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What if artificial intelligence could read your thoughts — not to spy on you, but to heal your brain? It may sound like science fiction, yet emerging research in brain-computer interfaces (BCIs) powered by AI is rapidly reshaping possibilities for people with paralysis, speech loss, or severe trauma. For professionals working in therapy, special education, and neuroscience, this isn’t just a technologic novelty — it signals a fundamental change in how we might approach intervention, autonomy, and recovery. Decoding the Brain: How BCIs and AI Work At their core, BCIs translate neural activity into digital commands. Historically, these devices captured signals (via EEG, implanted electrodes or minimally-invasive sensors) that corresponded with a user’s intention — like moving a cursor or selecting a letter. The leap now comes from AI. Sophisticated machine-learning and deep-neural-network models can decode nuanced brain patterns, adjust in real time, and even predict states such as mood shifts or seizure events. For example, a man with partial paralysis used a non-invasive BCI-AI hybrid system to control a robotic arm and complete screen-based tasks four times more effectively than with the device alone. This is not automation—it’s collaboration. The AI decodes the signal, but the human leads the intention. As a practitioner, it means thinking of BCIs not as “devices we deliver to clients” but as extensions of the therapeutic interface — neural input, meaningful output, and a feedback loop that connects brain to device, device to action, and action to meaning. Breakthroughs in 2024-25: From Paralysis to Restoration Recent stories illustrate the pace of change. A 2025 article reported how a man with paralysis controlled a robotic arm via thought alone, thanks to an AI-enhanced BCI. Another major milestone: the company Medtronic’s BrainSense Adaptive Deep Brain Stimulation system — a closed-loop BCI-informed therapy for Parkinson’s — was named one of TIME’s “Best Inventions of 2025” after more than 1,000 patients received the treatment. These examples aren’t just about technology; they’re about therapy delivered at the brain level. Speech therapists, neurorehabilitation professionals, and educators who support motor recovery might soon interact with clients whose therapy includes neural-interface elements: devices that decode intention, guide movement, or translate thought into speech. For many clients, the promise of regained autonomy—typing messages, controlling assistive devices, or even walking—becomes real. Ethical and Practical Considerations for Clinical Practice Despite the excitement, the shift from novelty to mainstream carries enormous responsibility. Data from neural interfaces is intensely personal: thinking, intending, perhaps even emoting. Decoding inner speech raises privacy questions. One recent implant study could interpret a user’s “inner monologue” with up to 74% accuracy. As clinicians or educators, we must ask: how do we preserve dignity, agency, and consent when the very channel of thought becomes part of therapy? Accessibility is another concern. These technologies are highly specialist, invasive in certain cases, and expensive. Without careful integration, we risk creating a two-tier system where only some clients benefit. The research commentary on BCIs in 2025 notes that despite dramatic advances, many devices still require frequent recalibration and remain confined to labs. From a practice standpoint, we’re entering the era of hybrid therapy—one where neural devices, AI analytics, and human relational expertise converge. Our role expands: we’re interpreters of neural data, ethical stewards of device use, and guides of clients whose therapy includes machine-mediated experience. The therapeutic alliance doesn’t disappear—it deepens. For therapists, special educators and researchers, the rise of AI-enabled BCIs signals three shifts: In effect, the future of rehabilitation and intervention may involve thought, device, and context in tandem—with the human at the centre, but AI and BCIs as powerful allies. While fully mainstream neurotechnology may still be a few years away, the trajectory is clear. We might soon design therapy plans that include neural intention measurement, adaptive devices that respond to brain-states, and home-based neural support systems. For now, staying informed, curious and ethically grounded is vital. When the channel of change is the brain itself, our practice must become correspondingly profound. Suggested Reading: Live Science (2025): The new implant that can decode inner speech

Artificial intelligence has woven itself into the fabric of modern healthcare. From diagnostic imaging to speech and language therapy, AI now touches nearly every aspect of practice. But as the technology grows more powerful, so does the need for clear ethical boundaries. Recent international reports and consensus statements show that 2025 may be remembered as the year the world finally agreed on what “ethical AI in healthcare” must look like. Across countries and disciplines, regulators and researchers are converging on similar principles: transparency, accountability, fairness, and above all, human oversight. The International Medical Committee of Research (ICMR) recently published its Ethical Guidelines for the Application of Artificial Intelligence in Biomedical Research and Healthcare, a comprehensive document outlining the responsibilities of professionals who use AI in health-related contexts. These guidelines call for explicit consent procedures, clear communication about the use of AI, and strong governance around data protection. At the same time, the World Medical Association (WMA) released its summary document on the Ethical, Legal, and Regulatory Aspects of AI in Healthcare — a blueprint that urges health and therapy professionals to safeguard autonomy and to ensure that the “human-in-the-loop” principle remains non-negotiable. This echoes the FUTURE-AI framework, published in The BMJ, which identifies seven pillars for trustworthy AI: fairness, transparency, human-centeredness, robustness, explainability, accountability, and sustainability. For therapists, educators, and clinical researchers, these frameworks are more than abstract policies — they are practical guardrails. As AI becomes more embedded in clinical systems, therapists may rely on algorithmic suggestions to guide interventions, predict outcomes, or tailor materials. Yet ethical AI demands that professionals remain critical thinkers, not passive users. A language model may suggest a therapy strategy or generate a progress note, but it cannot capture the emotional subtleties, ethical dilemmas, or contextual nuances that define human care. The implications for practice are profound. When integrating AI tools — whether a language analysis app, an adaptive learning system, or a mental health chatbot — professionals must consider how these tools handle data, what assumptions shape their algorithms, and whether clients fully understand the role of AI in their care. Informed consent becomes a living process, not a one-time checkbox. Ethical AI also requires vigilance against bias. Many datasets that train AI systems underrepresent neurodiverse populations, minority language users, or people from low-resource contexts. When bias is embedded in data, it is embedded in outcomes — potentially amplifying inequities rather than reducing them. The current international guidelines call on practitioners to advocate for inclusivity in AI design, urging collaboration between clinicians, technologists, and patient communities. Ultimately, the question is not whether AI should be part of healthcare — it already is — but how we ensure it serves humanity rather than undermines it. The future of therapy and rehabilitation will likely be hybrid: human judgment empowered by machine intelligence. But the ethical compass must always point toward empathy, consent, and equity. Professionals who engage early with these ethical frameworks position themselves as leaders in responsible innovation. Reading and reflecting on them isn’t just regulatory compliance — it’s professional integrity in action. Further Reading:

The pace of artificial intelligence advancement has been staggering, but OpenAI’s latest announcement marks a turning point that could redefine scientific discovery itself. By 2028, the company aims to develop fully autonomous AI researchers—systems capable of independently conceiving, executing, and refining entire scientific studies without human intervention. This isn’t merely an evolution of existing tools; it represents a fundamental shift in how knowledge is generated, one that promises to accelerate breakthroughs in fields ranging from neuroscience to education while forcing us to confront profound questions about the nature of research, authorship, and human expertise. The implications for scientists, clinicians, and educators are immense. Imagine an AI that doesn’t just assist with data analysis but actively designs experiments based on gaps in current literature, adjusts methodologies in real-time as new evidence emerges, and publishes findings that push entire fields forward. For researchers drowning in the ever-expanding sea of academic papers, this could mean identifying meaningful patterns in days rather than years. Therapists might gain access to personalized intervention strategies derived from millions of case studies, while special educators could receive AI-generated instructional approaches tailored to individual learning profiles. Yet with these possibilities comes an urgent need to consider: How do we ensure these systems serve human needs rather than commercial interests? What happens when AI makes discoveries we can’t fully explain? And how do we maintain ethical standards when the researcher is an algorithm? OpenAI’s roadmap to this future unfolds in deliberate stages, with the first major milestone arriving in 2026. By then, the company expects to deploy AI systems functioning as research interns—tools sophisticated enough to synthesize existing literature, propose testable hypotheses, and even draft experimental protocols with minimal human oversight. This intermediate step is crucial, as it allows the scientific community to adapt to AI collaboration before full autonomy becomes reality. The transition will require more than just technological advancement; it demands a cultural shift in how we view research. Peer review processes may need to evolve to accommodate AI-generated studies. Funding agencies might prioritize projects that leverage these tools effectively. And perhaps most importantly, researchers themselves will need to develop new skills—not just in using AI, but in critically evaluating its outputs, understanding its limitations, and ensuring its applications align with ethical principles. The potential benefits are undeniable. In psychology, an autonomous AI researcher could analyze decades of therapy outcome data to identify which interventions work best for specific demographics, leading to more effective treatments. In special education, it might design and test personalized learning strategies for students with unique cognitive profiles, offering educators evidence-based approaches they previously lacked. Even in fundamental science, AI could accelerate the pace of discovery by running thousands of virtual experiments in the time it takes a human lab to complete one. Yet these advantages come with significant risks. Without careful oversight, AI systems could perpetuate biases present in existing data, overlook nuanced human factors that don’t fit neat statistical patterns, or even generate findings that appear valid but lack real-world applicability. The challenge, then, isn’t just building these systems—but building them responsibly. As we stand on the brink of this new era, the scientific community faces a critical choice. We can approach this transition reactively, waiting to address problems as they arise, or we can take a proactive stance, establishing guidelines, ethical frameworks, and validation processes now. The latter approach requires collaboration across disciplines—computer scientists working with ethicists, clinicians partnering with AI developers, and educators helping shape how these tools integrate into real-world practice. It also demands public engagement, as the implications extend far beyond academia. When AI begins making discoveries that affect healthcare, education, and policy, who decides how those findings are used? The answers to these questions will determine whether this technological leap empowers humanity or leaves us struggling to keep up with machines that outpace our understanding. Ultimately, the rise of autonomous AI researchers isn’t just about faster science—it’s about redefining what research means in an age where human and machine intelligence intertwine. The goal shouldn’t be to replace human researchers, but to create a synergy where AI handles the heavy lifting of data and computation while humans bring creativity, ethical judgment, and real-world insight. If we navigate this transition thoughtfully, we could unlock a new golden age of discovery—one where the most pressing questions in psychology, education, and medicine find answers at an unprecedented pace. But if we fail to prepare, we risk creating a system where the pursuit of knowledge outpaces our ability to use it wisely. The clock is ticking; 2028 is closer than it seems, and the time to shape this future is now.

The scientific community stands at the threshold of a transformative shift. By September 2026, OpenAI plans to introduce AI systems capable of functioning as research interns—tools that go beyond simple data analysis to actively assist in literature synthesis, hypothesis generation, and experimental design. This development marks more than just a technological upgrade; it represents the first step toward a future where artificial intelligence becomes an integral partner in the research process. For psychologists, neuroscientists, and educators, this shift could mean faster insights, more efficient studies, and unprecedented opportunities for discovery—but it also demands a fundamental rethinking of how we conduct, validate, and apply scientific knowledge. The concept of an AI research intern might sound abstract, but its practical applications are both immediate and profound. Consider a clinical psychologist investigating new therapies for anxiety disorders. Today, the process begins with months of literature review, sifting through hundreds of studies to identify gaps and opportunities. An AI intern could accomplish this in hours, not only summarizing existing research but highlighting unexplored connections—perhaps noticing that certain demographic groups respond differently to mindfulness-based interventions, or that combination therapies show promise in understudied populations. From there, the AI might propose specific hypotheses (“Would adding a social skills component improve outcomes for adolescents with comorbid anxiety and autism?”) and even draft preliminary study designs, complete with sample size calculations and methodological considerations. For researchers accustomed to the slow, labor-intensive nature of academic work, this level of support could dramatically accelerate the pace of discovery, allowing them to focus on the creative and interpretive aspects of their work rather than the mechanical. Yet the introduction of AI interns isn’t just about efficiency—it’s about changing the very nature of research collaboration. Traditional scientific work relies on human intuition, serendipitous connections, and deep domain expertise, qualities that AI currently lacks. The most effective use of these tools will likely emerge from a hybrid approach, where AI handles the repetitive and data-intensive tasks while human researchers provide contextual understanding, ethical oversight, and creative problem-solving. For instance, an AI might identify a statistical correlation between early childhood screen time and later attention difficulties, but it would take a developmental psychologist to interpret whether this reflects causation, confounding variables, or cultural biases in the data. Similarly, in special education research, an AI could analyze vast datasets on reading interventions, but an experienced educator would need to determine how those findings apply to individual students with complex, multifaceted needs. The integration of AI interns also raises critical ethical and practical questions that the scientific community must address proactively. One of the most pressing concerns is validation. How do we ensure that AI-generated hypotheses are rigorous and reproducible rather than artifacts of flawed data or algorithmic bias? Peer review processes may need to adapt, incorporating AI literacy as a standard requirement for evaluators. Funding agencies might develop new criteria for AI-assisted research, ensuring that proposals leverage these tools responsibly. And journals will face the challenge of authorship and transparency—should AI systems be credited as contributors? If so, how do we distinguish between human-led and AI-driven insights? Another significant consideration is equity. While AI interns could democratize research by giving smaller labs and underfunded institutions access to powerful analytical tools, they could also exacerbate existing disparities if only well-resourced teams can afford the most advanced systems. OpenAI and similar organizations have a responsibility to prioritize accessibility, perhaps through open-source models or subsidized access for academic researchers. Similarly, there’s a risk that AI systems trained primarily on data from Western, educated, industrialized populations could overlook or misrepresent other groups, reinforcing biases in scientific literature. Addressing this requires diverse training datasets and inclusive development teams that understand the limitations of current AI models. Perhaps the most profound impact of AI research interns will be on the next generation of scientists. Graduate students and early-career researchers may find themselves in a radically different training environment, where traditional skills like manual literature reviews become less essential, while AI literacy, prompt engineering, and critical evaluation of machine-generated insights grow in importance. Academic programs will need to evolve, teaching students not just how to use AI tools, but how to think alongside them—when to trust their outputs, when to question them, and how to integrate them into a human-centered research process. This shift could also reshape mentorship, with senior researchers guiding juniors not just in experimental design, but in navigating the ethical and practical challenges of AI collaboration. As we approach the 2026 milestone, the scientific community would be wise to prepare rather than react. Researchers can begin by experimenting with current AI tools, such as literature synthesis platforms like Elicit or data analysis assistants like IBM Watson, to understand their strengths and limitations. Institutions should develop guidelines for AI-assisted research, addressing questions of authorship, validation, and bias mitigation. And perhaps most importantly, we must foster interdisciplinary dialogue, bringing together computer scientists, ethicists, domain experts, and policymakers to ensure that these tools are designed and deployed responsibly. The arrival of AI research interns isn’t just a technological advancement—it’s a cultural shift in how we pursue knowledge. If we embrace this change thoughtfully, it could liberate researchers from tedious tasks, accelerate meaningful discoveries, and open new frontiers in science. But if we fail to engage with its challenges, we risk creating a system where the speed of research outpaces its quality, where algorithmic biases go unchecked, and where human expertise is undervalued. The choice isn’t between rejecting AI or accepting it uncritically—it’s about shaping its role in a way that enhances, rather than diminishes, the pursuit of truth. The countdown to 2026 has begun; the time to prepare is now.

Every so often a research paper lands that feels less like a forecast and more like a field guide. The OECD’s new working paper on AI for students with special education needs is exactly that—practical, grounded, and refreshingly clear about what helps right now. If you care about brain‑friendly learning, this is good news: we’re moving beyond shiny demos into tools that lower barriers in everyday classrooms, therapy rooms, and homes. The paper’s central idea is simple enough to fit on a sticky note: inclusion first, AI second. Instead of asking “Where can we push AI?” the authors ask “Where do learners get stuck—and how can AI help remove that barrier?” That’s the spirit of Universal Design for Learning (UDL): give learners multiple ways to engage with content, multiple ways to understand it, and multiple ways to show what they know. AI becomes the backstage crew, not the headliner—preparing captions, adapting tasks, translating atypical speech, and nudging practice toward the just‑right challenge level. What does this look like in real life? Picture a student whose handwriting slows down everything. Traditional practice can feel like running in sand—lots of effort, little forward motion. Newer, tablet‑based coaches analyze the micro‑skills we rarely see with the naked eye: spacing, pressure, pen lifts, letter formation. Instead of a generic worksheet, the learner gets bite‑sized, game‑like tasks that target the exact stumbling blocks—then cycles back into real classroom writing. Teachers get clearer signals too, so support moves from hunches to evidence. Now think about dyslexia. Screening has always walked a tightrope: catch risk early without labeling too fast. The paper highlights tools that combine linguistics with machine learning to spot patterns and then deliver thousands of tiny, personalized exercises. The win isn’t just early identification; it’s keeping motivation intact. Short, achievable practice turns improvement into a string of small wins, which is catnip for the brain’s reward system. Some of the most heartening progress shows up in communication. If you’ve ever watched a child with atypical speech be understood—really understood—by a device that has learned their unique patterns, you know it feels like a door opening. Fine‑tuned models now translate highly individual speech into clear text or voice in real time. Families tell researchers that daily life gets lighter: ordering in a café, answering a classmate, telling a joke at the dinner table. The paper is careful not to overclaim, but the early signals are powerful. Social communication for autistic learners is getting smarter, too. On‑screen or embodied agents can practice turn‑taking, joint attention, and emotion reading in a space that’s structured and safe. Educators can tweak prompts and difficulty from a dashboard, so sessions flex with energy levels and goals. The magic here isn’t that a robot “teaches” better than a human; it’s that practice becomes repeatable, low‑stakes, and tuned to the moment—then transferred back to real interactions. Not all wins are flashy. Converting static PDFs into accessible, multimodal textbooks sounds mundane until you watch it unlock a unit for an entire class. Text‑to‑speech, captions, alt‑text, adjustable typography, and cleaner layouts benefit students with specific needs—and quietly help everyone else. This is UDL’s ripple effect: when we design for variability, the floor rises for all learners. Under the hood, personalization is getting sharper. Instead of treating “math” or “reading” as monoliths, systems map skills like networks. If multiplication is shaky because repeated addition never solidified, the system notices and steps back to build the missing bridge. Learners feel less frustration because the work finally matches their readiness. Teachers feel less guesswork because the analytics point to actionable scaffolds, not vague “struggling” labels. So where’s the catch? The paper is clear: many tools still need larger, longer, and more diverse trials. Evidence is growing, not finished. We should celebrate promising results—and still measure transfer to real tasks, not just in‑app scores. And we can’t ignore the guardrails. Special education involves some of the most sensitive data there is: voice, video, eye‑gaze, biometrics. Privacy can’t be an afterthought. Favor on‑device processing where possible, collect only what you need, keep it for as short a time as you can, and use consent language that families actually understand. Bias is another live wire. If speech models don’t learn from a wide range of accents, ages, and disability profiles, they’ll miss the very learners who need them most. And yes, there’s an environmental bill for heavy AI. Right‑sized models, greener compute, and sensible usage policies belong in the conversation. What should teachers and therapists do with all this tomorrow morning? Start with the barrier, not the tool. Identify the friction—copying from the board, decoding dense text, being understood—and pilot something that targets that friction for eight to twelve weeks. Keep it humble and measurable: a pre/post on intelligibility, words per minute, error patterns, or on‑task time tells a better story than “students liked it.” Treat multimodality as default, not accommodation: captions on, text‑to‑speech available, alternative response modes open. And capture whether gains show up in real classwork. If progress lives only inside an app, it’s not the progress you want. For school leaders, the paper reads like a procurement sanity check. Ask vendors for research summaries you can actually read, not just glossy claims. Demand accessibility as a feature, not an add‑on—screen reader support, captions, switch access. Check interoperability so your data doesn’t get stuck. Bake privacy into contracts: where data lives, how long it stays, how deletion works. Push for localization and equity—bilingual interfaces, dialect sensitivity, culturally relevant content—because a tool that isn’t understood won’t be used. And if a vendor can talk credibly about energy and efficiency, that’s a green flag. Bottom line: AI isn’t replacing the art of teaching or therapy. It’s removing friction so strengths surface sooner. It’s turning opaque struggles into visible, coachable micro‑skills. It’s helping voices be heard and ideas be expressed. If we keep learners and families at the center, measure what matters, and mind the guardrails, this isn’t hype—it’s momentum we can build on. Read the full OECD paper: https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/09/leveraging-artificial-intelligence-to-support-students-with-special-education-needs_ebc80fc8/1e3dffa9-en.pdf

ChatGPT Atlas is the kind of upgrade you only appreciate after a single workday with it. Instead of juggling a separate ChatGPT tab, a dozen research pages, and that half‑written email, Atlas pulls the assistant into the browser itself so you can read, ask, draft, and even delegate steps without breaking focus. OpenAI introduced it on October 21, 2025, as a macOS browser available worldwide for Free, Plus, Pro, and Go users, with Agent mode in preview for Plus, Pro, and Business and admin‑enabled options for Enterprise and Edu. Windows, iOS, and Android are on the way, but the story starts here: a browser that understands the page you’re on and can help you act on it. If you’ve ever copied a paragraph into ChatGPT just to get a plainer explanation, you’ll like the Ask ChatGPT sidebar. It rides alongside whatever you’re viewing, so you can highlight a passage and ask for an explanation, a summary for families, or a quick draft to paste into your notes—without leaving the page. You can type or talk, and the conversation stays anchored to the page in view. For writing, Atlas adds an “Edit with ChatGPT” cursor directly in web text fields: select text, invoke the cursor, and request a revision or dictate new content in place. It feels less like consulting a tool and more like having a helpful colleague in the margin. Where things get interesting is Agent mode. When you switch it on, ChatGPT can take actions in your current browsing session: open tabs, navigate, click, and carry out multi‑step flows you describe. Planning a workshop? Ask it to gather venue options that match your accessibility checklist, compare prices and policies, and draft a short email to the top two. Wrangling admin tasks? Let it pre‑fill routine forms and stop for your review before submission. You set the guardrails—from preferred sources to required approval checkpoints—and you can even run the agent “logged out” to keep it away from signed‑in sites unless you explicitly allow access. It’s a natural hand‑off: you start the task, the agent continues, and it reports back in the panel as it goes. Because this is a browser, privacy and control matter more than features. Atlas ships with training opt‑outs by default: OpenAI does not use what you browse to train models unless you turn on “Include web browsing” in Data controls. Browser memories—the feature that lets ChatGPT remember high‑level facts and preferences from your recent pages—are strictly optional, viewable in Settings, and deletable; deleting your browsing history also removes associated browser memories. Business and Enterprise content is excluded from training, and admins can decide whether Browser memories are available at all. If you want quality signals to improve browsing and search but not training, Atlas separates that diagnostic toggle from the model‑training switch so you can keep one off and the other on. Setup is quick. Download the macOS app, sign in with your ChatGPT account, and import bookmarks, passwords, and history from Chrome so you don’t start from zero. You can make Atlas your default in one click, and there’s a small, time‑limited rate‑limit boost for new default‑browser users to smooth the transition. It runs on Apple silicon Macs with macOS 12 Monterey or later, which covers most modern school or clinic machines. For a brain‑friendly practice—whether you’re supporting learners, coaching adults, or coordinating therapy—Atlas changes the cadence of your day. Research no longer requires the swivel‑chair routine: open a guideline or policy page, ask the sidebar to extract the eligibility details or accommodations, and keep reading as it compiles what matters. When policies conflict, have it surface the differences and the exact language to discuss with your team. Drafting becomes lighter, too. Need a parent update in Arabic and English? Keep your school page open, ask Atlas to produce a two‑column explainer grounded in that page, and paste it into your newsletter or WhatsApp note. Because the chat sits beside the source, you’re less likely to lose context—and more likely to keep citations tidy. The benefits are practical in Qatar and across MENA, where bilingual communication and time‑to‑action often make or break a plan. Atlas respects your existing logins and runs locally on macOS, which means it adapts to your regional sites and Arabic/English workflows without new portals. Start small: use the sidebar for comprehension scaffolds during lessons, quick plain‑language summaries for families, or bilingual glossaries on the fly. When your team is comfortable, try Agent mode for repeatable tasks like collecting venue policies, drafting vendor comparisons, or preparing term‑start checklists—while keeping the agent in logged‑out mode if you don’t want it near signed‑in records. The point isn’t to automate judgment; it’s to offload the clicks so you can spend attention where it counts. Safety is a shared responsibility, and OpenAI is frank that agentic browsing carries risk. Atlas limits what the agent can do—it can’t run code in the browser, install extensions, or reach into your file system—and it pauses on certain sensitive sites. But the company also warns about prompt‑injection attacks hidden in webpages that could try to steer an agent off course. The practical takeaway for teams is simple: monitor agent runs, prefer logged‑out mode for anything sensitive, and use explicit approval checkpoints. As with any new tool, start on low‑stakes workflows, measure outcomes like minutes saved or error rates, and scale intentionally. Under the hood, Atlas also modernizes search and results. A new‑tab experience blends a chat answer with tabs for links, images, videos, and news, so you can go source‑first when you want to validate or dive deeper. That’s useful for educators and clinicians who need traceable sources for reports: ask for a synthesis, then flip to the links view to gather citations you can verify. And because it’s still a browser, your usual web apps, calendars, and SIS/EMR portals stay right where they are—Atlas just gives you a knowledgeable helper at elbow height. If you publish a newsletter like Happy Brain Training, Atlas earns its keep quickly.

Artificial intelligence is now woven deeply into adolescents’ digital lives, and recent developments at Meta Platforms illustrate how this is prompting both excitement and concern. In October 2025, Meta announced new parental control features designed to address how teenagers interact with AI chatbots on Instagram, Messenger and Meta’s AI platforms. These new settings will allow parents to disable one-on-one chats with AI characters, block specific AI characters entirely and gain insights into the broader topics their teens are discussing with AI. For therapists and special educators, this kind of shift has direct relevance. Teens are using AI chatbots not just as novelty apps, but as everyday companions, confidants and conversational partners. Some research suggests more than 70 % of teens have used AI companions and over half engage regularly. That means when we talk about adolescent social and emotional support, the digital dimension is increasingly part of the context. Why does this matter? First, if a teen is forming a pattern of working through challenges, worries or social-communication via an AI chatbot, it raises important questions: what kind of messages are being reinforced? Are these increasing self-reliance, reducing peer or adult interaction, or reinforcing unhealthy patterns of isolation or dependency? For example, if a student with anxiety prefers sessions with a chatbot over adult-led discussion, we need to ask whether that substitution is helpful, neutral, or potentially problematic. Second, educators and therapists are well positioned to intervene proactively. Instead of simply assuming family or school IT will handle AI safety, you can build routine questions and reflections into your sessions: “Do you talk with a chatbot or AI assistant? What do you talk about? How does that compare to talking to friends or me?” These questions open discussion about digital emotional habits and help students articulate their experiences with AI rather than silently consume them. Third, this is also a family and systems issue. When Meta allows parents to monitor and set boundaries around teen-AI interactions, it offers a starting point for family education around digital wellbeing. For therapists, hosting a brief parent-session or sending a handout about AI chat habits, emotional regulation and healthy interaction might make sense. In special education settings, this becomes part of a broader plan: how does student digital use intersect with communication goals, social skills, and transition to adult life? From a school or clinic perspective, planning might include coordination with the IT team, reviewing how chatbots or AI companions are used in the building, and considering whether certain students need scaffolded access or supervision. For example, students with social-communication challenges might use AI bots unsupervised, which introduces risk if the bot offers responses that are unhelpful, reinforcing or misleading. It’s also important to stay alert to ethics and developmental appropriateness. Meta’s update comes after criticism that some of its bots engaged in romantic or inappropriate exchanges with minors. These new features—while helpful—are a minimum response, not a full solution. Vulnerable teens, especially those with special needs, may be at greater risk of substituting bot-based interaction for supportive adult engagement. What can you do right now? Consider including a digital-AI question in your intake or IEP forms. Run a short conversation with families about chatbot use in the home. Offer resources or a brief session for parents and guardians about setting boundaries and promoting emotional safety in AI use. Take a look at students whose digital habits changed dramatically (for example, more chatbot use, fewer peer interactions) and reflect on whether this coincides with changes in mood/engagement. Dialogue with your multidisciplinary team: how does AI interaction fit into the student’s social-communication plan, mental health goals or peer-interaction targets? Suggested Reading:

The promise of artificial intelligence in education has grown rapidly, and a new working paper from the Organisation for Economic Co‑operation and Development (OECD) titled “Leveraging Artificial Intelligence to Support Students with Special Education Needs” provides a thoughtful overview of how AI can support learners—but with major caveats. At its core, the report argues that AI tools which adapt instruction, generate accessible content and provide support tailored to individual learners have real potential in special education, therapy and inclusive classrooms. For example, an AI system might generate simplified reading passages for students with dyslexia, create visual supports or scaffolds for students with language delays, or adapt pace and format for students with attention or processing challenges. For therapists and special educators, this means opportunities to innovate. Instead of manually creating multiple versions of a lesson or communication script, generative AI can support you by producing varied, adapted material quickly. A speech therapist working with bilingual children might use an AI tool to produce scaffolded materials across languages; an occupational therapist might generate tactile-task instructions or interactive supports that match a student’s profile. However, the OECD report also emphasises that equity, access and human-centred design must accompany these possibilities. AI tools often rely on data trained on typical learners, not those with rare communication profiles or disabilities. Bias, representation gaps and access inequities (such as device availability or internet access) are real obstacles. In practice, you might pilot an AI-driven tool in one classroom or one caseload, with clear parameters: what are the outcomes? How did students engage? Did the tool genuinely reduce the manual load? Did it increase learner autonomy or scaffold more meaningful interaction? Collecting student and family feedback, documenting changes in engagement, and reflecting on how the tool leveraged or altered human support is key. Inclusive AI also demands that you remain the designer of the environment, not the tool. For example, when generating supports for a student with autism and a co-occurring language disorder, you might ask: did the AI produce appropriate language level? Did it respect cultural/language context? Do hardware/internet constraints limit access at home or in school? These reflections help avoid inadvertently widening the gap for students who may have fewer resources. From a professional development perspective, this is also a moment to embed AI literacy into your practice. As learners engage with AI tools, ask how their interaction changes: Are they more independent? Did scaffolded tools reduce frustration? Are they using supports in ways you did not anticipate? Part of your emerging role may be to monitor and guide how students interact with AI as part of the learning ecology. If you’re exploring inclusive AI, consider creating a small pilot plan: select one AI-tool, one student group, one outcome metric (e.g., reading comprehension, self-regulation, communication initiation). Run a baseline, implement the tool, reflect weekly, and refine prompts or scaffolded supports. Share findings with colleagues—these insights are vital for building sustainable AI-assisted practice. Suggested Reading:

One of the most promising frontiers in AI and special education is the blending of robotics and language models to support social communication. A recent project, Echo-Teddy, is pushing into that space — and it offers lessons, possibilities, and cautions for therapists, educators, and clinicians working with neurodiverse populations. What Is Echo-Teddy? Echo-Teddy is a prototype social robot powered by a large language model (LLM), designed specifically to support students with autism spectrum disorder (ASD). The developers built it to provide adaptive, age-appropriate conversational interaction, combined with simple motor or gesture capabilities. Unlike chatbots tied to screens, Echo-Teddy occupies physical space, allowing learners to engage with it as a social companion in real time. The system is built on a modest robotics platform (think Raspberry Pi and basic actuators) and integrates speech, gestures, and conversational prompts in its early form. In the initial phase, designers worked with expert feedback and developer reflections to refine how the robot interacts: customizing dialogue, adapting responses, and adjusting prompts to align with learner needs. They prioritized ethical design and age-appropriate interactions, emphasizing that the robot must not overstep or replace human relational connection. Why Echo-Teddy Matters for Practitioners Echo-Teddy sits at the intersection of three trends many in your field are watching: Key Considerations & Challenges No innovation is without trade-offs. When considering Echo-Teddy’s relevance or future deployment, keep these in mind: What You Can Do Today (Pilot Ideas) Looking Toward the Future Echo-Teddy is an early model of what the future may hold: embodied AI companions in classrooms, therapy rooms, and home settings, offering low-stakes interaction, scaffolding, and rehearsal. As hardware becomes more affordable and language models become more capable, these robots may become part of an ecosystem: robots, human therapists, software tools, and digital supports working in tandem. For your audience, Echo-Teddy is a reminder: the future of social-communication support is not just virtual — it’s embodied. It challenges us to think not only what AI can do, but how to integrate technology into human-centered care. When thoughtfully deployed, these innovations can expand our reach, reinforce learning, and provide clients with more opportunities to practice, experiment, and grow.

As artificial intelligence continues to permeate various sectors, its application in healthcare has garnered significant attention. A recent study published in Frontiers in Digital Health assessed the accuracy of several AI chatbots—ChatGPT-3.5, ChatGPT-4o, Microsoft Copilot, Google Gemini, Claude, and Perplexity—in providing evidence-based health advice, specifically focusing on lumbosacral radicular pain. Study Overview The study involved posing nine clinical questions related to lumbosacral radicular pain to the latest versions of the aforementioned AI chatbots. These questions were designed based on established clinical practice guidelines (CPGs). Each chatbot’s responses were evaluated for consistency, reliability, and alignment with CPG recommendations. The evaluation process included assessing text consistency, intra- and inter-rater reliability, and the match rate with CPGs. Key Findings The study also highlighted variability in the internal consistency of AI-generated responses, ranging from 26% to 68%. Intra-rater reliability was generally high, with ratings varying from “almost perfect” to “substantial.” Inter-rater reliability also showed variability, ranging from “almost perfect” to “moderate.” Implications for Healthcare Professionals The findings underscore the necessity for healthcare professionals to exercise caution when considering AI-generated health advice. While AI chatbots can serve as supplementary tools, they should not replace professional judgment. The variability in accuracy and adherence to clinical guidelines suggests that AI-generated recommendations may not always be reliable. For allied health professionals, including speech-language pathologists, occupational therapists, and physical therapists, AI chatbots can provide valuable information. However, it is crucial to critically evaluate AI-generated content and cross-reference it with current clinical guidelines and personal expertise. Conclusion While AI chatbots have the potential to enhance healthcare delivery by providing quick access to information, their current limitations in aligning with evidence-based guidelines necessitate a cautious approach. Healthcare professionals should leverage AI tools to augment their practice, ensuring that AI-generated advice is used responsibly and in conjunction with clinical expertise.