind of support is similar to the scaffolding used in therapy and education. The findings suggest that GenAI may support students’ participation, but it does not replace human support. Teachers, therapists and education assistants may help students create questions, understand responses and connect AI information with classroom activities. The goal should be to build independence gradually, rather than simply giving students access to technology. There are also important limitations. Only three students from one Australian school were included, so the findings cannot represent all students with disability. More research is needed with larger groups, different subjects and students with more significant communication or learning needs. Ethically, professionals must consider privacy, accuracy and bias. Students should not enter personal information into AI systems. ChatGPT can provide incorrect or confusing answers, so adults must check its responses carefully. Students should also learn that AI can be useful, but it can make mistakes. In the end, GenAI does not create learning independence by itself. It may support learning agency when it helps students ask questions, explore interests and make choices about learning. For therapists and educators, AI should be used as an additional support, not as a replacement for professional judgement, relationships or communication. Reference Rappa, N. A., Nonis, K. P., Tang, K.-S., Cooper, G., Cooper, M., & Sims, C. (2026). Can generative AI support the learning agency of students with disability? A case study of an Australian secondary school. British Journal of Educational Technology. https://doi.org/10.1111/bjet.70048

In clinical work, we often think before we speak. A therapist may listen, consider a few possibilities, and only then respond. Much of this thinking remains invisible. Researchers at Anthropic recently described something similar in their AI system, Claude: an internal space called “J-space,” where the model briefly holds and works with ideas before producing an answer. J-space can be understood as a small mental workspace. It is not physical, but a pattern where important concepts are gathered and used. This idea is similar to what psychologists call a “global workspace,” where selected information becomes available for reasoning. In both humans and AI, thinking depends not just on knowledge, but on how that knowledge is organized in the moment. A simple example helps clarify this. Claude was asked about “the animal that spins webs.” Internally, the concept “spider” became active, leading to the correct answer: eight legs. When researchers replaced that internal concept with “ant,” the answer changed to six. This suggests that the AI forms an internal idea first, which then guides its response. For therapists, this process feels familiar. We often hold a working hypothesis in mind before speaking. These internal steps are essential, even if they are not visible. Importantly, when researchers reduced the influence of J-space, Claude still produced fluent language, but its reasoning became less accurate. This reminds us that clear or confident speech does not always reflect sound thinking. At the same time, we should be careful with this comparison. AI systems do not understand in the human sense. Their internal patterns come from data, not lived experience. While the idea of a “workspace” is helpful, it is still a simplified description of a complex system. For research, J-space offers a new way to study how AI reaches its answers. It may help identify errors, biases, or hidden signals, such as when a model detects misleading input. In clinical contexts, this could support decision-making, especially in complex cases—but it does not replace professional judgment. There are also important limits. These findings are early and mostly tested in specific models. We do not yet know how consistent or stable this feature is. Future systems may also become harder to interpret, not easier. Ethically, this raises questions about trust and responsibility. If clinicians use AI tools, they remain accountable for decisions. Internal signals like those in J-space should not be treated as fully reliable evidence. Transparency must include recognizing uncertainty and avoiding overconfidence in systems we do not fully understand. In the end, J-space does not prove that AI “thinks” like humans, but it highlights something important: reasoning often depends on hidden steps. For clinicians, this is already familiar. What is new is the chance to observe a similar process in machines, which may deepen how we reflect on both human and artificial thinking.

In therapy, much of our work happens between knowledge and presence. We are not only applying what we know, we are listening, observing, and adjusting in real time. Often, the challenge is not a lack of knowledge, but making sense of it in a way that fits the person in front of us. As MedGPT expands across Europe, many therapists are beginning to wonder how a tool designed for medical use might support this process. MedGPT first gained attention in France, where it reached more than 200,000 users in a short time. It is now being introduced in several European countries, including Germany, Italy, Spain, Portugal, Belgium, the United Kingdom, Ireland, the Netherlands, Poland, and Switzerland. The rollout starts with smaller groups of users in each country, allowing the system to adapt to different healthcare systems, languages, and clinical cultures. This gradual approach is important, because clinical practice is never one-size-fits-all. Although MedGPT was developed for physicians, it can still be meaningful for therapists. Our work often involves connecting physical, psychological, and social aspects of a person’s experience. We move between different models and perspectives, sometimes within a single session. In this context, a tool that helps organize and revisit these layers can support our thinking without replacing it. When we explored MedGPT, what stood out was not that it provided perfect answers, but that it helped structure reflection. For example, when considering a patient with chronic pain and fear of movement, the system brought together different contributing factors, such as physical sensitization, beliefs about pain, and avoidance behaviors, in a clear and organized way. It did not make decisions for us, but it helped us slow down and see connections more easily. In everyday practice, this kind of support can take simple forms. MedGPT can help rephrase complex information into clearer language for patients, summarize research when time is limited, or assist in organizing clinical notes. It can also support preparation for communication with other healthcare professionals. Used in this way, it becomes less of an authority and more of a thinking partner. At the same time, therapy remains deeply human. Our decisions are guided not only by knowledge, but by experience, intuition, and the relationship we build with each patient. No system can fully capture these elements. This makes it important to use AI carefully, protecting patient data, staying aware of possible biases, and remembering that responsibility for clinical decisions always remains with the therapist. There is also a broader question of how these tools influence our thinking over time. If used without reflection, they may lead us to rely too quickly on structured answers. But if used thoughtfully, they can help us become more aware of our reasoning, more curious, and more precise in our work. In the end, MedGPT’s expansion across Europe is not only about technology, but about how clinicians choose to engage with it. For therapists, it offers a way to support clinical thinking while staying grounded in the human side of care. Its value may lie less in the answers it gives, and more in how it helps us reflect, organize, and approach our work with greater clarity.

De nombreux cliniciens et éducateurs sont maintenant censés créer du contenu vidéo, que ce soit pour l'éducation des patients ou l'enseignement. Pour beaucoup, l'édition a toujours été une barrière—temps, technique, et parfois accablant. De nouveaux outils construits autour de systèmes comme Claude commencent à rendre ce processus plus gérable. Des mises à jour récentes ont rendu cela encore plus visible. Les agents d'IA de Claude peuvent désormais prendre de plus grandes parties du processus d'édition, aidant à structurer le contenu et à affiner les vidéos avec moins d'effort manuel. Pour ceux qui utilisent déjà des outils comme ChatGPT Plus/Pro ou Claude Pro, cela ouvre des façons plus avancées et pratiques de travailler avec la vidéo. Un autre changement vient d'outils tels que Palmier, conçus spécifiquement pour l'édition pilotée par l'IA. Avec une seule prompte, les utilisateurs peuvent guider l'ensemble du processus vidéo, tailler des clips, réorganiser les scènes, ajouter du B-roll et travailler directement dans une timeline. Il se sent moins comme l'édition traditionnelle et plus comme guider le processus à travers la conversation. Cela change le flux de travail de manière significative. Au lieu de se concentrer sur les étapes techniques, vous décrivez ce que vous voulez communiquer. Le système aide à façonner la structure, les visuels et les patins, et vous pouvez l'affiner par la rétroaction. Le processus devient plus itératif et moins dépendant de l'expertise technique. D'un point de vue cognitif, cela réduit la charge de gérer plusieurs petites tâches à la fois. La mécanique détaillée de l'édition se déplace en arrière-plan, permettant une plus grande attention à rester sur le message lui-même. Pour les cliniciens et les chercheurs, cela peut rendre la création de contenu plus accessible. Dans la pratique,

Lors d'une récente séance de supervision, une thérapeute a partagé son expérience en utilisant l'IA pour écrire des notes et des résumés. La sortie était rapide et bien écrite. Mais quand on lui a demandé si cela lui a vraiment facilité le travail, elle s'est arrêtée. "Ça fait gagner du temps," dit-elle, "mais je dois penser autant." Cette hésitation reflète quelque chose que beaucoup de cliniciens commencent à remarquer. Nous entendons souvent dire que l'IA améliorera considérablement la productivité. Certains suggèrent même qu'il pourrait rendre le travail dix fois plus rapide. Pourtant, dans les milieux cliniques quotidiens, le changement se sent plus subtil. La vitesse peut s'améliorer, mais la profondeur du travail, la compréhension, la décision, le maintien présent, reste tout aussi exigeant. Cela revient en partie à la façon dont nous pensons à la productivité. Dans la pratique clinique, il va au-delà de faire les choses rapidement. Il s'agit de comprendre les situations complexes et d'appliquer un jugement réfléchi. L'IA peut supporter des parties du processus, mais elle ne remplace pas la pensée derrière elle. Parfois, il crée même un sentiment de se précipiter dans les décisions plutôt que de s'asseoir avec elles. L'engagement joue également un rôle. Gallup (une firme mondiale d'analyse et de conseil spécialisée dans la recherche sur le lieu de travail; State of the Global Workplace Report, 2023) indique que seulement 20 % des employés du monde entier se sentent engagés dans leur travail. Lorsque de nombreuses personnes se sentent déjà déconnectées, l'introduction d'outils plus rapides n'améliore pas nécessairement l'expérience. Il peut simplement faciliter le passage à travers les tâches sans se sentir plus connecté à elles. La motivation est importante ici. Les gens ont tendance à s'engager davantage lorsqu'ils ressentent un sens du but et de la propriété dans ce qu'ils font. Quand A

Quelque chose de subtil change dans les environnements d'entraînement. Les tâches qui appartenaient aux débutants, l'écriture des premières ébauches, l'organisation d'idées, la suggestion d'interventions, peuvent maintenant se faire rapidement avec l'IA. La sortie semble souvent polie, voire impressionnante. Mais cela soulève une question tranquille et inconfortable : si ces premiers pas s'effacent, où se déroule le véritable apprentissage ? Pendant longtemps, apprendre un rôle clinique ou de recherche signifiait passer par des étapes imparfaites. Tu as essayé, tu as fait des erreurs, tu as fait des ajustements et essayé à nouveau. Cela pourrait parfois être frustrant, mais cela faisait partie du processus. Ces premiers efforts n'étaient pas seulement la pratique, ils étaient là où le jugement clinique a commencé à se former. Peu à peu, avec suffisamment d'exposition, vous avez commencé à reconnaître les modèles, à vous asseoir avec l'incertitude et à réfléchir de manière plus souple. Maintenant, beaucoup de ces premières tâches peuvent être gérées par l'IA. Une étude réalisée par Dell-Acqua et ses collègues (2025), impliquant 776 employés chez Procter & Gamble, a montré qu'une personne utilisant l'IA pouvait obtenir des résultats semblables à ceux d'une équipe entière travaillant sans elle. Si cette direction continue, il n'est pas difficile d'imaginer que les milieux de travail embauchent moins de personnel junior et cherchent plutôt des gens qui peuvent faire un peu de tout avec le soutien de l'IA. Ce changement touche quelque chose d'important sur la façon dont nous apprenons. La recherche psychologique a été claire sur ce sujet pendant un certain temps: l'apprentissage approfondit quand nous sommes activement impliqués, surtout quand nous luttons un peu, faisons des erreurs, et réfléchissons sur eux. Quand cet effort disparaît, la compréhension peut devenir plus mince. Vous pourriez arriver à la bonne réponse, mais sans vraiment saisir comment vous y êtes arrivé. En clin

Pensez à la dernière fois que vous vous êtes assis avec un cas clinique complexe, des notes étalées, différentes possibilités à l'esprit, en essayant de donner un sens à tout. Ce processus demande généralement du temps, de la patience et une sorte de réflexion tranquille. Avec des outils comme NotebookLM, quelque chose change. L'outil aide maintenant à organiser ce que nous voyons, à faire avancer les connexions et parfois même à faire avancer notre raisonnement. Il commence à se sentir moins comme un outil que nous utilisons et plus comme quelque chose que nous pensons à côté. À mesure que ces systèmes deviennent plus capables, ils passent rapidement par l'information et offrent des perspectives structurées et claires. Cela peut être vraiment utile, surtout lorsque la charge de travail est lourde. Mais cela change aussi notre relation avec la connaissance. Lorsque les réponses sont plus rapides et plus soignées, nous pouvons nous trouver à passer moins de temps assis avec l'incertitude qui mène souvent à une compréhension plus profonde. Certaines des fonctionnalités les plus récentes rendent cela encore plus visible. La capacité d'exécuter des analyses, de traiter des données et de générer des résultats dans un espace élimine de nombreuses barrières habituelles. Les tâches qui une fois ont nécessité des efforts et plusieurs étapes se déroulent maintenant en douceur. Alors que cela ouvre de nouvelles possibilités, il peut également créer un peu de distance de la "comment" derrière les résultats. Il en va de même pour la production de rapports ou de présentations. Transformer les idées en quelque chose de structuré est devenu beaucoup plus facile, ce qui peut soutenir la communication de manière significative. Pourtant, il y a une question subtile qui persiste, quand quelque chose se réunit si rapidement, où a eu lieu la pensée plus profonde? Même la façon dont nous commençons notre travail change. Au lieu de soins