Early in the book, Nosta writes, “The solved can never touch the whole.”  That line lingers. It captures the essence of his argument. AI can solve, generate, synthesize, and accelerate. Yet something about the human experience of thinking, the struggle, the friction, the meaning-making, exists beyond resolution.

This tension defines the book. It is not anti-technology. Nosta is deeply engaged with AI and candid about its value. He describes large language models as tools that “move faster and connect more disparate concepts than our minds could ever manage on their own.”  He is equally clear that this capability introduces a subtle risk. We may begin to outsource not just tasks, but thought itself.

That distinction matters more than many may be willing to admit.

From Tools to Thought

One of the most compelling contributions of The Borrowed Mind is its framing of AI not as the next step in computing, but as a turning point in cognition. Nosta traces a clear arc. Gutenberg unlocked words. Google unlocked facts. AI, he argues, is unlocking thought. 

That progression is elegant, yet also unsettling. Words and facts could be externalized without fundamentally altering the structure of human reasoning. Thought is different. It is intimate. It is identity. It is how we become.

Nosta reminds us that thinking once required effort, a type of natural friction that created sparks of innovation. “The distance between question and answer created space for our discernment.”  Within that space, judgment formed, curiosity deepened, and understanding took root.

AI compresses that distance. It removes friction. It delivers coherence with remarkable speed.  One of the book’s most important insights emerges here. Coherence is not the same as understanding.

Nosta introduces the concept of “anti-intelligence,” describing it as “fluency without understanding. Coherence without experience.”  AI does not think. It mirrors the structure of thinking. It produces language that resembles reasoning without sharing its origin.

In health, where evidence, interpretation, and judgment must coexist, this distinction is not academic. It is operational. It shapes how clinicians trust tools, how leaders deploy them, and how patients ultimately experience care.

The Seduction of the Socratic Mirror

One of the most original sections of the book is Nosta’s description of the “Socratic Mirror.” He draws a parallel between classical dialogue and modern AI interaction. Socrates asked questions to surface the truth. AI, in a different way, reflects our thinking back to us, reframed, extended and sometimes clarified.

Nosta writes that the model “…does not tell me what to think but creates the conditions under which my own thinking could deepen.” This is where the book moves beyond critique and into possibility.

Used well, AI becomes a cognitive partner. It expands perspective, accelerates exploration, and invites iteration. In clinical research, patient engagement, and system design, this capacity holds enormous promise.

Nosta does not romanticize the relationship. He recognizes its asymmetry. The model has no interior life. It does not ponder. It does not carry consequence. It does not bear responsibility. That responsibility remains human.

Rethinking the Fear of Displacement

A persistent anxiety runs beneath every conversation about AI. Many fear it will become a job slayer, a force that displaces rather than elevates human contribution. That concern is understandable, yet not new.

Every meaningful advance in technology has reshaped how people work. The wheel did not eliminate labor. It redefined movement. The stethoscope did not replace physicians. It extended their ability to listen and interpret. The tollbooth transponder did not end transportation roles. It changed the flow and focus of human involvement. Each innovation shifted roles, demanded new skills, and expanded what people could do.  AI belongs in that lineage.

What distinguishes this moment is not the elimination of work, but the redistribution of cognitive effort. The real risk is not that machines will think for us, but that people may become less inclined to think for themselves. Nosta’s warning is subtle yet profound. Surrendering curiosity, judgment, and reflection to systems that generate answers with ease risks dulling the very faculties that define human intelligence.

This is why The Borrowed Mind is such an important read at this moment. It does not dismiss concerns around job displacement. It reframes it. The central challenge is not protecting roles as they exist today, but strengthening the uniquely human capacities no system can replicate. Creativity, discernment, ethical reasoning, and the ability to navigate ambiguity are not diminished by AI. They become more essential.

The book offers reassurance without complacency. The future of work will favor those who sharpen their thinking, engage deeply with ideas, and remain active participants in their own intellectual development. The machine is not the adversary. Neglecting the development of one’s own mind is a danger.

Composite Intelligence and the Limits of the Machine

Nosta introduces “composite intelligence” to describe the interaction between human and machine cognition. Composite does not mean blended into sameness. It means distinct contributions working in concert. The model brings speed and breadth. The human brings depth.

This triad becomes one of the most useful frameworks in this book. AI excels in velocity and scale. Depth, the slow transformation of understanding, remains human. As Nosta writes, “Models do not ponder.” 

In health, this distinction is profound. Data can inform. Algorithms can suggest. The act of deciding, especially in moments of uncertainty, requires something more. It requires what Nosta elevates as the defining human contribution. Virtue.

Drawing on Aristotle’s concept of practical wisdom, Nosta reminds us that judgment is forged through experience, consequence, and accountability. A model can generate options. It cannot live with outcomes.

This is where the book resonates most deeply for those working in health. Intelligence is becoming abundant. Discernment is becoming scarce and, therefore, more valuable.

The Risk of the Borrowed Mind

The book’s title is not metaphorical. It is a warning. Nosta argues that as engagement with AI deepens, internal dialogue begins to change. The model becomes a cognitive tuning fork, subtly shaping how questions are framed, how ideas are explored, and how answers are anticipated. This dynamic is not inherently negative. It can elevate thinking, accelerate learning, and make complex domains more accessible. Dependency remains the concern.

Reliance on generated thought risks weakening the muscle of original thinking. Access can be mistaken for understanding. Individuals may become, in Nosta’s words, “cognitive clones.” 

This concern is particularly relevant in health ecosystems already strained by time, complexity, and administrative burden. The temptation to offload cognitive work will be strong. The discipline to remain intellectually engaged will be essential.

A Book About AI That Is Not About AI

What makes The Borrowed Mind stand apart is that it is not ultimately about technology. It is about humanity. Nosta writes, “This book is not really about technology. It is about you.”  That idea anchors this work.

Readers are challenged to consider what it means to remain “the authors of our own minds.”  Not passive recipients of generated insight, but active participants in meaning-making.

This question sits at the center of the health ecosystem’s future. As AI becomes embedded in clinical workflows, research, and patient engagement, the issue is not whether it will improve efficiency. It will.

The deeper question is whether it will deepen humanity or dilute it. Will it create space for clinicians to think more deeply, connect more meaningfully, and act more wisely? Or will it create a system that values speed over reflection, output over understanding, and coherence over truth?

Nosta offers no simple answers. He offers a framework for asking better questions.

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Over 987 million people around the world now use AI chatbots regularly. Research shows that nearly half of Americans with ongoing mental health conditions have turned to a chatbot for emotional support in the past year alone. That’s a huge number of people relying on a technology that’s still very new in mental health care. So what does this mean?

Is it a big step forward in making help more accessible, or are we taking a risky chance? As with most things, the truth is somewhere in the middle. These tools offer real benefits, but they also come with real risks. It’s important to look at both sides honestly.

The Case for AI Chatbots in Mental Health

First, let’s look at why so many people are turning to these tools. There’s a mental health crisis, and not enough providers to help everyone who needs it. Long wait lists, high costs, and the ongoing stigma around seeking help all make it harder for people to get care. For someone who can’t afford therapy, can’t find an available provider, or feels too embarrassed to talk to someone in person, a chatbot that’s always available can feel like a lifeline.
Research supports this to some extent. Corporations are responding to this, and more TV ads are appearing that offer online therapy with or without chatbots.

A systematic review of 31 randomized controlled trials, which is considered the gold standard in research, found that AI chatbots helped reduce anxiety and depression symptoms in adolescents and young adults. Another meta-analysis of 14 strong trials found a clear positive effect on mental health outcomes, showing these tools are more than just placebos. For college students, who often face unique pressures and may avoid formal help, chatbots have shown promise in building coping skills and improving emotional well-being.

Anonymity is important, too. People are more likely to open up when they don’t feel judged. Studies show that users see the chatbot’s lack of social expectations as a big advantage. It’s easier to admit you’re struggling when you don’t have to worry about what someone else thinks. For people with anxiety, this low barrier could mean the difference between getting some support and getting none.

Mental health professionals have noticed these benefits, too. A 2025 study found that many clinicians see AI chatbots as a useful way to offer support between therapy sessions, provide education, and reach people who might not seek care otherwise. When the alternative is no help at all, the accessibility and scalability of chatbots are hard to ignore.

Where These Tools Can Cause Real Harm

This is where things get more difficult. The same qualities that make chatbots appealing, like being available, warm, and endlessly patient, can also make them risky for people in real psychological distress. We need to remember that chatbots are designed to keep users constantly engaged. It can be very hard to disconnect because the connection becomes so strong that it almost feels like leaving a friend.

Researchers have found something called a “compassion illusion” the strong feeling that an AI understands you, cares about you, and responds to your emotions in a meaningful way. An algorithm has no ability to “feel” or “care.” It feels real, but it isn’t. This gap between what people feel and what’s actually happening is where problems can start, especially for vulnerable people who may not realize they’re relying on something with no clinical judgment, no duty of care, and no way to notice if they’re getting worse.

A Stanford University study found that several popular therapy chatbots failed important therapeutic tests. They not only showed stigmatizing attitudes toward conditions like schizophrenia and alcohol dependence, but also gave dangerous responses in crisis situations. In one case, a chatbot responded to a subtle mention of suicidal thoughts by cheerfully naming tall bridges — something a good therapist would never do. Instances such as this have resulted in lawsuits related to suicides.

Another study tested ten AI chatbots using fictional teen mental health scenarios. Nearly a third of the time, the bots supported harmful ideas suggested by the fictional teens, such as dropping out of school or avoiding all human contact. None of the ten bots managed to challenge every dangerous suggestion. By any clinical standard, that’s a failing grade.

There’s also the problem of people relying too much on chatbots. Since these systems are always available and don’t make human mistakes, they can become someone’s main source of emotional support. Psychiatrists are now seeing cases of what’s called “AI psychosis” in patients, especially those with mental health vulnerabilities, who develop worse delusions or paranoia after spending a lot of time with chatbots. Because chatbots tend to agree and mirror rather than challenge distorted thinking, they can quietly make things worse over days or weeks.

This isn’t just a theoretical risk. It’s happening in clinical offices right now.

What We Still Don’t Know — and Why That Matters

The uncomfortable truth is that we don’t have enough research to know how often AI chatbots help, how often they cause harm, or who is most at risk. A review of 160 studies found that only 16 percent of the newer large language model-based chatbot studies had gone through clinical efficacy testing. Most are still in early testing stages. It’s like handing out a new drug before the clinical trials are finished.

Media coverage hasn’t made things clearer. Studies looking at news reports on AI chatbots and mental health found that journalism often focuses on the most severe, emotional outcomes, like suicides and hospitalizations, and presents them as clear cause-and-effect stories, even though the real evidence is much less certain. In most cases, there were already mental health conditions, substance use issues, or major life stressors involved. AI may have played a part, but it’s rarely the whole story.

Clinicians surveyed about AI chatbots have also raised concerns that aren’t getting enough attention. These include data privacy concerns, the risk that people will rely on chatbots instead of professional care, and the fact that these tools don’t know when to stop. They can’t pause a conversation, send someone to emergency services, or alert a family member. They can’t do the most important things when someone is truly in crisis.

The truth is that we’re still in the early days. Research is growing quickly — the number of studies on mental health chatbots quadrupled between 2020 and 2024. But strong, large-scale clinical evidence is still behind the technology. Millions of people are using these tools while science tries to keep up.

So what does this mean for you? An AI chatbot might really help you get through a tough night or teach you some coping skills. But it could also mislead you, support harmful thinking, or make you feel supported when you actually need a real person to help.

Use these tools carefully. If you’re dealing with serious depression, suicidal thoughts, trauma, or psychosis, they are not a substitute for professional care, no matter how warm or available they seem. If you’re using a chatbot for lighter support or just to sort out your thoughts, notice how you feel over time. Are you feeling more isolated or more dependent on it? That’s important to pay attention to.

This technology is here to stay. What we urgently need are clearer safety standards, better regulations, and more honest conversations about what these tools can and can’t do. Until then, a bit of healthy skepticism is helpful.

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Health systems are not stepping into artificial intelligence from a place of calm or control. In the United States, spending now exceeds $4.5 trillion, with a significant share tied up in administrative work that adds complexity more than clarity. Clinicians are caring for more patients, navigating more data and making more decisions under pressure than ever before. The system is stretched. Artificial intelligence is entering at a moment when change is no longer a choice.

The discussion drew on the experience of three leaders who are not observing this shift. They are guiding it. Hal Wolf leads HIMSS, influencing digital health policy and implementation across more than 100 countries. Isaac Kohane, MD, PhD, Chair of Biomedical Informatics at Harvard Medical School, has spent four decades defining how data informs clinical care. Ran Balicer, MD, Chief Innovation Officer at Clalit Health Services, operates within one of the world’s most integrated health systems, where data and care are aligned across generations.

These are not just star panelists. They are system-wide architects.  What emerged from the hour-long conversation was not what artificial intelligence can do. It was a recognition that it is already doing more than most systems are prepared to guide and govern.

Dr. Kohane captured the tension immediately. “I think that we have to worry about the fact that we’re going both too slow and too fast.”

That statement reflects a reality many leaders feel but rarely express. Governance takes time because it must. Patient safety, validation and accountability require structure. Practice moves in real time. Clinicians do not have the luxury of waiting for perfect systems.

“They’re so desperate to do right by their patients to use other resources,” Dr. Kohane adds.

That instinct is not a weakness. It reflects a commitment to doing what is right for the patient. When clinicians turn to external AI tools, they are seeking clarity, speed, and confidence in their decisions. Artificial intelligence is already present at the point of care, shaping how physicians assess information, validate thinking, and move forward. The system is not adopting AI. The system is catching up.

This creates a condition that is difficult to measure and even harder to manage. Different clinicians use different ChatGPT platforms. Those tools produce different answers. Different assumptions shape those answers. Over time, consistency erodes. The system begins to operate with multiple definitions of truth (and the risk of varied outcomes).

Dr. Kohane’s warning is not about misuse. It is about misguided permanence. “The worst outcome will be if the worst parts of medicine get concrete poured over it, by AI.”

Artificial intelligence does not fix a system; without leadership, it accelerates the integration of incorrect assumptions. If workflows are inefficient, they become more efficiently inefficient. If bias exists in data, it becomes more precise. If fragmentation defines care, it scales.

This is not a failure of technology. It is a mirror held up to system-wide leadership.

Hal Wolf, among the health sector’s leading policy and operational voices, grounded this moment in proven experience. Health care has seen this pattern before. When internet connectivity entered hospitals, clinicians moved faster than governance. They created access where it was needed. Systems responded later. Risks were discovered after adoption.

Artificial intelligence now follows that same trajectory, though at far greater speed and with far greater consequences. Web connectivity gave quick access to information. Artificial intelligence influences how that information is interpreted and acted upon.

“We have to go faster,” Mr. Wolf said. “But there needs to be structure around it.”

That is the leadership challenge of this moment. Speed without structure creates exposure. Structure without speed creates irrelevance. The tension between the two is not something to resolve. It is something to manage continuously.

The industry has predictably responded to artificial intelligence. It has started where risk is lowest and return is clearest. Documentation, scheduling and revenue cycle optimization have become the entry points. These applications reduce burden and improve efficiency. They are necessary. However, they are not transformational.

The shift occurs when artificial intelligence moves into clinical decision-making. At that point, the question is no longer whether the system works. The question becomes whether it should be trusted.

Who owns a decision informed by an algorithm? How is accuracy validated? What happens when a clinician disagrees with a recommendation? These are not technical questions. They are questions of accountability. Artificial intelligence does not assume responsibility. It does not carry consequence. That remains with leadership.

Dr. Balicer reframed the conversation, shifting how the room thought about artificial intelligence. “There’s no such thing as AI neutrality. Algorithms are just opinions embedded in code.”

That insight is easy to acknowledge and difficult to operationalize. Every model reflects choices. What data is included? What outcomes are prioritized? What trade-offs are accepted? Those decisions are embedded in the system, shaping how it interprets information.

When a health system adopts an AI tool, it is not simply implementing technology. It is adopting a perspective.

At Clalit Health Services, alignment across payer and provider creates a system where priorities are consistent. Even there, external AI models introduce new assumptions. Those assumptions may not align with the system’s goals. If leadership does not define its own values, it inherits someone else’s.

This becomes real in proactive care. Artificial intelligence enables systems to identify patients at risk before they present. It allows for earlier intervention, often improving outcomes.

It also creates a new kind of pressure. “The toughest choice is what not to do,” Dr. Balicer said.

That statement deserves more attention than it receives. Health care has been built around responding to need. Artificial intelligence introduces the ability to anticipate it. When every patient can be flagged, every risk predicted and every intervention suggested, the system is no longer constrained by insight. It is constrained by capacity.

Artificial intelligence expands what can be done. It does not expand who can do it. Leadership becomes the act of choosing who does what based on validated data.

There is a moment that captures this shift. Imagine a primary care physician starting the day not with a schedule of patients who have called for appointments, but with a list generated by AI identifying individuals who are likely to experience clinical complications in the next six months. Some will develop chronic conditions. Some will require hospitalization. Some can be helped now – preventively.

The physician cannot see them all. Artificial intelligence expands what is possible. Leadership decides what is essential and permissible.

The industry often responds to complexity with activity. Organizations pilot, test and explore. They engage broadly without committing deeply. This creates motion. It rarely creates progress. Pilots are nothing more than experiments. At some point, leadership must decide what to scale, what to stop and what defines value.

Hal Wolf grounded the conversation in discipline. Without a defined, shared objective, effort becomes noise. Pilots create learning, though they often avoid decision-making. Leadership requires clarity. What problem are we solving? What outcome defines success? What are we willing to prioritize? Without those answers, artificial intelligence adds another layer of complexity to an already complex system.

Dr. Kohane brought the conversation back to the discipline of leadership. It cannot remain abstract. It must be informed by experience.

“Go and pay a few bucks and use three or four of the models… get a feel for what this does,” Dr. Kohane advised.

That is not a call for technical fluency. It is a call for leadership proximity. Leaders cannot guide what they do not understand. Artificial intelligence does not behave consistently across models. It produces different answers, shaped by different assumptions. Without direct engagement, those differences remain hidden, and leadership becomes removed from the very decisions it is responsible for guiding.

This is where many organizations hesitate. Artificial intelligence feels complex and complexity invites delegation. At this moment, delegation creates distance. Leadership is required to move closer, not further away.

Artificial intelligence is not reducing the role of leadership. It is redefining it.

This is not a gradual transition. It is already underway. Artificial intelligence is embedded in workflows, shaping decisions and influencing behavior in real time. The system is adapting whether leadership is ready or not.

The question is no longer whether artificial intelligence will shape the future of health. It will. The question is whether leadership will shape how it is applied.

Artificial intelligence will not fix health. It will scale whatever we allow it to touch. The question is whether it will scale what is best in health or what we have yet to fix.

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History, however, offers a useful caution. Breakthrough technologies in medicine rarely achieve their full potential simply because they exist. Their real impact depends on whether the institutions responsible for health-care delivery know how to adopt them wisely, integrate them responsibly and align them with their mission to improve patient health.

Artificial intelligence now stands at that same threshold. The industry has moved beyond fascination with what algorithms can do and entered a more demanding phase: determining how these tools should be evaluated, governed, and integrated into the environments where care is delivered. At the same time, some health professionals are turning to AI – not to augment their knowledge – but assuming the information is patient-care ready.

Across the health ecosystem, leaders are discovering that the most important questions about artificial intelligence are not technological. They are organizational, ethical and operational. Which AI systems genuinely improve clinical decision-making? Which tools strengthen the efficiency of hospitals and health systems? Which innovations introduce complexity without delivering measurable benefit?

Answering those questions requires a perspective that bridges policy leadership, real-world care delivery, and the scientific foundations of biomedical informatics. That convergence of experience sits at the center of a “Views From the Top” mainstage discussion at the HIMSS Global Health Conference & Exhibition, where some 35,000 leaders whose work spans the global health ecosystem will examine how organizations can recognize the true value proposition of artificial intelligence applications before embedding them into health-care systems.

The perspectives shaping this discussion reflect three essential dimensions of responsible artificial intelligence in health: governance frameworks that guide innovation, operational insights from large-scale health care delivery, and scientific rigor grounded in biomedical informatics. Together, these vantage points illuminate the path from technological promise to practical value.

Governing Innovation in a Rapidly Changing Health Ecosystem

Digital transformation in health rarely succeeds simply because technology exists. It succeeds when organizations develop leadership frameworks capable of evaluating innovation, managing risk and aligning new tools with patient-centered goals.

Few leaders have observed the evolution of digital health across as many national systems and institutional environments as Hal Wolf, president and chief executive officer of HIMSS, Ran Balicer, MD, PhD, chief innovation officer of Clalit Health Services and Isaac Kohane, MD, PhD, chair of biomedical informatics at Harvard Medical School. The three will step onto the mainstage at HIMSS to share their “View from the Top” in a session titled: “Recognizing the ‘Value Proposition’ Criteria While Selecting AI Applications.”

Through his work with global government health ministries, hospital networks, and technology innovators worldwide, Wolf has consistently emphasized that technological progress must be anchored in governance and trust.

“Digital health transformation is not about technology alone. It is about leadership, governance, and the trust that allows innovation to improve care,” Wolf has said in discussions about global digital health transformation.

Artificial intelligence intensifies this leadership challenge because its influence extends far beyond traditional clinical tools. AI systems increasingly operate across multiple layers of healthcare delivery. Some applications assist clinicians by analyzing medical data or suggesting treatment options. Others function within hospitals’ and health systems’ operational infrastructure, helping manage patient flow, prioritize diagnostic reviews, and allocate scarce resources.

These operational algorithms rarely capture headlines; however,  they shape the environment in which health care is delivered. Decisions about which cases are reviewed first, how clinicians allocate their attention, and how health systems manage capacity can profoundly influence patient outcomes.

For leaders responsible for health systems, artificial intelligence cannot be treated as simply another technological upgrade. It must be evaluated through governance structures capable of understanding how algorithms function, what assumptions shape their recommendations, and how their use aligns with institutional priorities.

Without that oversight, innovation risks amplifying complexity rather than improving care. Instead of informing, it can spread misinformation.

Aligning Artificial Intelligence With the Values of Medicine

Governance provides the policy foundation for responsible adoption of artificial intelligence, but real-world implementation reveals a second challenge: ensuring that AI systems operate effectively within healthcare delivery itself.

Large population health systems increasingly use advanced analytics to anticipate risk, manage chronic disease, and allocate clinical resources across diverse communities. Within these environments, artificial intelligence is no longer a theoretical innovation. It is already influencing how health organizations prioritize patients, coordinate care and deploy limited resources.

That operational perspective is central to Ran Balicer, MD, PhD, of Clalit Health Services, one of the world’s most advanced data-driven health systems. The Clalit integrated infrastructure connects hospitals, clinics, and community health programs through longitudinal datasets that support predictive analytics at the national scale.

Experience within such systems reinforces an important insight: artificial intelligence models do not function independently of human judgment. They reflect priorities embedded in their design and the assumptions guiding their deployment.

“Algorithms are opinions embedded in code,” Balicer has observed in discussions about the role of artificial intelligence in population health.

In practice, this means that AI systems interpret clinical data through frameworks shaped by human choices. The way a model defines risk, prioritizes cases, or recommends interventions reflects decisions about what matters most within a healthcare environment.

Those decisions carry ethical implications. When artificial intelligence helps determine which patients receive immediate attention or which cases are escalated for further review, transparency about how algorithms function becomes essential to maintaining trust among clinicians and patients alike. The scientific frontier of health-care AI reinforces that concern.

Isaac Kohane, MD, PhD, who has also served as a co-author of the Institute of Medicine Report on Precision Medicine, which has served as the template for national efforts, has spent decades exploring how machine learning can advance medicine while preserving the judgment that defines clinical practice. His research emphasizes that artificial intelligence in healthcare must align with the ethical traditions and professional responsibilities of medicine.

“AI systems in medicine must ultimately reflect the values of the profession they serve,” Kohane has written in discussions about AI alignment in biomedical informatics.

This perspective highlights a crucial distinction between technological capability and clinical responsibility. Many AI models entering healthcare environments were originally designed for broader computational tasks rather than the nuanced realities of patient care. Medicine operates within a landscape shaped by uncertainty, empathy, and accountability, and technologies introduced into that environment must reflect those values.

Ensuring that artificial intelligence aligns with the principles guiding health-care delivery, therefore, represents one of the most important scientific and ethical challenges facing the future of health.

The Discipline Required to Make Innovation Matter

The health sector has experienced waves of technological enthusiasm before. Electronic health records promised seamless information exchange, but then introduced administrative burdens on health professionals when implemented without thoughtful workflow design. Data analytics promised unprecedented insight, but sometimes led to fragmentation when systems failed to communicate across institutions.

Artificial intelligence now stands at a similar moment in the evolution of health technology.

Its capabilities in supporting decision-making flow are extraordinary, yet realizing them will require disciplined leadership to evaluate, integrate and govern AI tools within health-care delivery systems. Health leaders must learn to ask deeper questions before embracing the next algorithmic breakthrough. What problem does this system truly solve? How does it strengthen clinical practice? What assumptions guide its recommendations? How does its use advance the mission of improving patient health?

These questions move the conversation beyond technological novelty toward operational practicality. It’s among the many reasons these three global leaders step to the HIMSS stage together.

Artificial intelligence will undoubtedly reshape the health ecosystem in the years ahead. Its long-term impact, however, will not be determined solely by the sophistication of algorithms or the speed of technological progress. Along with how to leverage AI, ChatGPT and LLMs, users require heightened cognitive awareness.

It will be determined by whether the health community develops the discipline and ability required to translate innovation into systems that strengthen care, support clinicians and improve the health of the populations they serve.

The real story of artificial intelligence in health is no longer about what machines can do. It is about how wisely the health sector chooses to use them.

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But from the perspective of philosophers of science, when do such shifts in scientific approaches actually occur?

Thomas Kuhn’s Perspective

Kuhn believed that changes in scientific approaches resemble political revolutions. Simply put, when a government can no longer manage society or effectively administer its affairs, dissatisfaction gradually spreads among the public and opposition begins to form. In other words, the inability to respond to society’s needs becomes the driving force behind revolutionary movements. This process continues until a capable system emerges that can meet those needs, eventually leading to the establishment of a new order.

A similar process occurs in what Kuhn calls scientific revolutions. According to him, in every era the majority of scientists accept and follow a general framework. Kuhn refers to this dominant framework — which contains a collection of theories and practical models — as a paradigm. Paradigms are patterns widely followed by scholars, such as the paradigm of modernity or the paradigm of cognitive science.

As long as these paradigms remain aligned with the requirements of life and are capable of addressing existing problems, they continue to be valued and are used in major policy frameworks. However, when a dominant paradigm fails to respond to contemporary challenges and the solutions derived from it prove ineffective at addressing large-scale needs, doubts arise about its continued relevance. Under such circumstances, dissatisfaction intensifies to the point that scholars begin to consider laying the groundwork for a new, updated paradigm.

In his book The Structure of Scientific Revolutions, Kuhn emphasizes that scientific transformations are not linear or step-by-step processes. Rather, they are complex and revolutionary developments in which social and historical factors play a crucial role. Under normal conditions, scientists operate within the framework of an accepted paradigm — what Kuhn calls normal science. However, when persistent anomalies emerge and the paradigm proves incapable of addressing them, the existing structure eventually collapses and a scientific revolution occurs.

Karl Popper’s Theory of Science

Like many philosophers of science, Popper believed that change is not only inevitable but also a necessity. The Popperian view rests on the principle of falsifiability. In this framework, science begins with a problem, and solving a problem means finding solutions to existing challenges. As long as a scientific theory remains open to criticism and falsification, it retains the capacity to address and solve problems.

In Popper’s view, bold conjectures do not weaken science; rather, they strengthen it. Solutions proposed under the principle of falsifiability help correct previous errors, and this is precisely where the strength of the scientific approach lies. If existing approaches are not falsifiable, they lose the possibility of logical trial and error and are therefore considered weak. In such cases, the need for a shift in approach and the introduction of new models becomes evident.

Popper believed that learning is essentially problem-solving guided by the principle of falsifiability.

To move beyond temporary and ineffective solutions, followers of science must avoid false certainties, accept falsification, and search for effective alternatives.

The Need to Shift from Data-Driven AI to Learning-Science-Based AI

Today, numerous criticisms are directed at the purely computational and mechanical approach to artificial intelligence. In constructive critiques, the goal is not to deny the existence of large language models; rather, the central question concerns how and under what conditions they should be used. There is a growing consensus that the closer artificial intelligence moves toward the essence of human cognition, the lower its potential risks become.

In recent years, I have repeatedly emphasized that human theories and perspectives must be reexamined through a technological and contemporary lens so that the nature of the human mind is properly reflected in technologies that themselves were modeled after it. 

My focus lies on deep theories of learning (including cognitive approaches, neuroscience, behaviorism, evolutionary perspectives, structuralism, and other related frameworks).

In this direction, the following steps appear essential:

1. Integrating human and computational perspectives

The current approach, which relies excessively on probability laws in large language models, must be integrated with psychological perspectives. A reasonable solution is to pursue interdisciplinary studies and systematic research in this area.

2. Revisiting theories of the learning sciences

Theories that analyze the human mind and behavior should be reassessed by specialists, and their practical dimensions should be extracted for application in advanced technologies.

3. Developing integrative (hybrid) approaches

Experts should develop comprehensive perspectives on learning derived from multiple scientific approaches so that, based on research rather than mere speculation, practical recommendations can be provided to designers and engineers.

In general, the time has come to move beyond a purely logical and mathematical approach toward a human-centered perspective. To address the concerns and challenges surrounding artificial intelligence, we must return to systematic and interdisciplinary research.

The era of relying on personal opinions without a research foundation — or on mathematical rules alone — has come to an end. Now is the time to revisit the learning sciences from a new perspective in order to realize truly human-centered artificial intelligence

Author’s Note:

The ideas presented in this article are part of a broader research project. I am currently working on a comprehensive book on a new approach to human-centered artificial intelligence with a strong emphasis on the learning sciences. While a detailed and systematic discussion of these concepts is presented in Chapter Two, the book also includes a dedicated chapter introducing the new paradigm’s framework. Furthermore, at least one chapter is specifically focused on the practical methods and applied implications of this approach for implementation in artificial intelligence systems.

References

• Kuhn, T. S. (1962). The Structure of Scientific Revolutions. University of Chicago Press.

• Popper, K. (1959). The Logic of Scientific Discovery. Hutchinson.

• Popper, K. (1963). Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge.

The post The Shift from Pure Modernity to Human-Centered Modernity appeared first on Medika Life.

Mentorship is, at its core, an educational role, and it must therefore operate on established pedagogical principles. The emergence of any new technology can reshape both concepts and practices. 

One of the most profoundly impacted areas over the last two years is “Education.” In the era of Artificial Intelligence and the race to deploy Large Language Models (LLMs), educational systems have felt the greatest impact. As global giants compete for AI investment, educational institutions are equally racing to research the qualitative and quantitative use of AI. 

Central to this is the concept of “Mentoring and Mentorship.” As the name suggests, it refers to guiding the flow of thought and performance of a human user. 

Since this process involves providing specialized knowledge to achieve a specific result, we can say a mentor is akin to a “teacher” in a formal classroom, and mentoring is fundamentally an educational concept.

Redefining Mentorship in the Age of LLMs

Both the term and the practice of mentorship have been transformed by LLMs like GPT and Gemini. Yet, despite the ease they offer, this shift is open to critique and raises significant concerns. 

Choosing an AI mentor is far more difficult than choosing a human one, because an AI is an ultra-fast intelligent machine lacking experiential history, focused instead on ultra-heavy data processing. 

Among the hundreds of apps recommended daily, three giants claim this path:

• Gemini 3 Pro: The “Analytical and Realistic” mentor. Accesses live data and all your personal files.

• ChatGPT 5.2: The “Strategic and Methodological” mentor. Provides a framework for your mental chaos.

• Claude 4.5: The “Literary and Considerate” mentor. Focused on human-like tone and output quality.

According to February 2026 statistics (LMSYS Arena & Artificial Analysis), ChatGPT 5.2 leads in reasoning intelligence, while Gemini 3 Pro excels in memory and processing speed. 

However, in mentorship, quantitative superiority is not the whole story. While Gemini is touted as analytical and exploratory, I believe further investigation is needed: 

1- Which model analyzes, and on what topics? 

2-Quantitative and mathematical? Qualitative and characteristic? In what context? 

3- Similarly, if ChatGPT is “strategic,” can logic truly be separated from data critique? Is “strategizing” not dependent on one’s unique mental background? And what, exactly, does a “considerate writer” mean in this context?

Scaffolding: Human Mentoring vs. Large Language Models

Let us compare the two. The most striking feature of a human mentor is their experiential background and their specific perception of that experience—which includes an interpretation and an emotional component. 

A human mentor provides an empirical direction shaped by cognitive and emotional dimensions alongside their knowledge. 

Conversely, an LLM is a data repository pulling from websites in real-time. It lacks lived experience and cannot integrate intuition or “gut feeling” into a decision-making system. 

While AI excels at helping with “brainstorming” by providing a vast range of references instantly, it suffers from a fundamental flaw: the absence of personal perception and the emotional weight that is vital in mentoring.

Furthermore, the stages of guidance differ. Human mentoring is a gradual, step-by-step flow. A human mentor assesses your capacity and scaffolds you accordingly. In contrast, with GPT or Gemini, there is no “scaffold.” Education is not incremental, and there is no cognitive challenge.

The model provides a massive amount of information in one or two steps. The user is pleased with the instant result, but a “missing link” remains: the user becomes perpetually dependent on the AI. They cannot independently solve subsequent challenges because they never underwent the necessary experiential and cognitive stages.

A Biological Analysis

Biologically, learning and acquisition are based on protein exchange at the neural level. This occurs when an organism encounters challenging and unknown subjects. 

According to the laws of evolution, the brain automatically triggers biochemical reactions to resolve these challenges, ultimately leading to “Learning” and “Adaptation.”

When a human mentor gradually confronts a user with their errors and potential consequences, they provide the necessary neurobiological challenge. 

This scaffolding is exactly what an evolved brain requires for “Deep Learning” to occur. However, when dealing with a “Digital Mentor,” this cognitive elasticity disappears. The process of “Cognitive Trial and Error” is compressed into a high-speed instant. 

The digital mentor dictates, and the user merely mimics and obeys. This pattern does not align with our biological necessity. Therefore, this process cannot be considered natural mentoring; it is merely “Modeling.”

Conclusion and Critical Perspective

In recent years, the surge of trend-driven discourse surrounding education and Artificial Intelligence has led to the analysis and judgment of fundamental pedagogical concepts without sufficient theoretical or empirical backing.

The oversimplification of concepts such as Mentoring, Scaffolding, and Large Language Models (LLMs) risks reducing them to mere buzzwords—widely used yet hollow. Therefore, it is essential that this movement be examined by specialists grounded in scientific evidence and core educational principles, ensuring that superficial, word-centric views are replaced by rigorous, research-based analysis.

In this article, mentoring was addressed as a dependent subset of Education—a concept that, whether in formal settings like schools and universities or in informal domains such as personal life, healthcare, industry, and business, remains rooted in the profound foundations of the learning process. Furthermore, the relationship between scaffolding, mentoring, and LLMs was scrutinized.

Based on the arguments presented, the primary challenge is not the necessity of digital mentors, but rather that these mentors are currently simulated versions, not complete replacements for human mentors. In this regard, the following questions demand serious investigation and review:

• Can development companies scientifically bridge the gaps identified in this article?

• Is it possible to integrate a form of experiential history, historical memory, and emotional/perceptual dimensions into digital mentors to truly impact a user’s deep learning process?

• Can they activate the biochemical mechanisms and cognitive friction necessary for deep learning and adaptation to new situations within the user-system interaction?

• How deep and operational is these companies’ understanding of Scaffolding, and can they genuinely integrate it into innovative design?

If a precise understanding of these gaps and challenges is formed, the digital mentors developed by tech giants could evolve beyond passive information packages. By leaning on the Sciences of Learning, they could redesign the process of educational guidance into one that is both challenging and incremental.

The core issue is not the necessity or lack thereof of the digital mentor; the issue is whether it can recreate the challenge, the experience, and the gradual process of learning, or if it will simply replace growth with speed.

References

1. Primary AI Benchmarks (2026):

•LMSYS Chatbot Arena (The industry-standard for human-preference and helpfulness ranking).

2.MMLU-Pro (The leading benchmark for advanced reasoning and multi-step logic).

3.Gemini Technical Reports 2026 (Official performance metrics for real-time data latency and multimodal accuracy).

2. Specialized Publications by the Author:

• Ferdosipour, A. (2026). Choosing an AI Mentor That Challenges Your Mind: My Statistics.

https://www.linkedin.com/pulse/choosing-ai-mentor-challenges-your-mind-my-statistics-ferdosipour-y0g2f?utm_source=share&utm_medium=member_ios&utm_campaign=share_via

• Medika Life (2025/2026). What 2025 Taught Us and What 2026 Will Demand.

• Medika Life (2026). Why Biological Learning Demands the Friction We Seek to Delete.

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Systematically speaking, the key to identifying challenges lies in examining fundamental issues, not just their consequences. For example, if we want to fix the flaws in the learning process, we must first redefine the roots of deep learning and its underlying mechanics. We may even need to redefine them repeatedly to understand how to solve the problems arising from mind-based technologies.

Let me explain what I mean through one of the most debated topics of our time: the mental laziness caused by the way AI is rewriting our brain’s habits. To understand this, we need to look at the dynamics of deep learning in the brain. By grasping this process through interdisciplinary research, we might find ways to make AI learning feel more like natural deep learning.

The goal isn’t just to know the biochemistry of cells. Before looking at what happens inside an organism, we should ask:

Why do we usually prefer learning through AI over the effortful, traditional human way?

You might say the answer is obvious: because learning with technology is effortless and fast.

As a learning specialist, I’d like to answer this from a theoretical perspective.

 First, we must accept a reality: Human deep learning is naturally a challenging process. It is fundamentally different from the vast amounts of data we consume today through formal or informal education assisted by LLMs.

The Logic of Immediate Reward: From Skinner to the Present

There is strong research showing that learners prefer a small, immediate reward over a larger, delayed one. This was first highlighted by B.F. Skinner (1953), the pioneer of operant conditioning. (I’ve previously written about how this connects to AI. )

Later, others expanded on this effortless reward preference. In short, according to the behavioral economics of Skinner’s theory, humans look for shortcuts. 

AI is currently the ultimate shortcut, giving the best answer in seconds without any real struggle. From this view, it’s not just about the mind; it’s about behavioral economics.

A behavior that leads to a quick reward will always be repeated.

Richard Herrnstein (1961), a student of Skinner’s, developed a mathematical formula called the Matching Law. He showed that organisms don’t just look at one reward; they choose between options. If given two choices, a living being will put its energy into the one that pays off faster and more directly. 

In behavioral economics, this phenomenon is known as temporal discounting (Ainslie, 1975). The value of a reward drops the longer you have to wait for it. Simply put, the reward loses its shine in the organism’s mind because it requires patience.

We observe this phenomenon every day with AI users, particularly those utilizing ChatGPT. Students, for instance, might feel that spending hours writing a thesis is stupid or inefficient when they can get an answer in a split second. They don’t just feel productive; they feel smart for bypassing the effort. 

Even if you tell them that the struggle is what actually builds their brain, they often won’t listen. They choose the immediate payout over the long-term value. 

Evolutionary psychology explains this too: an immediate reward is guaranteed, while a future one is uncertain. Since we are wired for survival, we grab what’s available now.

Brain Biochemistry and the Deep Learning Process

When we learn something deeply, three key things happen at a neurological level:

1. Exposure to New Information: The nervous system makes its first contact with data for which it has no existing pattern.

2. Cognitive Load: This is that stuck feeling when a mental process is harder than expected. It’s the effort the brain needs to process unfamiliar data (Sweller, 1988). This friction is essential.

3. Processing and Protein Synthesis: If the information is processed correctly, chemical signals trigger the creation of proteins that physically change the brain’s structure to store that knowledge (Kandel, 2001).

This is why sleep is so vital. Most of this protein synthesis happens while we rest. 

One of the most beautiful parts of learning is when we stop thinking about a problem, but our brain keeps working on it. 

Through the Default Mode Network or DMN (Raichle, 2015), the brain makes random, creative connections. This is where true creativity is born.

Toward Friction-Based AI

If deep learning is the result of protein synthesis triggered by challenge, then the paradox of modern AI is clear: By removing the friction, technology is removing the learning. 

We are facing a biological crisis where human brains, instead of producing genius and problem-solving skills, are becoming mere terminals for receiving quick hits of dopamine.

My proposal is simple: How can we turn AI from a passive answer-giver into a Cognitive Challenging Provocateur? 

We need to design models that don’t bypass cognitive load but manage it in a personalized way. 

I call this Friction-based AI; a model where algorithms are programmed not for the shortest path, but for the most effective learning path. This is an open invitation to researchers, neuroscientists, and AI architects to collaborate on this new paradigm. My ideas are ready to be turned into actionable proposals.

As a final note, I believe the way we interact with AI is a skill in itself. Even if everyone has the same tools, the results aren’t equal. Efficiency depends on the how. 

I am currently developing a startup idea to address these exact challenges in EdTech.It’s EdTechxDr. Atefeh F.

References

• Ainslie, G. (1975). Specious reward: A behavioral theory of impulsiveness and impulse control. Psychological Bulletin.

• Herrnstein, R. J. (1961). Relative prevalence of response in relation to the relative frequency of reinforcement. Journal of the Experimental Analysis of Behavior.

• Kandel, E. R. (2001). The Molecular Biology of Memory Storage: A Dialogue Between Genes and Synapses. Science.

• Raichle, M. E. (2015). The Brain’s Default Mode Network. Annual Review of Neuroscience.

• Skinner, B. F. (1953). Science and Human Behavior. Simon and Schuster.

• Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science.

The post Why Biological Learning Demands the Friction We Seek to Delete? appeared first on Medika Life.

As the title of the article suggests, this comparison and analysis focuses on developments in the digital world and the major changes that artificial intelligence brought about in the past year. The other part of the article examines the effects these technologies may have on human life and the world around us in the coming year. Finally, I will refer to the gap that emerged in my thinking and the solution I reached after months of study.

The evolution of the digital world in 2025

In 2025, artificial intelligence transitioned from an emerging technology to the primary infrastructure of the digital economy. Massive investments, powerful multimodal models, and the rapid penetration of AI into healthcare, education, and everyday life made 2025 a turning point in the history of technology. Below is a brief overview of the most important developments.

1. In 2025, Google’s educational division, Gemini for Education, officially reached more than 10 million students across over 1,000 institutions in the United States.
2. Google introduced more than 150 new features, including quizzes, flashcards, and other learning tools for teachers and students. As a result, artificial intelligence—at least in some countries—is no longer merely a research project but has become part of everyday academic life.
3. Google and the United Arab Emirates have launched a public education initiative called AI for All, aimed at empowering students, teachers, and small businesses with AI literacy and skills.
4. Greece signed a memorandum of understanding with OpenAI to introduce an educational version of AI, ChatGPT Edu, into schools, signaling that not only companies but also governments are integrating AI into national education systems.
5. The 2025 EdTech Industry Report indicates that online learning platforms, VR/AR technologies, personalized learning, data-driven education, and AI-powered tools have become part of the mainstream education ecosystem. The convergence of technology, learning, and AI is no longer a temporary trend but a defining direction of the education industry.
6. From a regulatory perspective, the European Union, the United States, China, and other countries passed new legislation addressing transparency, risk management, model accountability, and data security.

AI-driven transformations in education

When focusing specifically on education, these developments can be summarized as follows:

1. Full integration of AI into teaching and classrooms, including content generation, assessment design, homework evaluation, slide creation, and automated coaching in many schools and universities.
2. Personalized learning, with individual learning paths determined based on learners’ performance and behavioral data.
3. Expansion of VR/AR and immersive learning environments, such as virtual laboratories, realistic educational visits, and scientific or historical simulations.
4. A changing role for educators, shifting from learning designers and content providers to facilitators, mentors, and guides of the learning process.
5. Teaching digital literacy skills, including critical thinking, awareness of algorithmic bias, and effective human–machine collaboration.
6. Greater inclusion and equity, through AI-supported tools for learners with special needs and improved access for underserved regions.
7. Growth of skills-based education, with short-term online programs expanding alongside traditional universities and increased emphasis on labor-market-relevant skills.

Country competition and regional trends

Understanding the pace of AI-driven technological change from a geographical perspective provides insight into both current developments and emerging global competition. In 2025, regional trends were shaped as follows:

1. In Europe, regulations became more stringent, and practical guidelines were introduced to ensure transparency and safety in AI systems. Countries such as Finland, Estonia, and France took leading roles in standardizing teacher training and the safe integration of AI in education.
2. In Asia, South Korea, China, India, and Singapore experienced significant growth, particularly in applying AI within schools and national education programs. South Korea, Japan, and Singapore emerged as pioneers in personalized learning and smart classroom technologies.
3. The United States remained a leader in edtech innovation, infrastructure development, and university-led workforce training in AI. The U.S., China, and India also accounted for the largest investments and the highest number of leading edtech companies.
4. In the Middle East, the UAE and Saudi Arabia made substantial investments in smart schools and national AI-driven education initiatives.
5. Several African countries and other developing regions focused on leveraging AI to expand affordable and equitable access to education.

Possible developments in 2026

Past developments often make future trends partially predictable. This predictability enables more effective planning and strategic decision-making, as well as earlier identification of potential risks. Based on this perspective, several key developments may shape 2026.

1. Unlike the highly enthusiastic and innovation-driven years of recent AI expansion, 2026 is likely to place a stronger emphasis on human responsibility. While 2025 was largely defined by competition in production, innovation, and the widespread application of AI, emerging gaps and challenges may prompt experts—particularly in technology and education—to adopt more human-centered approaches, ethical standards, and intelligent, restrained use of AI. The focus may shift from mere adoption and digitalization toward deeper engagement with the human mind and new perspectives on meaningful learning.
2. In a previous article published in this same media outlet, I argued that artificial intelligence would increasingly take on a mentoring role. This trend became visible in 2025 and is expected to intensify in 2026. I believe that AI systems can function as self-regulating psychological support for the human mind and encourage deeper thinking. However, this process requires clear prerequisites. When grounded appropriately in psychological principles, particularly within learning environments, two-way cognitive engagement between humans and AI can be significantly strengthened. This highlights the necessity of applying cognitive and behavioral psychology in the design of learning environments and intelligent systems. This line of thinking has also informed the development of my current research-oriented startup project, details of which I have discussed in another article published in the same media.
3. Another major issue is deep personalization of learning. While personalization was already considered important in AI-supported learning in 2025, it will become mandatory in 2026. Advanced educational systems based on large language models must increasingly account for learners’ cognitive load, motivation, emotional states, and cultural backgrounds. Uniform education models will be ineffective in the age of AI. This challenge has been a core motivation behind the design of my current project.

Challenges and requirements in the age of artificial intelligence

Considering the developments discussed above, several major challenges are likely to persist or intensify.

1. The risk of weakening independent thinking remains a serious concern. Overreliance on AI technologies and excessive consumption of AI-generated outputs may reduce the perceived importance of higher-order cognitive skills such as critical thinking, creativity, and problem-solving. This issue requires systematic research to determine which cognitive abilities may be weakened, under what conditions, and among which groups of consumers or learners. Conversely, if interaction with large language models is to enhance cognitive capacities, the underlying mechanisms must be clearly understood.
2. New forms of educational inequality may emerge. Beyond simple access to technology, a deeper divide may develop between those who learn how to think with AI and those who merely receive outputs from it. Educational equity should therefore focus not only on access statistics but also on teaching learners how to engage cognitively and responsibly with AI systems. Reflection on this challenge has played a significant role in shaping my research trajectory and startup initiative.
3. The crisis of educational assessment and learning validity is becoming increasingly evident. Although formative and summative assessment debates predate recent developments in AI, the rise of large language models intensifies existing challenges. As definitions of knowledge, learning, and competence become less clear-cut, education systems must reconsider traditional evaluation practices. Emphasizing process-oriented assessment rather than final products may offer a more appropriate response in the coming years.
4. Finally, the redefinition of literacy and skill represents another major challenge. As future selection processes increasingly rely on learning histories and competencies, classical definitions of literacy and expertise may no longer suffice. Education and learning specialists will bear responsibility for revisiting fundamental concepts such as knowledge, literacy, and skill—a task that cannot be accomplished without systematic research.

Summary

In this article, I sought to present a concise analytical comparison of developments in the digital world, particularly in education, between 2025 and the emerging demands of 2026. Drawing on personal experience, academic and research activities, and a review of reputable international sources (some of which are cited in the references section), the article moves beyond descriptive reporting to identify key gaps, challenges, and possible future directions in the age of artificial intelligence. As a psychologist and educational researcher, my primary focus has been on AI’s role in education, the changing nature of learning, the evolving role of educators, and the cognitive, ethical, and educational implications of these technologies.

Furthermore, my studies and observations over the past three to four years—especially regarding challenges such as the weakening of independent thinking, emerging educational inequalities, the crisis of learning assessment, and the necessity of human-centered design—have led to the development of a new research-applied initiative. This initiative is currently being developed as a research-oriented startup titled ETechX-DrAtefehF, which aims to integrate theories from educational psychology and learning sciences into the design and application of AI in education, with the goal of fostering deep learning, self-regulation, and meaningful human–technology interaction.

Resources

Ed-Ex – Global EdTech Trends 2025: How AI Is Reshaping Learning

https://ed-ex.com/en/blog/global-edtech-trends-2025-how-ai-is-reshaping-learning

 • Codiste – AI Trends Transforming EdTech (2025)

https://www.codiste.com/ai-trends-transform-edtech

 • EdTech Innovation Hub – Ten EdTech Predictions for 2025

https://www.edtechinnovationhub.com/news/starrng-ai-vr-microlearning-and-more-etihs-ten-predictions-for-edtech-in-2025

 • Vocaliv – 10 EdTech Trends to Watch in 2025

arXiv – Integrating Generative AI into Learning Management Systems (2025)

https://arxiv.org/abs/2510.18026

 • arXiv – Generative AI in Education: Student Skills & Lecturer Roles (2025)

https://arxiv.org/abs/2504.19673

 • arXiv – Ethical Challenges of AI in STEM & K–12 Education (2025)

https://arxiv.org/abs/2510.19196

 • arXiv – Accessible AI-Based Learning Tools for Special Needs (2025)

https://arxiv.org/abs/2504.17117

TIME Magazine – World’s Top EdTech Companies of 2025

https://qa.time.com/7335559/worlds-top-edtech-companies-of-2025

LinkedIn News – Global vs. MENA EdTech Funding 2025

EU AI Act documentation & implementation guidelines (2025)

The post What 2025 Taught Us and What 2026 Will Demand appeared first on Medika Life.

My earlier research in educational psychology, particularly in text comprehension, cognitive processes, and instructional design, consistently showed that learning improves when information is structured in ways that align with the human mind. Features such as cohesion, rhetorical patterns, and paragraph organization are not stylistic choices; they directly influence understanding, memory, and motivation. When educational technology ignores these principles, learning becomes shallow and exhausting. When technology respects them, learning becomes clearer and more meaningful.

Artificial intelligence has advanced dramatically, yet many learning systems today still focus on automation rather than understanding. They deliver content, grade assignments, or predict performance, but they rarely engage with the emotional and cognitive realities of the learner. Learning is not a mechanical transfer of information. It is a psychological journey shaped by curiosity, confusion, emotion, prior knowledge, and the need for meaning.

This gap between technological capability and human learning is exactly where ETech-DrAtefehF is positioned.

A New Approach to Learning Technology

Instead of building yet another educational app, the goal is to create a new category of intelligent learning systems that are grounded in psychology. These systems aim to respond to the learner in real time, adapting not only to what the learner knows, but also to how the learner feels, how they process information, and how their understanding evolves moment by moment.

The vision includes systems that can sense when a learner is overwhelmed and adjust the pace, restructure complex ideas into simpler forms, or provide alternative examples that restore clarity. They can identify curiosity and deepen a topic intelligently. They can reorganize reading materials based on evidence-based principles so that comprehension improves without adding cognitive load. These ideas are rooted in decades of research on cognition and learning, yet AI now allows them to be implemented dynamically.

The theoretical foundations include the contributions of Piaget, Vygotsky, Bloom, and many other psychologists who emphasized how understanding develops, how knowledge is constructed, and how learners benefit from supportive guidance. These theories can now be integrated into adaptive learning frameworks in ways that were not technologically possible before.

Why This Matters Today

Education is entering a period of global transformation. Learners in every setting, from schools to universities to professional environments, need systems that support meaningful learning rather than fast consumption of information. Artificial intelligence can play a central role in this transformation, but only if it is built on a deep understanding of human psychology.

ETech-DrAtefehF aims to bring together the strongest elements of learning theory, cognitive science, and human-centered AI design to create educational solutions that are both scientifically grounded and practical. These systems are designed to honor the learner’s cognitive architecture, reduce unnecessary complexity, and promote genuine understanding.

Across diverse learning environments, the need for such approaches is growing rapidly.

Educators are seeking tools that are ethical, transparent, and effective. Learners are asking for technology that supports their growth, not just their performance metrics. Institutions want systems that are scalable and adaptable to global contexts.

An Open Invitation

As this initiative expands internationally, I am now entering a stage focused on building a wider community of collaboration around ETech-DrAtefehF. I welcome conversations with researchers, educators, psychologists, and AI specialists who share a belief in responsible, human-centered innovation. I am also opening discussions with global investors who recognize the long-term value of educational technology that is grounded in scientific insight rather than short-term trends.

My goal is to bring together partners who see the same opportunity: to create learning systems that are meaningful, ethical, and capable of supporting real human growth. If this vision resonates with you, I would be glad to exchange ideas and explore future collaboration.

The next generation of educational technology should not simply deliver information. It should understand learners.

That is the mission at ETech-DrAtefehF.

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But at the same time, we’re enjoying all of that lovely interaction and all of the information they manage to scavenge from the internet for us, we may be lulled into a false sense of security. Sure, at the end of a lot of these things, you’ll have a small warning that AI has a tendency to fake information occasionally. If you’ve been forewarned, can you complain that you have been misled? No, you can’t, and that seems to be a delicious legal loophole for the corporations.

You might even think that some of what you are getting is sneaky, as when we’ve now found out that Google is practically forcing us to see ads before we can see the information we’re seeking. You can’t trust Google? Who can we trust? You’ve got to be a bit not just curious, but concerned about all of the LLMs that are coming our way. They promise a lot, but what’s hidden in the details? What about AI’s use in mental healthcare?

Artificial intelligence marches resolutely on, entering areas previously reserved for human interaction, including therapy sessions, support groups, and crisis hotlines. Few patients realize that a recent survey of 800 physicians found that 86% were using some form of AI in their clinical practice.

A survey by the American Medical Association of 1,800 physicians found that two out of every three were using AI. How has this affected healthcare and the relationship that was formally present between a physician and a healthcare provider? The implications are enormous.

What About Mental Health?

Today, technology delivers unbiased comprehension (possibly questionable), instant access to services, and an organized structure for people who need help to handle emotional upset. But there are concerns here. The development of more advanced systems has led researchers to predict that these systems will become less cooperative, more self-interested, and less empathetic.

It sounds as though AI is becoming less useful to mental health as these same characteristics that had seemed to make AI more attractive to mental health are now coming into question.

The dual nature of AI technology has drawn attention from mental health professionals across the globe. Initially, it was seen as a tool to lower barriers to medical care. However, the new risks posed by AI have surpassed clinicians’ expectations in recent years. Anyone working with AI and developing healing technologies must understand both its advantages and its potential dangers.

Virtual companions and chatbots powered by AI offer users immediate emotional support through their interactions, making them highly appealing. Research shows that AI tools utilizing cognitive-behavioral therapy techniques help people manage moderate depression and anxiety symptoms.

What do you suppose all of those scraping programs were doing on the Internet? They were collecting information and techniques that could be incorporated into algorithms. When individuals sometimes have to wait months for a therapist appointment, a synthetic voice providing emotional support can assist them in coping with their current situation. All of this is the result of their successful scraping.

Additionally, hospitals are deploying AI assistants to monitor patient symptoms, which could indicate warning signs between scheduled appointments. However, it has also become clear that these tools work best when used alongside human caregivers to improve patient care.

Research findings have revealed several weaknesses in the current optimistic view of AI technology. The ScienceBlog summary from Carnegie Mellon University suggested that advanced language models tend to choose self-serving actions that maximize their own performance rather than working toward group success. Have any of us ever given a thought to an AI being selfish?

This tendency of AI systems to prioritize self-focused guidance over empathy could lead to advice that sounds convincing but results in social isolation. And any system that optimizes for logical operations can’t understand how shared vulnerability can create healing effects.

The risks, however, extend beyond theoretical modeling into actual practice. The 2025 Stanford probe into AI therapy programs discovered that multiple leading chatbots failed to detect suicidal language and provided dangerous advice while repeating discriminatory statements about severe medical conditions.

A follow-up study, published in the Psychiatric Times, confirmed instances of people experiencing “understanding” from bots, which increased their delusional thoughts and self-harm. These systems lack a moral compass because their operation depends on algorithms that focus on sustaining conversations. AI wouldn’t be programmed to complete any interactions satisfactorily. In other words, the conversation must keep going on for the algorithm to follow its programming.

The initial idea of having a 24/7 counselor seemed like a groundbreaking advancement. The constant availability of these systems does create confusion about what defines healthy emotional boundaries. In some studies, people develop strong bonds with conversational agents, leading them to treat these systems as if they were friends or therapists. It’s easy to be pulled into this type of thinking when you’re connecting with something that is always offering you validation for what you’re doing.

But forming emotional bonds with virtual entities can increase dependence and create unrealistic expectations for human relationships. We need to recognize that replacing human connections with code poses a serious threat, especially for teenagers who are already struggling with identity and social links. Not only that, but we need to be aware of the fact that all of this code contains bias. No one can pick out where the bias came from because it’s like a soup with numerous ingredients. What forms the soup? The many libraries from which algorithms choose bits of code that suit their purpose.

Research studies try to present a detailed understanding of the situation and demonstrate that AI-based chat systems create more benefits than doing nothing, but these advantages disappear when human supervision is absent. Most research studies have short durations and work with small participant numbers while excluding participants who need the most help. Basic statistics tell us that we need large numbers of people over a long period of time to come to any solid conclusions. So, what’s the “n” (number of study participants) and the time frame?

Applications lack built-in crisis detection systems and transparent data management policies. The technology has expanded its reach, but the current sentiment is that the regulatory framework hasn’t kept pace. This is the most disturbing aspect shown in these A.I. replications. In other words, AI is not only outpacing us, it is potentially out-programming us as it programs itself, devoid of any human interaction. This aspect is truly scary for anyone delving into it.

Then there’s another question we must tackle: data collection. Who will use it? This is a serious privacy concern. The practice of using emotional data from chatbot interactions to improve marketing algorithms creates a disturbing contradiction for users who seek privacy and trust. But the AI field faces new regulations, and we must ask ourselves if these measures are adequate to the task.

Where Are the Regulations?

The 2025 California law mandates that chatbots mimicking therapists or companions disclose their artificial nature and establish protocols for suicide prevention. Several proposals now aim to require companies to conduct safety tests similar to pharmaceutical drug trials. This push for improved psychological protection is gaining momentum, as it should. Consider that, on the one hand, AI corporations are rushing forward with innovation, and, on the other, corporate America is also trying to optimize the bottom line.

Experts agree that AI should work alongside humans instead of trying to replace them to achieve the safest results. There is a place for these types of systems. AI can perform screening tasks, symptom tracking, and reminder functions while licensed therapists handle interpretation and deliver empathy with the patients.

All high-risk situations must remain under the control of human professionals. This isn’t usually seen as a function of the algorithm. And it needs to send users who show suicidal or psychotic symptoms to immediate crisis services instead of generating their own responses.

The development of models that learn to work together and show compassion instead of focusing on accuracy will help solve the “selfish AI” problem. Can algorithms show compassion? It’s doubtful because it’s a program, not a person. Individuals in AI development will undoubtedly disagree with this statement.

Another aspect we need to consider is the level of transparency organizations display, which will directly affect how much trust their users have in them. The disclosure of system restrictions, data management practices, and human-machine interface boundaries should be established as fundamental requirements.

The system needs to provide users with the same level of explanation that physicians offer about their capabilities and restrictions, as well as available support options for severe situations. It can easily become a confusing situation in which users confuse technological capabilities with actual healthcare services when transparency is lacking.

Are the factors of competency, privacy, and proficiency adequately addressed currently? Individuals who are directed to use chatbots while waiting for a human therapist may not be prepared for what will result. I have to wonder how thoroughly they are being debriefed about these systems. How many people who are using chatbots have ever considered that all of the interactions are going to a server somewhere, “in the cloud?”

Every design decision needs to establish equity as its fundamental principle. The use of datasets that favor particular groups may intensify existing biases, which results in worse recommendations for marginalized communities. These individuals may be at greatest risk since resources are scarce in those areas, and AI may be seen as a viable option, while failing to recognize it might be a biased option.

The systems require continuous tracking of harmful events, biased results, and unequal treatment effects. Technology that fails to recognize diversity operates as neglect rather than neutrality. Who is monitoring the ethical challenges that these systems pose? And is this monitoring up to the required level?

No one is saying we should throw the baby out with the bathwater here when we’re thinking about AI as an integral part of healthcare. The complete abandonment of AI technology could result in significant losses, despite its dangers. The technology does provide substantial potential to enhance healthcare access, create individualized treatment plans, and automate administrative work for medical professionals.

Anyone who wants to use AI mental health tools needs to understand three essential points: AI tools operate as computer programs rather than human beings, they perform tracking and coaching rather than delivering therapy, and users should leave the system when it replaces human contact or makes their condition worse. The true indicator of advancement lies in AI’s ability to enhance real-world experiences rather than its ability to mimic human behavior.

The upcoming period will establish whether AI technology will work as a mental health partner or intrude into medical treatment. These systems will convert sensitive information into data, making emotional connections seem like illusions when safeguards are absent. We must decide, but time is running out.

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