Introduction

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

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

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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.

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