It goes without saying that artificial intelligence has digitalized everything these days, including the healthcare sector—ranging from mental health chatbots to health assessment and monitoring tools. While these tools are impressive in terms of quality and speed, many users may abandon them after initial use. Alternatively, there may be a lack of sufficient trust in user data privacy, and this inability to capture consumer trust can leave product developers disheartened. These are just a small fraction of the challenges and issues that the digital health sector faces today.

These concerns are not merely theoretical assumptions. Recent digital health industry reports show that sustaining user engagement and retention remains one of the most critical challenges in this field. Despite heavy investments in developing technical and AI capabilities, many digital health startups face high drop-off rates and declining user engagement after the first few weeks of use. Recent reports from Rock Health and Deloitte have also shown that trust, user experience, and user-perceived value are among the most critical factors determining the success or failure of digital health solutions.

Let us dissect the core challenge a bit more deeply.

The reality is that the ultimate success of digital health tools does not depend solely on their technical prowess, even though the most precise mathematical calculations are designed and implemented by elite engineering teams to build these tools. Rather, their ultimate success depends on the quality of the user’s “cognitive,” “motivational,” and “behavioral” experience. In other words, the core issue is not simply “what the AI knows” and how fast it delivers it to users; the golden nugget is “how the human interacts with the AI.”

In the previous article or part one, the importance of “learning sciences” in developing “human-centered AI” in digital health was discussed. I highlighted the crucial point that the missing link in AI technology, including digital health, is the absence of a vital foundation known as the learning sciences. The present article is an operational continuation of that discussion, attempting to demonstrate how the learning sciences can serve as a framework for designing cognitive and behavioral experiences in AI-driven digital health tools.

In this article, I offer recommendations that are more operational in nature for manufacturers and designers of AI tools within the digital health industry.

Learning Sciences as the Foundation for AI Design in Digital Health

The learning sciences and psychology of learning consist of a body of findings and theories regarding how a relatively permanent change occurs within an organism or learner. These changes depend on various internal and external factors. Furthermore, this change involves the learner’s cognitive, behavioral, motivational, and physiological dimensions.

With this simple description, it becomes clear that the learning sciences are not limited strictly to educational environments. Because they study the processes of cognition, attention, motivation, mental engagement, feedback, self-regulation, and behavior change in human-environment interaction, the learning sciences are vital wherever learning, interaction, action and reaction, or behavioral continuity are involved. They aid us in understanding the behaviors, motivations, cognitions, and perceptions of learners.

Especially in the era of AI, the science and psychology of learning demand deeper immersion and greater precision in constructing AI tools. This urgency arises from growing concerns that these tools may not be human-centered, neglecting the existential dimensions of the human being as the primary user.

One of the most important areas of AI application is digital health. The learning sciences can serve as a necessary prerequisite in AI design, acting as both an interpreter and a facilitator.

In what ways are they a prerequisite for AI and digital health?

They are a prerequisite because digital health tools can themselves be viewed as environments for learning and behavior change—environments where users are constantly interpreting information, making decisions, regulating behavior, and building trust. Many current digital health tools, despite their technical complexity, are not designed based on the cognitive, behavioral, motivational, and transactional complexities of human beings.

Some users have reported that the explanations provided by AI systems were ambiguous, complex, or even confusing to them. This finding aligns with the World Health Organization (WHO) report on the ethics and governance of artificial intelligence for health. The report emphasizes that explainability is only valuable when it is understandable to the end-user, as overly complex explanations can themselves become a factor in reducing trust and increasing confusion.

In certain studies, users have stated that they cannot comprehend the system’s decision-making logic and are forced to simply trust or distrust its output blindly. In many cases, the user eventually abandons these tools after a while—a reaction that a learner might similarly display in an AI-driven educational environment!

These and similar problems demonstrate that providing information clearly and orderly is not enough on its own. The interaction must be meaningful and comprehensible within the user’s cognitive dimension; the tool must understand the user’s behavior and reinforce their motivations. As a result, continuous, purposeful interaction and trust will be fostered. It is precisely through purposeful interaction and trust that the tool becomes useful and works in service of the consumer (or the learner).

Designing Tools Aligned with the User’s Cognitive Dimension

As previously stated, the human being is a creature of interwoven, complex dimensions. According to psychological and especially learning theories, a major part of the learning process occurs mentally within the dimension of cognition. Therefore, understanding the formula of learning and its cognitive dimension is an essential blueprint for designing digital tools.

For instance, in the human learning process, an unfamiliar and unknown topic transitions into a familiar one through distinct stages. This process can typically occur via mental stimulation and environmental support. If this learning is deep and meaningful (rather than superficial or based on rote, parrot-like memorization), it can be recalled for a long time, and the likelihood of forgetting is minimized.

The science of learning encompasses cognitive theories that emphasize concepts such as perception, meaningfulness, the integrated whole, problem-solving, scaffolding, and similar ideas. If designers implement these abstract concepts operationally, they can achieve practical results, including solving the following issues:

1. Information Overload: One of the most significant challenges in digital health tools is information overload. For example, in some studies conducted on chronic disease monitoring platforms, users reported that a high volume of simultaneous notifications, charts, and recommendations led to mental fatigue and a decreased willingness to continue usage. Researchers describe this phenomenon as a type of Cognitive Overload, which can even degrade the quality of health decision-making. Users often interact with these tools under conditions of stress, anxiety, or mental exhaustion. In such states, presenting a massive volume of data, alerts, or advice simultaneously can induce cognitive fatigue, confusion, and reduced decision-making quality.
2. Complex Explanations: In some research, users of AI-driven health systems reported that overly complex explanations did not increase their trust; instead, they heightened anxiety, hesitation, and mental strain. These findings demonstrate that successful cognitive design does not mean providing more information, but rather reducing mental strain and facilitating user comprehension.
3. Sustaining Attention: Maintaining users’ attention and mental engagement is another major challenge in digital health. Many health applications are abandoned by users after a short period. Reviews published in the mHealth sector show that a significant portion of health app users severely reduce or entirely stop their interaction within the first three months. This indicates that initially acquiring a user and retaining their cognitive engagement for continuous use are two completely different challenges. Part of this issue stems from the interaction experience becoming repetitive, impersonal, and cognitively tedious. The missing goal here is the personalization of the process!
4. Gradual Adaptation: On the other hand, altering a user’s attitude and perception is typically a gradual process, not an instantaneous one. Yet, some digital health tools deliver a large volume of recommendations and information abruptly, without accounting for the user’s gradual learning and adaptation process. Learning sciences can help design experiences that create progressive, sustainable paths for shifting attitudes and beliefs toward a process or a tool, rather than applying sudden pressure.

Designing Tools with Regard to the Users’ Behavioral Dimension

As noted earlier, learning involves permanent changes in behavioral potential. Therefore, if a change occurs in the users’ cognition and attitude, we expect to see corresponding changes in their behavioral performance as well.

The learning sciences introduce frameworks to help us understand when and how a behavior becomes consolidated. How, and under what conditions, can we successfully navigate the channel of user cognition, establish a positive attitude toward using a tool or smart test, and ultimately compel them toward stable, purposeful behavior?

The psychology of behavior, as a branch of the learning sciences, steps in at this stage to assist digital tool designers. It prescribes that the formation and continuity of behavioral learning follow specific stages. Therefore, you must clearly define the behavioral prescription you intend to instill in the user:

For instance, in many diabetes management or weight loss programs, merely presenting information about an individual’s health status has not led to behavior change. Studies have shown that when tools provide features for gradual goal-setting, self-monitoring, and continuous feedback, the probability of forming sustainable health behaviors increases.

In short, determine what the target behavior is and define it clearly. Through what stages and micro-steps does this behavior form? What types of feedback and responses guide the user toward the final target behavior? Once the target behavior is formed, what factors or feedback mechanisms can sustain it? And based on what metrics can we determine that the user’s behavior and its continuity result from the proper functioning of the tool?

Furthermore, because our objective is to build human-centered AI and tools, we do not intend to control the user. Instead, we aim to reinforce healthy attitudes and behaviors by boosting their sense of self-efficacy and perceived control over their health journey. In doing so, we guide their behavior and choices along the right path, aligned with the human blueprint.

In fact, one of the growing concerns in the literature on AI in healthcare is the reduction of Human Agency. Some experts have warned that if systems replace human decision-making rather than enhancing it, cognitive dependency and diminished independent judgment may lead to unintended consequences. Hence, the goal of human-centered design must be user empowerment, not user replacement.

Additionally, creating sustainable behavioral habits requires progressive interaction, continuous feedback, and a design that adapts to the real-world context of users’ lives. Tools designed without considering the cognitive and social conditions of the user frequently fail to yield lasting change. Understanding behavioral science and the factors influencing the reinforcement or weakening of a response helps designers correctly guide user behavior while identifying and controlling potential confounding variables inherent in digital tools.

Designing Tools Aligned with the User’s Motivational Dimension

Precisely when some neuroscience specialists argue that everything occurs at the level of cognition and that all other complex human aspects are overshadowed by it, the learning sciences (of which neuroscience is only a part) tell us it is not that simple!!! Learning an idea is a process. If we want a meaningful idea—such as using a health monitoring app—to transform into a highly repetitive, sustained behavior, we must account for other human dimensions as well!

“Trust” is one of the most critical factors in the adoption and motivational continuity of digital health tools. However, trust is not built solely through technical transparency. Users need to feel that the system is understandable, predictable, and psychologically safe.

Several studies have shown that complex or overly technical explanations fail to build trust and instead trigger greater anxiety and confusion among users. Moreover, concerns regarding privacy, data sharing, and the secondary use of health data represent major drivers of distrust among users. In digital health, trust is not merely a technical issue; it is part of the relational experience between the human and the system. For this reason, user experience design must consider psychological safety and relational trust alongside technical security.

Why is trust important? Because it is the loop that connects a user’s cognition, beliefs, and attitudes to their actual behavior! It generates the necessary motivation for follow-through, and ultimately, consolidates a behavior.

Findings from studies conducted on mental health chatbots indicate that anxiety over how personal data is stored, secondary data use, and a lack of transparency regarding data ownership are primary factors driving down user trust. In many instances, users evaluated the perceived quality of the relationship with the system as even more critical than the technical complexity of the algorithm.

While many digital health tools focus heavily on delivering information, possessing information does not automatically translate into the “motivation” required for behavior change. If the user does not feel capable of performing the recommended behavior, the likelihood of continued system utilization drops.

Alongside trust as a motivational component of user behavior, one of the most foundational concepts in the psychology of learning is “self-efficacy.”

Extensive research in health behavior change demonstrates that individuals who believe they possess the capacity to execute recommended actions are far more likely to initiate and maintain the new behavior. Consequently, successful design does not stop at giving advice; it must craft an experience where the user can taste small but meaningful victories.

Self-efficacy refers to an individual’s belief and confidence in their own abilities to organize and execute the courses of action required to achieve a specific goal. This psychological attribute can be modulated via controllable, situational feedback, and AI designers can leverage it as a key lever to impact human motivation.

In a digital health environment, this motivational characteristic can serve as the driving force behind consumer behavior when interacting with smart medical tools. Therefore, AI-driven tools must be capable of reinforcing a sense of empowerment and progressive mastery in the user, rather than merely broadcasting a barrage of alerts and directives. Studies indicate that users achieve more sustainable, satisfying engagement with health systems when feedbacks are personalized, actionable, and contextualized within the actual reality of their daily lives.

Feedback itself is only effective when it is timely, clear, and meaningful. Generic, non-actionable feedback—such as vague lifestyle advice—typically exerts a highly limited impact on behavior change. Conversely, contextualized, action-oriented feedback can significantly heighten both the cognitive and motivational engagement of the user.

Expected Operational Implications for Design and Development Teams

Many digital health tools still focus predominantly on algorithmic performance and technical functionalities, whereas the sustainability of human-system interaction relies on the quality of the cognitive and behavioral experience—and, of course, the motivational loop that links cognition to behavior and drives its continuity.

The learning sciences and psychology of learning can empower design and development teams to move far beyond the mere metrics of “ease of use” and “time management.” This shift is the most vital achievement of a cognitive, motivational, and behavioral architecture governing human-AI interaction.

This issue holds particular urgency for startups, product design teams, and digital health developers; the true success of these tools does not hinge on the sheer number of features, but on their capacity to preserve sustained engagement, secure trust, and guide human behavior change.

The learning sciences offer a framework to design tools that are not just usable, but comprehensible and justifiable within the users’ cognitive schemas. It shapes and directs user behavior under their own autonomy through self-regulation mechanisms, supplies the motivational loops connecting thought to action, and stabilizes user behavior.

Perhaps the most definitive question for the future of digital health is not how much smarter artificial intelligence will become, but rather how much better it can comprehend human cognition, motivation, agency, and behavior. Ultimately, the most successful tools will not necessarily feature the most advanced algorithms, but those that possess the deepest understanding of the human being.

References

Bandura, A. (1997). Self-Efficacy: The Exercise of Control. New York: W.H. Freeman.

Schunk, D. H. (2020). Learning Theories: An Educational Perspective (8th ed.). Pearson.

Hergenhahn, B. R., & Olson, M. H. (2015). Theories of Learning (7th ed.). Pearson.

Zimmerman, B. J. (2002). “Becoming a Self-Regulated Learner: An Overview.” Theory Into Practice, 41(2), 64-70.

Deloitte. (2024). 2024 Global Health Care Outlook.

Deloitte Center for Health Solutions. (2024). Digital Transformation and Consumer Engagement in Healthcare.

Rock Health. (2024). Digital Health Consumer Adoption Survey.

Blease, C., Kaptchuk, T. J., Bernstein, M. H., et al. (2019). “Artificial Intelligence and the Future of Primary Care.” The Lancet Digital Health, 1(8), e353-e354.

Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

World Health Organization (WHO). (2021). Ethics and Governance of Artificial Intelligence for Health.

Sweller, J. (1988). “Cognitive Load During Problem Solving: Effects on Learning.” Cognitive Science, 12(2), 257-285.

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At a time when Artificial Intelligence is rapidly weaving itself into the fabric of physical and mental healthcare, basic user-friendliness, processing speed, and market acceleration are no longer enough. To build digital solutions that actually work, we must grasp how humans learn, adapt, and transform their behaviors. This is exactly where the learning sciences become vital. Put simply, until we decode the mechanisms of ‘deep learning in humans’ through the lens of learning sciences, the concept of ‘Deep Learning’ in AI development will never reach its true potential.

Digital health, much like any other modern domain, is now permanently tied to technology. From education and corporate structures to parenting and economics, technology is built to streamline processes, widen access, and boost precision. At its core, technology was created to serve humanity across individual and social spheres, and digital health stands as one of the most critical testing grounds for this promise.

Yet, alongside this reality lies a much bigger issue—one that is gaining traction and deserves a rigorous, interdisciplinary look.

The question isn’t whether technology is inherently good or bad; it is that even the most advanced technology remains ineffective if it fails to align with human blueprints.

Today, more than ever, we need to look at AI and digital systems through a deeply human lens. This means moving away from treating an individual merely as a ‘user,’ a ‘data processor,’ or a passive ‘receiver,’ and instead recognizing them as a multi-dimensional, complex, living being.

In digital health, our core focus is the human being—the patient striving for recovery, the client seeking a precise diagnosis, the therapist requiring sharper diagnostic tools, or the physician leaning on technology to make high-stakes clinical decisions. The human is always the ultimate destination. If a digital tool is to succeed in this space, it must genuinely connect with real people, accounting for their cognitive, behavioral, biological, and experiential complexities.

Why Research in the Learning Sciences is Indispensable

In the digital health space, the real challenge is never just about getting someone to install an app or use a digital tool temporarily. The true measure of success is whether that tool can drive a real, lasting change in human behavior, attitude, and lifestyle. If a person engages with a platform for a brief period but experiences no sustainable shift in their health or daily habits, the technology has fundamentally missed its mark.

This is where the learning sciences help us elevate technology design far beyond surface-level mechanics and computational algorithms. When we understand how a person actually internalizes information, we can build better communication strategies, deliver more constructive feedback, apply the right behavioral reinforcements, and create environments that foster genuine trust, motivation, and user engagement.

Furthermore, this scientific backing allows us to grasp privacy and data security from the psychological standpoint of the user, since a patient’s willingness to trust a system is directly tied to how safe they feel sharing their data.

Two Foundational Pillars: Trust and Continuance Intention

To see how the learning sciences practically guide human behavior in the era of AI, we can look at two crucial dynamics in digital health:

1. The Mechanics of Trust
Trust is the ultimate currency in digital health, because users are asked to hand over highly sensitive personal, biological, and psychological data to an algorithm.

2. Continuance Intention and Habit Formation
Capturing a user’s attention at launch is relatively easy; keeping them engaged over time is where the tech industry routinely struggles.

Conclusion

The defining critique of modern AI is not its widespread adoption, but its lack of authentic human-centricity. Successful digitalization in healthcare cannot rely solely on technical scalability; it must place the complex human being squarely at the center of the design process.

Technology only gains meaning when it can understand human beings, build a relationship with them, earn their trust, and guide them toward lasting well-being.

Ultimately, the future of digital health will not be measured by raw processing power, but by the depth of the developer’s understanding of the human condition.

References

Sucala, M., Cole-Lewis, H., Arigo, D., Oser, M., Goldstein, S., Hekler, E. B., & Diefenbach, M. A. (2021). Behavior science in the evolving world of digital health: Considerations on anticipated opportunities and challenges. Translational Behavioral Medicine, 11(2), 495–503. https://doi.org/10.1093/tbm/ibaa034

Bai, B., & Guo, Z. (2022). Understanding users’ continuance usage behavior towards digital health information system driven by the digital revolution under COVID-19 context: An extended UTAUT model. Psychology Research and Behavior Management, 15, 2831–2842. https://doi.org/10.2147/PRBM.S364275

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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.

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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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Since my field of study is educational psychology, and I am especially familiar with learning sciences, I write mostly about them. I believe combining research-based evidence is always more valuable and reliable than relying solely on personal ideas, even if they are logical.

In my writings and articles, I have repeatedly emphasized that sometimes we need to look back and integrate well-established scientific theories with modernity and artificial intelligence. I combine scientific evidence, including research articles and theoretical frameworks, with my own analyses, using them as a bridge to technology.

This approach and strategy prevent many potential risks. Instead of a preachy, rigid, or purely philosophical perspective, we adopt a systematic, scientific approach to derive practical solutions. One of the issues and concerns frequently discussed these days, which I have also mentioned in my recent articles, is the “consequences of excessive ease of performance through artificial intelligence.”In my latest article, I discussed the absence of “Fraction.”

In this article, I do not intend to discuss Fraction directly but rather focus on another challenge in the same area, which is not entirely unrelated to Fraction. This topic is the “level of anxiety and arousal resulting from facing performance.”

First, I will briefly explain this concept and then examine its connection to artificial intelligence systems.

Arousal Theory in Learning Psychology

One important theory in the neurophysiology of learning is Donald Hebb’s framework, which aligns with evolutionary approaches.

According to these perspectives, the human brain needs challenges to survive. The nervous system has evolved in challenging environments, and both anxiety and an optimal level of arousal have always been essential for survival. They increase alertness against potential risks and guide humans toward growth and the adaptation of necessary skills.

Donald Hebb, a neuroscientist, studied human learning, and one of his significant contributions was explaining the role of arousal in learning.

In Hebb’s framework, “arousal” is considered the fuel for the cerebral cortex to process information. Learning depends on neural plasticity, and this process occurs under an optimal level of arousal.

From this perspective, the brain is not simply trying to reduce tension but is seeking an optimal level of stimulation. If environmental stimuli are too low, the brain may create artificial stimuli or lose part of its natural efficiency.

As a result, neural firing and synaptic strengthening occur under the influence of arousal, and when arousal decreases significantly, the likelihood of forming or strengthening these connections decreases.

In addition to Hebb’s explanation, the classical “Yerkes-Dodson Law” also supports this necessity. According to this law, human performance improves with increasing physiological or mental arousal up to a certain point. When arousal is very low (a state toward which AI tools tend to push us), individuals experience reduced focus and cognitive motivation, and learning efficiency reaches its lowest point. In fact, a certain level of pressure or anxiety is not harmful; it is a prerequisite for achieving peak mental performance.

The “Arousal Gap” Challenge in Interaction with AI

As briefly explained in Hebb’s framework, the prerequisite for the neural interactions that lead to learning, perception, and cognitive actions is stimulation and arousal.

This moderate level of stimulation, which Hebb calls optimal arousal, is neither unpleasant nor at odds with the brain’s evolutionary nature in adaptation processes.

Now, imagine that a significant portion of our tasks is performed by an artificial partner and creates no direct cognitive responsibility for the individual. In such a scenario, what challenge will arise in human thinking?

These days, many articles and writings discuss the “excessive ease” challenge posed by AI tools. However, this article specifically focuses on reducing arousal levels and achieving optimal anxiety, according to Donald Hebb’s framework. Here, anxiety is considered one form of arousal, not equivalent to it entirely.

If most daily tasks are performed without prior stimulation or anxiety and without active cognitive engagement by AI, instead of the tools being under the consumer’s control, the consumer will be under their control.

From an evolutionary perspective, under such conditions, learning and cognitive adaptation processes will not align with the brain’s natural growth patterns, and the likelihood of effective knowledge adaptation will decrease.

The manifestations of this challenge will likely be observed in longitudinal studies as changes in the quality of cognitive performance and in neural circuit activity patterns.

References

Olson, M. H. & Hergenhahn, B. R. (2020). An Introduction to Theories of Learning (10th ed.). Routledge. 

Schachtman, T. R. & Reilly, S. (Eds.). (2011). Associative Learning and Conditioning Theory: Human and Non‑Human Applications. Oxford University Press.

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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)

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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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Unlike the previous generation, which has only lived in the era of artificial intelligence technology, the Beta generation will be the first generation to grow up fully in a world of integrated technology, including autonomous vehicles, health technologies, and pervasive virtual environments. 

Undoubtedly, this new generation, which is the children born in 2025, faces new challenges. 

This group will inherit a world that is grappling with complex challenges such as climate change, rapid urbanization, and changing population dynamics that require adaptation, collaboration, and innovation. Therefore, understanding the potential challenges and anticipating effective solutions for such a period is very much inevitable. As usual, the first and most important step in nurturing such a special generation that will live in a challenging period of human history is educational measures and investing in their proper education. 

Therefore, the traditional and common education system will no longer meet the stated needs of the Beta generation and the current conditions, and perhaps changes or reforms will be necessary to prepare as much as possible not only a generation but all units of society that are responsible for this generation and the conditions and challenges ahead. 

A lot of research has been done on education systems, highlighting the problems of the current systems. These systems may not only not foster creativity and innovation but sometimes suppress higher mental abilities such as creativity and problem-solving. 

For example, George Land and his colleagues designed a landmark experiment for NASA in 1960 that showed that one reason many people are geniuses in childhood but less so in adulthood is the weakness of education and school-solving.

In explanation, traditional schools and educational systems promote convergent thinking and reinforce a single answer to a single problem, while the emergence of creative thinking is based on convergent thinking. 

On the other hand, creative children not only do not thrive in this environment but also become frustrated due to the lack of support from the school for genius and unique answers and the hope of obtaining high scores on common school tests. They may not find the educational environment attractive and leave it! 

It seems that a general solution is to reform the educational environment so that the integration of innovative and flexible methods that give priority to children’s independence, experience, and interests is a correct educational reform that paves the way for the training of modern world actors, who are the children of the Beta generation. Meanwhile, it seems that Elon Musk has an educational idea that has a kind of futurism hidden in it, and he showed it a few years ago by founding the Ad Astra School.

– Elon Musk personality as someone who doesn’t believe in the impossible 

Before anything else, I would like to ask the following question and present my general analysis:

– Why can Elon Musk have an educational idea in addition to the ideas of technologists?

My analysis and impression is that Elon Musk is known not only as a wealthy entrepreneur but also as an innovator, creator of practical ideas, and futurist.

It seems that he is not just an idea generator or supporter of new ideas, but he has repeatedly shown that he is an extremely pragmatic and pragmatic person. A pragmatic person who has the flexibility to actualize his own and others’ ideas, puts them into practice and tries to make access to the future smoother by realizing new ideas. In addition to perfectionism, he has intellectual flexibility. “Impossible” seems to be an unfamiliar word for him. The attractive and effective seasoning of these characteristics is his remarkable hard work.

Therefore, given what was said in the introduction and what I briefly said about Elon Musk’s personality, educational ideas for the new generation that consider the future are not far-fetched.

Elon Musk said    …. We should expand consciousness to the stars so that we may better understand the wonders of creation.

In my opinion, the best way to explore Elon Musk’s educational ideas in this sensitive era is to analyze the structure and nature of the Ad Astra School.

-Ad Astra School Educates the Innovative Generation and Creators of Future Technologies 

In 2014, Elon Musk launched an institution in California near Space x to educate children aged 3 to 9, which seems very different from conventional education and traditional schools from the very novel. Musk’s initial goal in establishing this school was to provide exclusive education for his children and those of his employees.

Therefore, Ad Astra School is an innovative and nontraditional educational process whose main goal is to prepare students to face the real challenges of the world and the future. Instead of following traditional educational systems, this school focuses on developing practical skills, critical thinking and problem-solving, and creativity.

Compared to the regular educational courses, Ad Astra School focuses on teaching subjects such as advanced mathematics, engineering, science, artificial intelligence, programming, ethics, robotics, marketing, and some other practical, real-life skills. Moreover, instead of memorizing material, students do practical projects that involve solving real problems(project-based).In addition, students learn the ability to analyze, think critically, and be creative in solving problems through teamwork. 

It seems that the purpose of this school is to educate students who can succeed in the fast-paced world of technology. Ad Astra tries to cultivate students’ natural curiosity and help them acquire the skills necessary to build a better future. 

Perhaps in those years, Elon Musk and the experts of the founding team of Ad Astra predicted the fast-paced years of advanced technologies of the present and thus designed a school suitable for children of the beta generation. That is why a significant part of the content and structure of Astra is working with modern science and technology. Children are considered small entrepreneurs and masters who have all the possibilities of manufacturing and production, and the role of the educational environment is the role of a guide and provider of the necessary platform! 

Elon Musk’s Ad Astra School exemplifies a futuristic approach to education tailored for the Beta Generation. Its Ad Astra focuses on future-oriented topics and equips students with skills and mindsets to navigate a world shaped by artificial intelligence and advanced technologies.

– Key features and fundamental principles 

What possible approaches or trends have inspired Ad Astra?

In my point of view, like many unconventional trends, this educational transformation has also been influenced by trends with different perspectives. Some of which are old and some of which are future-oriented.

Here is my classification of inspiring ideas:

1- The influence of the Montessori approach as a philosophical approach 

The Montessori method is an educational approach based on active learning that was designed by Maria Montessori, an Italian physician and educator, in the early 20th century. This method believes that every child has a unique potential for learning and growth and that the role of education is to facilitate this natural process.

This method emphasizes that the education system should stop wasting time and teaching unused reserves in real life and wasting time and instead try to educate future citizens and professionals in a way that they know what to do as soon as they enter the real world. This approach is futuristic and believes that education should consider the learner as a problem solver.

According to this perspective, the learners progress at their own pace and in serious activity, and no learner will be passive and forced to acquire knowledge. Knowledge and learning are learner-centered and are the product of the learner’s active interaction with the educational environment. This means that it is based on the learner’s abilities, interests, and needs. In other words, the learner has an active and meaningful role in this educational environment to acquire applied knowledge.

This approach is based on principles such as learner-centeredness, an organized learning environment, and learning and problem-solving through experience and observation. among contemporary scholars, Jean Piaget, Vygotsky, and Gardner have been inspired by the Montessori approach.

2- Artificial Intelligence and a Future-Oriented View of Generation Beta 

It seems that the most effective trigger for the layout and design of such an institution is the rapid advances in advanced technologies in artificial intelligence.

As mentioned in the first part of the article, these days, the requirements of life have changed due to the amazing speed of technological changes. It is better for the heart of any society that educates future generations to accept the responsibility of creating them the power of adaptation and the knowledge necessary to live in the super-modern era.

The Alpha generation has largely experienced these changes but has not yet been immersed in them. However, the Beta generation is expected to be immersed in the leaps of the age of artificial intelligence and large language models. This generation must have the skills to adapt to technology from the very beginning. In other words, the Beta generation is a problem solver and adapts to the rapid changes of this era as quickly as possible.

It is used to describe a new generation that is growing up in a world heavily influenced by artificial intelligence (AI). 

Here is a brief explanation: Generation Beta (AI Age) This term may symbolize a generation that was born or raised during the rapid rise of artificial intelligence, automation, and advanced technology. Key characteristics of this generation can include the following: 

A)  AI-Native Mindset

They are AI natives, who are deeply integrated with the understanding, application, and potential of artificial intelligence. This mindset is shaped by living and working in a world where AI is a natural and ubiquitous part of daily life, decision-making, and creativity.

B) Access to advanced technology:

 They grow up with AI-based personal assistants, robots, and smart environments, influencing the way they learn, communicate, and solve problems. 

 C) They face new challenges and opportunities: 

 They may face challenges related to privacy, ethics, and automation in the workforce, but also opportunities for creativity and innovation enhanced by AI.

 D) They value evolving social norms: 

With AI deeply embedded in their lives, this generation could redefine ideas about work, identity, and human-AI collaboration. 

 The name Beta generation may suggest a period of experimentation and transition, as this generation could be seen as a prototype for future societies shaped by AI.

Ad Astra’s effectiveness in educating Beta generation 

Ad Astra is likely to be effective for Generation Betas for the following reasons: 

1. Focuses on Problem Solving and Creativity 

– Ad Astra prioritizes problem-solving, critical thinking, and creativity, which are essential skills for the Beta generation growing up in the age of AI. Instead of traditional classroom learning, the curriculum emphasizes collaborative, hands-on projects and tackling real-world problems, preparing students for a future where AI will perform repetitive tasks. 

2. It has customized learning approaches. 

 – This school rejects traditional age-based grading and standardized testing, opting instead for personalized instruction tailored to each student’s strengths and interests.

 – This aligns with an AI-driven world where adaptive learning and individualization are key to maximizing human potential alongside machines. 

3. Emphasis on Technology and Ethics

 – Students are exposed to cutting-edge technologies including programming, robotics, and artificial intelligence, giving them a head start in understanding and shaping technological advancements.

 – They also explore the ethical dilemmas surrounding technology and develop responsible innovators who can address challenges such as AI bias, data privacy, and the social impacts of automation. 

4. Interdisciplinary Thinking

 – The curriculum integrates diverse disciplines such as science, art, philosophy, and engineering, encouraging students to think across multiple disciplines. This holistic approach is vital for the Beta generation, who will likely work in a world where interdisciplinary skills are essential. 

5. Prepare for a rapidly changing future

 The age of AI requires agility and lifelong learning. Ad Astra fosters a mindset of curiosity and adaptability, helping students thrive in an environment of rapid technological and social change.

– The most key concept of Ad Astra school 

It seems that the most key concept and skill that is considered in ad Astra is synthesis.

This approach, where learners are placed in a situation where they solve complex problems with a set of information, ideas, and different disciplines, reflects the focus on the skill of synthesis in ad Astra School. As mentioned earlier, in such an institution, the main goal is to train and prepare future inventors and creative minds who can solve complex problems in the age of artificial intelligence, build bridges, learn digital marketing, and all this is done in an atmosphere of collaboration and work in small groups and exposed to different perspectives. 

Teachers’ role is to guide the flow of thought indirectly. They act as vigilant observers and try to pave the way for creativity and creative thinking, placing them in teamwork conditions in diverse contexts. In such a context, thinking shifts from preserving reserves to critical thinking and synthesis.

Such conditions in the era of artificial intelligence help solve life challenges and increase adaptability and flexibility.

Benjamin Bloom’s famous taxonomy of educational goals is considered in order from learning the reserves to the lowest level to the highest level, which is creative thinking.

This taxonomy gradually guides learners from simple educational categories to the most complex ones. Creative thinking is the same as synthesis, which combines information innovatively in new situations. In other words, solving problems for which there was no solution before and a skilled learner can create a solution. This skill is a step higher than the ability to analyze topics and problems.

Focusing on the concept and skill of ‘Synthesis’ enhances creativity and transforms learners into future inventors and innovators. Additionally, it helps them prepare for the fast-paced complexities of the technological world. In a world dominated by artificial intelligence, the ability to integrate knowledge with advanced technology is undoubtedly one of the most essential skills for the future generation and a defining characteristic of the Beta generation

Conclusion 

 As a pragmatic entrepreneur, Elon Musk has a unique and forward-thinking perspective on the future. This vision has led to the creation of an innovative educational institution. 

The structure and philosophy of this school break from traditional education and align with the fast-paced advancements of the artificial intelligence era. 

 The educational foundation behind this shift draws inspiration from the Montessori philosophy, blending it with modern technologies and teamwork-based learning. The school emphasizes creative skills and critical thinking, as outlined in Bloom’s taxonomy while fostering adaptability to meet the challenges of an AI-driven world. 

 There are even suggestions that this school model may expand further in the future. Musk may have new ideas for revolutionizing education in addition to his groundbreaking work in industry and technology. Only time will tell what impact these innovations might have.

Musk’s futuristic vision for education has recently taken another step forward with the approval of a new school charter in Bastrop County, Texas. 

The school is said to incorporate a Montessori-inspired approach, underlining Musk’s commitment to fostering innovative educational models. His efforts aim to equip future generations, particularly Gen Beta, with the skills and adaptability needed to thrive in an AI-driven era.

References

–https://www.kvue.com/article/news/local/elon-musks-ad-astra-montessori-school-permit-to-open-bastrop-county/269-22f51286-34cc-4349-9355-653f96910f65

–https://search.app/ZZmMGUcHSbeLbasm6

– https://search.app/EpmfUVQcM1D4LGde8

– https://search.app/z9WQumHedmnJm7JE7

– https://fortune.com/2024/11/20/elon-musk-ad-astra-school-permit-montessori-bastrop-texas/

– https://youtu.be/kuUG0pQmlwM?feature=shared

– https://www.reformaustin.org/education/elon-musks-ad-astra-school-gets-texas-green-light/

– https://youtu.be/lrBp5BL20Nw?feature=shared

– https://youtu.be/KbowJbyxn64?feature=shared

– https://www.entrepreneur.com/business-news/elon-musk-is-opening-a-school-called-ad-astra-how-to-get-in/484491

– https://www.businessinsider.com/elon-musk-opening-private-preschool-ad-astra-education-texas-trump-2025-1

– https://www.edweek.org/leadership/elon-musk-is-opening-a-school-for-young-students-heres-what-we-know-about-it/2024/11

– https://ntrs.nasa.gov/api/citations/19940029213/downloads/19940029213.pdf

 – https://symposium.org/wp-content/uploads/2024/04/Striving-For-More-or-Thriving-With-Less-%E2%80%94-What-We.pdf

– https://twentyonetoys.com/blogs/teaching-21st-century-skills/creative-genius-divergent-thinking?srsltid=AfmBOorjtHDw_j6JpzcXGf2s6ImvFwv_MqQUvalUTXUHk7aOjwpKjR1J

–https://www.forbes.com/councils/forbesbusinesscouncil/2022/12/09/how-to-unleash-your-creative-genius-at-work/

 – https://www.cwilsonmeloncelli.com/creativity-flow-states-nasa-study/

 – https://magazine.lucubrates.com/my-real-story-about-childhood-and-education/

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Shortly before 2024 and even in early 2024, the concern of many experts and developers was how to understand about correct use of AI tools and LLMs. Along with this global concern, others thought that this fast-paced technology was a great opportunity to establish startups and invest heavily in them.

Another group, mainly social theorists and humanities experts, tried to work on this question :

“How can humans interact with GPTs”, while their concern about the importance of human-AI interaction seemed reasonable, they raised questions about the mode of interaction, the type of interaction, and the emotional variables affecting human-technology interaction.

What was and is most remarkable among all was the unprecedented speed of the emergence and evolution of large language models. This speed exceeded all the questions and challenges considered by experts, to the point that at the end of the new year and the last days of 2024, raising better and more practical questions about how to guide the human thought process and guide the human user by LLMs in the form of a dynamic coexistence.

The evolution from “assistant” “to “mentor “

Predictably, 2025 will also time for significant developments in the dimension of dynamic interaction between human and LLMs.

As you have been seeing in 2024 varieties of large language Models emerged, providing significant assistance to humans, to the point that many articles and technology experts selected the names “assistant” and “partner” to large language models. However, at the same time, these services still included pre-programmed responses. In addition, despite the efforts of various businesses in producing AI startups around the world, mainly in the United States, China Japan, and some European countries, the focus of AI tools was on healthcare, education, finance, and consumer services. So, they seldom were used in other industries.

Nevertheless, the developments at the end of 2024 the quantitative growth of LLMs on the one hand, and the expected expectations of a qualitative leap on the other hand, strengthen the prediction of more interesting developments. In other words, we can now see artificial intelligence in the context of LLMs becoming more specialized and having more dynamic interactions with humans. Going beyond the education and healthcare industry and including other industries and areas.

Instead of predetermined and specific answers, it will have more multifaceted and broader answers to human needs and questions and will be more personalized than ever.
There’s, the word “mentor “can better describe the roles than assistant and partner!

Some of the reasons for changing the name of the LLM systems from “assistant “to “mentor “in the coming year are:

– Key Features of 2025 LLMs

1- Emotional understanding

In 2024, language models were based on text clues and tones, while in 2025, it seems that multi-analysis including text, audio, video, or even deeper emotional impressions will be possible.

2- Feedback style

In 2024, responses were “reactive “and based on immediate inputs and seemed to lack thought and cognition. In the coming year, feedback from large language models can be advanced and progressive and strengthen the development of the emotional intelligence of users. In other words, they will be more “reflective “rather than reactive.

3- Context awareness

You, as a user of LLM, have experienced that when you ask for specific information about an article or a researcher, it gives you a specific answer and this answer may not even be durable. That is, when you ask the same question again, it gives you a different answer. This indicates shallow and non-durable information. In next year’s language models, it is expected that the level and duration of the requested information will be slightly longer and that users will be given coherent information based on previous information history.

4- Role in Empathy

As mentioned earlier, large language models were passive information partners that provided predefined scenarios to users and applicants. The terms “partner” and “assistant” have been used many times to describe their role.
For example, sometimes ask them for information that we do not currently have and that we urgently need to perform a specific function. LLMs remind us of the answer in the fastest time from the information store and our “puzzle “is solved because a piece of the puzzle was not available in our short-term memory and we probably found that piece and solved the puzzle with the help of our super-advanced partner.

On the other hand, this process can be even more advanced, meaning that instead of passively calling out a predefined answer, a response based on the user’s emotional state and feelings is issued.

This is empathy because it can transform the role of the AI from a passive assistant who only calls out the missing link in the puzzle and solves the puzzle to the role of a “mentor” who guides the user towards the goal with empathy and emotional support.

5- Collaboration-based connection

Probably you have also had the experience of using AI tools to perform or complete a task such as composing a piece of music, writing a letter or a targeted request, or designing a specific decoration. It quickly answers each of your questions and at the end asks whether the information it has provided in this way was sufficient or not. If you ask for more information again, it will still be responsive like a hardworking and tireless assistant. If it does not know enough about your question or request, it will respond with an apology.

This is a collaborative approach to LLM systems. But recently, significant advances in AI systems promise new changes.

Therefore, it is possible and predictable that in the next few months, for example, request for help to complete a project, it will not only guide you to the end of the task but also witness the system’s effort to bond and cultivate long-term collaboration and build trust in you. This effort to encourage the user is evidence of the change in the position of artificial intelligence from “assistant” to “mentor”.
You are right, “the interaction between humans – and technology is becoming easier because this collaboration will change from a dry and reactive to a reflective state. Therefore, what started in the last months of 2024 will gain strength in 2025.

7- Application Areas

As mentioned above, the major industries that applied AI last year are healthcare, education, finance, and customer service. Among these, education and healthcare were and are ranked first. However, as users’ knowledge expands and AI’s capabilities increase in guiding personal and professional performance, it seems that more industries will accept the dominance of AI and apply these technologies as non-human assistants and even mentors.

Conclusion

Last year, the world of technology faced rapid developments in artificial intelligence systems, so that LLMs were nicknamed “human assistants” in many industries. Considering the developments of the last months and days of 2024, it is not far-fetched the majority of companies will accept applying these technologies, and even AI will become a “mentor” in the context of LLM. This mentor doesn’t mean to replace humans, but rather an intellectual technology that responds to users’ requests and can dynamically guide their performance.

This guidance seems to rely on two-way collaboration, emotional elements, and other components mentioned in the article. Undoubtedly, these are all personal predictions that have arisen from studying past trends and logic, and we need to see what will happen in the first months of 2025.

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