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In a recent supervision session, a trainee shared an idea for a simple tool to help a patient track mood between sessions. Not long ago, this idea would likely have stayed theoretical unless a developer was involved. Today, with OpenAI just releasing Codex Sites, a written idea can be turned into a working app that can be shared through a simple link. This changes not only what we can build, but how we think about applying ideas in clinical care. What is new is not just faster coding, but a simpler process overall. In the past, building a tool required planning, design, programming, testing, and hosting. Each step required time and often different skills. Now, much of this can happen through a guided conversation with a system. This reduces the distance between having an idea and seeing it in action. From a clinical perspective, this can support how we think and reason. The concept of the “extended mind” suggests that tools can help us think more effectively (Clark & Chalmers, 1998). When clinicians can quickly turn ideas into small working tools, they can test, reflect, and refine their thinking. This may encourage a more active and flexible approach to problem-solving in practice. This shift has practical value across different areas. A therapist might create a simple app for mood tracking, coping reminders, or session feedback. A researcher might design a tool to collect data in a more tailored way. Instead of relying only on standard tools, clinicians can begin to shape tools that better fit their patients’ needs and contexts. However, ease of creation does not guarantee quality. A tool that works technically is not always clinically appropriate. Evidence-based practice still requires theory, research, and careful judgment (Sackett et al., 1996). Without this foundation, there is a risk of creating tools that are engaging but not effective, or even misleading. Looking at fields like design science can offer useful guidance. New tools are often developed in small steps, tested, and improved over time (Hevner et al., 2004). Clinicians can adopt a similar mindset, while maintaining attention to safety and validity. Iteration is valuable, but it must be guided by clinical knowledge and patient well-being. These developments may also change how clinicians see their role. Some may begin to act not only as practitioners, but also as creators of simple digital tools. This can feel empowering, but also unfamiliar. Many clinicians do not have formal training in technology, so support and education will be important for responsible use. Ethical considerations are central in this process. When clinicians create or use digital tools, they are responsible for how these tools affect patients. Transparency about how a tool works, how data is handled, and what its limits are is essential. Patients should clearly understand what they are using and how it may influence their care. When AI is involved, additional caution is needed. These systems can produce errors, reflect bias, or generate outputs that appear reliable but are not well supported. Clinicians must remain critical and thoughtful in their use. While the gap between idea and application is shrinking, clinical responsibility and careful judgment remain unchanged.

Anthropic, the company behind Claude, widely considered one of the most capable AI systems available today, did not build seven completely different models. What they built is simpler than it first appears. It is one system, organized as a ladder. The names can sound confusing at first, but in practice there are only five levels to understand, moving from fast and efficient to increasingly powerful forms of reasoning. Once you understand this ladder, the system becomes much easier to use. A therapist recently said, “I keep hearing different names, Haiku, Sonnet, Opus, and I don’t know which one I should use.” This is a very common experience. The difficulty is not the technology itself, but how it is presented. When there are too many names, it starts to feel complicated very quickly. A more helpful way to understand Claude is this: it is not many separate AI systems. It is one system organized as a ladder. Each name represents a different level of ability, moving from fast and simple to more powerful and complex. Once you see it this way, the confusion becomes much easier to manage. At the lowest level is Haiku. This is the fastest and most affordable option. It works well for simple, repetitive tasks that do not require deep thinking. For example, a therapist might use Haiku to summarize session notes, rewrite a paragraph, or organize brief information. It is helpful for saving time, but it is not designed for complex clinical reasoning. The next level is Sonnet, and this is the model most clinicians will find useful in everyday work. It offers a good balance between quality and cost. Sonnet can support tasks like writing case notes, developing treatment plans, or creating psychoeducational material. It is reliable and clear, without being too resource-intensive. For many therapists, this becomes the default choice. Above that is Opus, which is designed for more demanding thinking. This is the model to use when the work becomes more complex, such as exploring case formulations, comparing diagnostic possibilities, or integrating research into practice. It can handle more depth, but it also requires more resources, so it is usually best used when needed rather than all the time. At the top of the ladder are Fable and Mythos. These are not two different systems, but the same model with different settings. Fable includes safety guardrails, meaning it is more cautious when responding to sensitive or high-risk topics. Mythos has fewer restrictions and allows more open responses, but it is typically limited to expert use. For most clinical settings, Fable is the more appropriate and responsible choice. However, a recent development adds a key limitation: the US government has issued an export control directive suspending access to Fable 5 and Mythos 5 for foreign nationals, both inside and outside the United States. As a result, these systems may be abruptly unavailable to many users in practice. Thinking in terms of a ladder can help guide decisions in practice. Instead of asking, “Which model is best?”, it is more useful to ask, “How much support do I need for this task?” Simple tasks can stay at the lower levels, while more complex clinical questions may require moving higher. This approach is similar to how therapists already adjust interventions based on client needs. It is also important to remember that these tools support thinking, but do not replace clinical judgment. AI can help organize ideas, suggest possibilities, or clarify language, but it does not fully understand the client or the therapeutic relationship. The clinician remains responsible for interpreting and deciding what is appropriate. There are also ethical considerations to keep in mind. When using AI in clinical or research settings, it is important to be transparent about how it is used and to ensure that sensitive information is handled carefully. Clinicians should also be aware that AI systems can reflect biases or make errors, even when responses sound confident. Careful review and critical thinking are always necessary. As these tools become more common, they may begin to shape how clinicians write, think, and communicate. This creates new opportunities, but also new responsibilities. It will be important to continue reflecting on how AI fits into clinical practice, rather than using it automatically or without question. In the end, the goal is not to master every model or feature. It is simply to understand the basic structure: one ladder, with different levels of support. With this perspective, AI becomes less overwhelming and more practical. It can then serve as a helpful extension of clinical work, while the therapist remains at the center of decision-making and care.

In everyday clinical practice, many therapists are beginning to use AI tools to help with writing notes, organizing information, or reflecting on diagnoses. These tools can be useful, but they also raise an important question: do we really understand how they work? The launch of Apertus, a multilingual open-source AI model developed in Switzerland, invites us to think not only about what AI can do, but also about how much we can trust it. One key idea behind Apertus is digital sovereignty, which means having control over where data is stored and how it is used. For therapists, this is especially important because we work with sensitive patient information. When AI systems are owned by large companies abroad, it is not always clear how data is handled. Apertus aims to offer more local control, aligning better with ethical and legal responsibilities in healthcare. Many existing AI tools, like ChatGPT, are powerful but not fully transparent. We can see their outputs, but not easily understand how they are produced. For clinicians trained in careful reasoning, this can feel uncomfortable. Apertus offers a more open approach, allowing experts to examine and understand how the system works, which may support more informed use. Another important feature of Apertus is its multilingual ability. In therapy, language plays a key role in how patients express themselves. A system that works across languages may help reduce misunderstandings and support more inclusive care. However, cultural context remains complex, and no AI system can fully capture it. Because Apertus is open source, researchers and developers can study, adapt, and improve it. This supports transparency and collaboration, but it also means that different versions of the system may exist, which can make standardization more difficult in practice. Apertus is also designed as a public good, meaning it is freely accessible. This could make advanced AI tools more available to therapists and institutions with limited resources. However, maintaining such a system requires ongoing support, which remains a challenge. The institutions behind Apertus, EPFL and ETH Zurich, bring strong scientific credibility. Still, in clinical settings, trust develops over time. Therapists need to see how a tool performs in real situations before fully relying on it. Ethically, the use of AI in therapy requires careful attention. Even with transparent systems, clinicians remain responsible for their decisions. AI should support, not replace, clinical judgment. It is important to question outputs, understand limitations, and remain aware of possible biases. Apertus represents more than a new tool, it reflects a different approach to AI. It encourages clinicians to engage actively with technology rather than use it passively. As AI continues to evolve, the challenge will be to keep clinical responsibility, critical thinking, and patient trust at the center of practice.

Are we ever going to stop the development of AI? As these systems become increasingly intelligent, a deeper question emerges: when do we decide that enough is enough for humanity? In May 2026, Pope Leo XIV contributed to this global debate by warning that artificial intelligence must not take moral decision-making out of human hands. While his message speaks broadly, it resonates strongly within clinical practice, where efficiency, innovation, and ethical responsibility are already tightly intertwined. In everyday care, AI often appears in subtle ways. It summarizes patient histories, drafts reports, or suggests diagnoses. These tools can ease administrative burden, but their influence is not neutral. Over time, they begin to shape how clinicians organize information and approach decisions. This raises a critical question: where does clinical judgment truly reside? Clinical reasoning has traditionally been a reflective process grounded in human experience and patient interaction. As AI provides ready-made interpretations, there is a growing risk that parts of this reasoning become less deliberate, even if unintentionally. Responsibility, however, remains fully human. Clinicians are still accountable for evaluating and deciding whether to trust AI-generated outputs. This requires active engagement, asking not only “what does this suggest?” but also “why, and should I rely on it?” Beyond healthcare, some experts and voices within the AI industry have already called for slowing development, emphasizing the need for society to adapt. The debate is no longer just about capability, but about the pace of integration. Transparency adds another layer of concern. Many AI systems cannot clearly explain how they reach their conclusions. In clinical care, where decisions must be understandable, this creates tension, especially as patients seek meaning, not just outcomes. Bias and inequality further complicate the picture. AI systems reflect the data they are built on, which can carry social and cultural biases. At the same time, access to advanced tools remains uneven, raising questions about fairness in care quality. At its core, the rapid expansion of AI challenges the relational nature of clinical work. Therapy is not just about information, it is about presence, attunement, and human connection. Increasing reliance on AI risks filtering patient experiences through predefined systems rather than fully exploring them. So the question is not whether AI should continue to develop, but how far it should go without clearer ethical limits. Stopping it is unrealistic, but moving forward without reflection carries real consequences. For clinicians, this means maintaining an active, critical stance, using AI as support, not substitute, and staying transparent with patients. More broadly, it calls for a collective effort to ensure that innovation does not outpace responsibility. Ultimately, this is not just a technological shift, but a human one. The challenge is not only to understand what AI can do, but to decide, carefully and consciously, where we choose to draw the line.

Happy Brain Training was built to move beyond theory and into real, hands-on transformation. Over the past year, my work has grown across borders, formats, and communities, guided by one clear goal: helping therapists use AI in ways that truly make a difference. What I do is simple in idea, but powerful in practice. I train therapists to use AI effectively in therapy, focusing on practical, real-world applications that improve efficiency, creativity, and clinical outcomes. My training programs are structured as a journey. Therapists begin by building a strong foundation, understanding generative AI and tools like ChatGPT, then progress to creating personalized therapy materials, and eventually move into advanced case studies and full therapy planning supported by AI. Each step is designed to build confidence, autonomy, and clinical impact. Throughout this journey, we also explore prompt engineering, productivity, and the use of AI as a support tool for patients. The programs have evolved from basic interactions to structured materials, techniques, and domain-specific applications tailored to each specialty. Targeted solutions are also designed for professionals who want to improve efficiency and streamline their workflow. Beyond structured programs, I offer one-to-one advisory sessions, providing personalized guidance tailored to each therapist’s challenges. My approach stays grounded in real therapy needs, from communication and language to complex disorders such as aphasia, dysarthria, and dysphagia. Learning doesn’t stay in one place. My in-person trainings have reached multiple cities, from Luxembourg and Vienna to the Baltic States, Lyon, and Belgium, each adding to a growing international movement. At the same time, webinars and e-learning programs connect therapists across continents. I’ve worked with professionals across Europe, the Middle East, North America, Asia, and Australia. Today, more than 2,500 therapists worldwide have trained with me, each contributing to a global shift in how therapy is practiced. Across all these countries, one thing stands out: we share the same challenges. While our ways of working may differ, our core needs are deeply similar uniting us in a shared purpose. My workshops and live events are interactive, practical, and immediately applicable. To support this learning, I’ve developed a prompt book, a ready-to-use resource for creating personalized therapeutic materials across ages and clinical needs. What makes this different is not just what I offer, but how I think. Everything is therapist-centered, grounded in AI expertise, and built for real-world use. This is more than learning AI. It’s learning how to lead with it.

Happy Brain Training was built on a clear and ambitious vision: to rethink how therapy evolves in a world that is changing faster than ever. I founded it as a speech therapist and neuropsychologist with strong expertise in generative AI, bringing together clinical depth and technological innovation in a way that feels both grounded and forward-looking. What started as an idea has grown into something much more tangible. It is now a space where therapists come to explore, question, and reshape their practice. With my background in clinical work, research, and AI, the goal has never been to simply introduce new tools, but to help therapists use them with clarity, purpose, and clinical judgment. At its core, Happy Brain Training is driven by a simple yet powerful mission: to unlock the potential of both therapists and their patients through the thoughtful and responsible use of generative AI. This means equipping therapists with tools that genuinely support their work, helping them create more personalized and engaging materials, and making AI feel accessible rather than overwhelming. That is exactly why I created the Happy Brain Training newsletter: to keep therapists informed, inspired, and connected to the evolving landscape of both therapy and technology. Published weekly, it provides updates on new models, emerging possibilities, and key developments in the field. It also addresses essential topics such as data security, privacy, regulations, ethical considerations, and even the ecological impact of AI. In a field that is evolving rapidly, staying informed is no longer optional. The newsletter acts as a bridge between innovation and daily practice, offering insights, practical ideas, and reflections that therapists can directly apply in their work. Over time, this space has grown into a truly international and multidisciplinary community, with readers across Europe, the Middle East, Africa, North America, and beyond. It now connects professionals from Japan to Canada, reflecting how global this conversation has become. This diversity enriches the exchange of perspectives and strengthens a community grounded in real clinical practice. There is a strong commitment to bridging a gap many clinicians still feel. Technology can seem distant from the reality of therapy rooms, so the focus here is on making it practical, relevant, and aligned with real clinical needs, never abstract, but directly applicable. What defines this work is its focus on action. Therapists are not just learning about AI, but how to use it to save time, support clinical reasoning, and open new possibilities in their sessions, without compromising quality or depth. We explore areas such as prompt engineering, productivity, and how AI can support patients, always through concrete applications. For those who feel overwhelmed, especially by prompt engineering, I’ve developed structured solutions to make it simpler and more accessible. Today, I am proud to celebrate one year of the Happy Brain Training newsletter, one year of sharing ideas, learning together, and growing alongside a community of professionals who are curious, committed, and open to change. Throughout this journey, I’ve had the chance to meet inspiring therapists across different countries, and to meet you. These encounters continue to inspire me to adapt, evolve, and move forward. Even when the pace is intense, they remind me why this work matters. What started as a simple way to stay connected has become something far more meaningful: a space for reflection, inspiration, and progress.

A therapist looking at an image often focuses on meaning, what it shows, how it feels, and how it connects to a client’s story. Until recently, we could also assume that most images reflected something real. Today, with the rise of AI-generated images, that assumption is less certain. New tools, such as OpenAI’s image verification system, are beginning to help us check where images come from and whether they were created by AI. This tool allows users to upload an image and see if it contains hidden signals linked to AI systems, such as metadata or digital watermarks. These signals can suggest that an image was generated using tools like ChatGPT or related APIs. For clinicians, this is less about technology itself and more about developing a habit of asking: “Can I trust the origin of this image?” In practical terms, using the tool is simple and does not require technical knowledge. You begin by going to the verification webpage and uploading the image you want to check. It is helpful to crop the image so that only the main picture is included, especially if it comes from a screenshot. After uploading, the tool analyzes the file and looks for known signals linked to AI generation. It will then show whether these signals are present. If they are, the image was likely created using AI tools. If not, the result is less certain, and further caution is still needed. This process takes only a few moments and can easily become part of routine checking when working with unfamiliar images. In everyday clinical work, images are used in many ways, psychoeducation, assessment, and even therapeutic exercises. When we assume an image is real, we may respond to it differently than if we knew it was created by AI. This makes it important to pause and reflect, especially when an image plays a role in clinical understanding or emotional processing. From a thinking perspective, this shift asks us to slow down. We often rely on quick impressions when we see an image, especially if it looks familiar or realistic. However, AI-generated images can look highly convincing. Verification tools can support us in moving from quick assumptions to more careful, reflective thinking. There are also learning implications. Students and early-career therapists often use visual materials to support memory and understanding. If an image is later found to be artificial, it may create confusion or reduce confidence. Knowing that verification tools exist can help build a more balanced approach, one that combines curiosity with healthy doubt. In research settings, the issue becomes even more important. Fields that depend on images, such as medical or rehabilitation sciences, require accurate and trustworthy data. If AI-generated images are used without clear labeling, this can affect the quality of research. Verification tools can support better data practices, but they are only one part of the process. It is also important to understand the limits of these tools. Not all AI-generated images contain detectable signals, and not all real images are easy to verify. A tool may suggest that an image is AI-generated, but it cannot fully explain how or why it was created. This means we should use these tools as support, not as final proof. In clinical practice, ethical questions naturally arise. If a therapist uses an AI-generated image, should they tell the client? In most cases, transparency helps maintain trust. Even if the image is helpful, its origin still matters. Being open about this can strengthen the therapeutic relationship rather than weaken it. Responsibility remains with the clinician or researcher. Tools can assist, but they do not replace professional judgment. This includes thinking carefully about how images are used, checking their sources when needed, and being honest about their origins. It also means being aware that AI systems can carry biases or produce misleading content. As these technologies become more common, clinicians and researchers will need to adapt without feeling overwhelmed. The goal is not to become an AI expert, but to stay thoughtful and attentive. By combining simple verification tools with good clinical judgment, we can continue to use images in ways that are both effective and responsible. Looking ahead, these tools may become a normal part of practice, much like checking references or reviewing data sources. They remind us that in a digital world, seeing is not always the same as knowing. What remains essential is our ability to reflect, question, and make careful decisions in the service of those we work with. To explore the verification tool, visit: https://openai.com/research/verify/

A recent experimental study examined how tools like ChatGPT influence thinking during writing A recent experimental study by Kosmyna, N. et al. (2025), conducted at the MIT Media Lab, explores how tools like ChatGPT influence human thinking during writing tasks. The study, titled “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task,” examined how different levels of technological assistance shape cognitive engagement. The researchers recruited participants and divided them into three groups: one using an AI assistant (LLM group), one using a search engine, and one relying only on their own thinking (Brain-only group). Across three sessions, each group used the same method. In a fourth session, roles were reversed for some participants: those who had used AI were asked to work without it (LLM-to-Brain), while those who had worked independently were introduced to AI (Brain-to-LLM). The study combined EEG brain recordings, language analysis, essay scoring, and participant interviews to understand not just performance, but underlying cognitive processes. The results showed clear differences in how participants engaged cognitively. Brain activity, measured through neural connectivity, was strongest in the Brain-only group, moderate in the Search Engine group, and weakest in the LLM group. This suggests that as external support increased, internal cognitive engagement decreased. In parallel, language analysis revealed that essays produced with AI were more similar to each other, showing less variation in vocabulary and structure, while independently written essays were more diverse and distinct. Participants’ experiences also reflected these differences. Those in the LLM group reported a lower sense of ownership over their essays and had more difficulty recalling or quoting what they had just written. In contrast, the Brain-only group showed strong memory recall and a clear sense that the work belonged to them. Even when AI-assisted essays scored well, they often required minimal editing and remained close to default AI-generated responses, indicating lower levels of active processing. The fourth session provided some of the most important insights. Participants who moved from Brain-only to AI use (Brain-to-LLM) showed increased brain connectivity across multiple frequency bands, suggesting active integration of AI support with prior knowledge. They also performed well in terms of memory and structure. However, those who moved from AI use to independent writing (LLM-to-Brain) showed reduced neural engagement and did not return to the same level of cognitive activity as the original Brain-only group. Their writing also showed traces of AI-influenced vocabulary and structure, indicating a lingering effect of prior AI use. From a clinical perspective, these findings are highly relevant. Clinical reasoning depends on active engagement, organizing information, making connections, and reflecting on decisions. Writing is one of the main ways clinicians develop and refine this reasoning. If AI reduces the need for this effort, especially early in training, it may lead to what can be described as cognitive debt: a gradual weakening of the thinking processes that support clinical judgment. At the same time, the study suggests that AI can be beneficial when used after independent thinking has been established. The Brain-to-LLM group demonstrated that prior effortful engagement may allow clinicians or students to use AI in a more integrated and reflective way. This aligns with educational and clinical models where support tools are most effective when they build on an existing foundation rather than replace it. These findings also echo everyday clinical practice. Therapists often emphasize the importance of active participation and reflection in patients. Similarly, clinicians themselves rely on repeated, effortful thinking to build expertise. If AI tools begin to replace rather than support this process, there may be subtle but meaningful changes in how clinicians think, remember, and make decisions. The ethical implications are important. Clinicians remain responsible for their reasoning and documentation, even when AI is involved. The reported decrease in perceived ownership raises concerns about reduced critical engagement. If a clinician feels less connected to what they have written, they may be less likely to question it. There are also broader concerns about bias and accuracy, as AI-generated content may not always align with individual patient contexts or cultural considerations. For researchers and students, similar risks apply. High-quality writing is not only about clarity but about understanding. If AI assists in producing text without deep engagement, there is a risk of creating work that appears strong but lacks true comprehension. Maintaining intellectual integrity requires active involvement in the thinking process, not just the final output. Overall, this study offers an important early perspective on how AI tools like ChatGPT may shape cognition over time. For clinicians and therapists, it highlights the need for a balanced approach, one that uses AI as a support while preserving the effortful thinking that underpins clinical expertise. The goal is not to avoid these tools, but to use them in ways that strengthen, rather than replace, the cognitive processes at the heart of learning and practice.

How many times have we wished we could hold everything at once? All the reports, all the notes, all the small details that seem important but are spread across files and moments. What if the system we use could actually “remember” more, not just a few pages, but entire histories? Google’s Gemini is moving in that direction. With the ability to process up to 2 million tokens, it can take in what would roughly equal 1.5 million words. That could include therapy notes, assessments, videos, interviews, and research, all at once. For therapists, this touches something very familiar: our work is rarely based on one piece of information, but on how many pieces come together. Think of a child followed over several years. Different professionals, different reports, different perspectives. We often move back and forth between documents, trying to connect them into a meaningful picture. A system like this could help bring everything into one place and highlight patterns or gaps. But even then, it is not “understanding” the child. It is organizing information, not sensing meaning. Because in clinical work, more data does not equal deeper understanding. We listen, we observe, we feel the atmosphere in the room. We notice what is said, but also what is not said. AI can gather and structure, but it does not experience the relationship. It does not feel the hesitation, the resistance, or the shift in tone. Used carefully, this kind of tool can still support us. We might ask it to compare reports, summarize repeated goals, or point out inconsistencies. It can help us prepare and save time. But the interpretation, the decision, the responsibility, all of that stays with us as clinicians. There is also something tempting here. When answers come quickly, we might rely on them too easily. But clinical thinking takes time. It is built through questioning, reflecting, and sometimes sitting with uncertainty. If AI moves too fast, we risk skipping that process. And beyond practice, there are ethical questions. What are we sharing? Do we have consent? Are we protecting the people behind the data? Even if AI can handle more information, we are still responsible for how that information is used. In research, this expanded “memory” may help us review and organize large amounts of material. But again, it does not replace careful reasoning or methodological rigor. It can support the process, not guarantee its quality. So maybe the question is not only whether AI can remember more. It is how we use that memory. Does it help us think more clearly, or does it think for us? In the end, what matters is that the person in front of us does not become just another piece of data, but remains at the center of our attention.

A therapist sits with a young adult during a counseling session. The client suddenly becomes quiet, then says they feel overwhelmed but cannot explain why. There is a long pause. The therapist notices the tension, gently acknowledges it, and gives the client space to speak without pressure. Moments like this are at the heart of clinical training. They remind us that learning to be a clinician is not only about knowledge, but also about how we respond to real human emotions. AI is already part of our clinical learning, but it is integrated in different ways across systems. In many universities and training programs, it is now included in both formal education and continuous professional development. Today, there are many tools that clinicians and students can use in their ongoing learning, from conversation simulators to note-writing assistants and case review platforms. This is not something coming in the future, it is already shaping how we learn. These tools can be helpful. They allow students to practice conversations, receive quick feedback, and repeat exercises as often as needed. For learners who benefit from repetition or flexible pacing, this can make education more accessible and personalized. However, learning with AI is not the same as learning with real people. AI can simulate a conversation, but it does not truly feel emotions or respond with genuine presence. In real clinical situations, patients may be silent, overwhelmed, resistant, or unclear. These moments require more than technique. They require attunement. The same applies to education itself. A trainer or supervisor does more than provide information. They sense when a student is struggling, disengaged, or not fully understanding. They adjust their teaching in real time, sometimes slowing down, sometimes challenging, sometimes simply being present. AI cannot sense when someone is emotionally unavailable, anxious, or disconnected. This human sensitivity is a key element of clinical training. For example, a student practicing a difficult conversation with AI might learn useful phrasing. But in a live supervision session, a trainer might notice hesitation in the student’s voice, or discomfort in how they approach a sensitive topic. The trainer can pause, ask reflective questions, and help the student explore what is happening internally. This kind of learning goes beyond performance, it builds clinical awareness. There is also a concern that students may rely too much on AI. If they consistently turn to it for answers, they may not fully develop their own clinical thinking. Good clinicians do more than respond, they question, reflect, and tolerate uncertainty. AI can generate quick answers, but it does not replace the deeper thinking that develops through experience and guided reflection. At the same time, AI can still support learning when used thoughtfully. Students can practice before real encounters, compare AI-generated responses with theoretical models, and discuss them with supervisors. In this way, AI becomes a tool that supports critical thinking rather than replacing it. It is also important to recognize that AI is not neutral. It is trained on large datasets that may include biases. As a result, it may not always reflect diverse cultural, linguistic, or personal experiences accurately. In clinical work, where understanding each individual context is essential, this is a significant limitation. Responsibility remains with the clinician. Even when AI is used, students and professionals must be able to justify their decisions and understand the reasoning behind them. AI should assist learning, not replace professional judgment. For educators, this moment is reshaping how we design and deliver training. Curricula are being adapted, not only to include AI tools, but also to teach how to use them critically and ethically. This includes setting boundaries, protecting confidentiality, and helping students understand both the strengths and the limits of these technologies. In the end, AI may support clinical education, but it cannot replace the human connection at its core. In both therapy and training, what matters most is presence, empathy, and the ability to sense what is happening in another person. These are not technical skills alone, they are relational capacities that develop through real human experience. As education continues to evolve, the challenge is not whether to use AI, but how to integrate it without losing what makes clinical work deeply human.