Throughout the history of science, it has rarely been the case that any phenomenon has remained permanent and unchanging. Theories, approaches, research methods, philosophies, and everything related to scientific perspectives have continually evolved. These changes have been adaptive and have moved toward improving human living conditions. If science is meant to serve humanity, it follows that whenever a tool fails—for whatever reason—to fulfill this responsibility effectively, it must either change or, over time and under changing circumstances, be updated into a more efficient version.
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.
