In recent years, the field of artificial intelligence has made significant strides in areas such as language understanding, image recognition, and task execution. However, mainstream AI models still rely heavily on large-scale data and computational power, often based on symbolic or connectionist paradigms, which exhibit limitations like static structures, non-developmental approaches, and decontextualized reasoning. These models treat intelligence as a fixed reproduction system, overlooking the generative and growth-oriented nature of human cognition. In educational contexts, this gap becomes particularly evident, as current AI systems struggle to simulate the gradual learning processes of children in real-world situations, lacking the ability to model learning pathways, developmental stages, and social interactions. This not only restricts their potential in personalized teaching, cognitive intervention, and developmental assessment but also fails to address the long-standing questions in educational psychology about the mechanisms of constructing cognition from perception. Therefore, enabling AI systems to possess developmental cognitive modeling capabilities has become a core challenge in integrating education and intelligent technology. In this article, we introduce the concept of “cognitive growth” and propose a pathway based on embodied intelligence, which integrates interdisciplinary insights from developmental psychology, embodied cognition, and generative AI to build a dynamic, contextualized, and interpretable cognitive modeling framework.
The theoretical foundations of this approach are rooted in the idea that cognitive abilities do not emerge from abstract computation alone but develop through interactions between the body, environment, and actions. Drawing from cognitive science, developmental psychology, and AI perspectives, we outline typical stages of cognitive growth, including bodily regulation, object construction, intention simulation, and inferential reasoning, and analyze supporting mechanisms such as embodied regulation, situational construction, and representational reorganization. We argue that these mechanisms hold potential for simulation in artificial systems, providing theoretical connections for cross-disciplinary research. Moreover, we explore the implications of this embodied cognitive growth path for educational psychology research, children’s cognitive development assessment, and teacher professional learning, emphasizing its role as a tool for understanding cognitive differences and supporting personalized instruction.
To begin, let us delve into the theoretical underpinnings and motivations for this approach. Human intelligence originates from early interactions with the world, which encompass not only language but also multimodal information integration involving gaze, touch, movement, and the environment. Research shows that infants build basic cognitive structures through these sensory and behavioral interactions. For instance, Piaget’s theory of cognitive development highlights that cognition is not passively acquired but actively constructed, adjusted, and optimized through interactions with the environment. Intelligence, as an adaptation, evolves from the interplay between the individual and their surroundings, involving processes like assimilation and accommodation to refine cognitive structures. Human cognitive development can be divided into stages such as the sensorimotor, pre-operational, concrete operational, and formal operational stages, with the sensorimotor stage in the first two years of life being particularly crucial. During this phase, infants lack language and logical reasoning but gradually develop an understanding of reality through direct interactions, where intelligence emerges from exploration. Key achievements in this stage include object permanence—the realization that objects continue to exist even when out of sight—and causal reasoning, where infants learn that actions have consequences, such as pushing a toy to make it roll. This active exploration fosters a sense of agency, the awareness that one’s actions can influence the world, which is fundamental to autonomous learning. Mapping this to machine learning, we advocate for a shift from data-driven supervised learning to interactive, exploratory knowledge discovery, where embodied robot systems learn through trial and error, much like infants.
Embodied cognition theory further reinforces this perspective, positing that cognition is not confined to the brain but is grounded in sensory, motor, and bodily experiences. Traditional AI often views intelligence as a product of information processing systems, neglecting the structural role of the body in cognitive formation. In contrast, embodied intelligence emphasizes that smart systems arise from the continuous coupling between organisms and their environments, where knowledge is generated through perception-action-feedback loops. For example, human walking relies on coordination between the body, gravity, and perception, rather than precise calculations. This view, supported by Gibson’s ecological perception theory and the concept of affordances, suggests that perception is not a passive representation but a dynamic interaction where meaning is defined by the body’s structure and capabilities. In embodied robot applications, this implies that machines should not passively process data but actively engage with their surroundings to develop cognitive abilities. Developmental robotics, inspired by infant learning, aims to create systems that progress from basic sensorimotor coordination to complex skills like causal inference and social understanding, rather than relying solely on large-scale data training.
However, current machine learning approaches face significant limitations. Deep learning, based on statistical learning through neural networks, excels at pattern recognition but struggles with causal reasoning, adaptation to new contexts, and knowledge construction. These systems often fall into the trap of correlational thinking, lacking stable causal structures and behavioral adaptability. For instance, while humans can learn from few examples and generalize across situations, AI models require massive labeled datasets and are prone to catastrophic forgetting—where new knowledge overwrites old ones. This highlights the need for a paradigm shift toward embodied, interactive learning mechanisms that mimic human cognitive development.
To address these challenges, we propose a cognitive growth path that simulates the progressive development of human cognition, enabling machines to evolve from basic sensorimotor interactions to advanced reasoning. This path consists of three core stages, as summarized in Table 1.
| Stage | Description | Key Features | Examples in Embodied Robot Applications |
|---|---|---|---|
| Sensorimotor Stage | Initial phase where machines interact with the environment through perception and action, forming basic understandings without abstract reasoning. | Perception-action mapping, object permanence, causal exploration. | Robots learning to grasp objects via trial and error, developing internal models through feedback. |
| Goal-Directed Stage | Machines begin to understand causality and orient actions toward specific goals, engaging in active exploration. | Causal reasoning, target-oriented behavior, adaptive strategy selection. | Embodied robots testing actions to verify effects, such as pushing buttons to observe outcomes. |
| Symbolic Learning Stage | Advanced phase where machines form abstract concepts and perform higher-level reasoning, enabling generalization. | Concept formation, symbolic representation, inferential capabilities. | Robots categorizing objects (e.g., “table” across variations) and applying knowledge to new contexts. |
The sensorimotor stage serves as the foundation, where embodied robot systems establish perception-action mappings through iterative interactions. For example, a robot might learn to stabilize its grip on objects by experimenting with different movements and adjusting based on sensory feedback, akin to an infant’s exploratory touches. This stage emphasizes the development of object permanence and basic causal awareness, which can be modeled computationally using reinforcement learning frameworks. The goal-directed stage builds on this by fostering causal reasoning, where machines actively test hypotheses about their environment. In embodied robot setups, this could involve manipulating tools to see cause-effect relationships, supported by intrinsic motivation mechanisms that drive curiosity-driven exploration. Finally, the symbolic learning stage enables abstraction, where machines internalize concepts and reason symbolically, similar to how children learn language and generalize categories. This progression ensures that knowledge is constructed incrementally, reducing reliance on massive datasets and enhancing adaptability.
Underpinning this cognitive growth path are several mechanisms that facilitate learning and development. One key mechanism is embodied regulation, which involves the dynamic adjustment of actions based on sensory feedback. This can be formalized using mathematical models such as Markov Decision Processes (MDPs), where an embodied robot interacts with its environment by perceiving states, taking actions, and receiving rewards. The state transition probability in an MDP can be represented as:
$$ P(s’ | s, a) $$
where \( s \) is the current state, \( a \) is the action taken, and \( s’ \) is the resulting state. This framework captures the perception-action loop, allowing robots to optimize policies over time through algorithms like Q-learning, which updates action values based on rewards:
$$ Q(s, a) \leftarrow Q(s, a) + \alpha [r + \gamma \max_{a’} Q(s’, a’) – Q(s, a)] $$
Here, \( \alpha \) is the learning rate, \( r \) is the reward, and \( \gamma \) is the discount factor. Such models enable embodied robot systems to learn from interactions, gradually building world models and causal structures.
Another mechanism is situational construction, where the environment is structured to support progressive learning. This involves designing tasks that increase in complexity, allowing machines to scaffold knowledge. For instance, in embodied robot training, starting with simple object manipulation and advancing to multi-step problems helps prevent overwhelm and promotes skill retention. This aligns with the concept of “plasticity-stability trade-off,” where systems must balance learning new information with retaining old knowledge. Techniques like elastic weight consolidation (EWC) can mitigate catastrophic forgetting by penalizing changes to important parameters:
$$ L(\theta) = L_{\text{new}}(\theta) + \lambda \sum_i F_i (\theta_i – \theta_{\text{old}, i})^2 $$
where \( L(\theta) \) is the total loss, \( L_{\text{new}} \) is the loss on new data, \( \lambda \) is a regularization parameter, \( F_i \) represents the importance of parameter \( i \), and \( \theta_{\text{old}} \) are the parameters from previous tasks. This ensures that embodied robot systems maintain long-term knowledge while adapting to new experiences.
Representational reorganization is a third mechanism, where internal models are refined through self-supervised learning and attention mechanisms. For example, transformers with multi-scale visual fusion can help embodied robots focus on relevant perceptual cues, improving efficiency. The attention mechanism can be expressed as:
$$ \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right) V $$
where \( Q \), \( K \), and \( V \) are query, key, and value matrices, and \( d_k \) is the dimensionality. This allows robots to compress sensory data into compact representations, reducing cognitive load and enhancing generalization. Together, these mechanisms form a computational framework for cognitive growth, as illustrated in Figure 1, which integrates decision modeling, dynamic optimization, and data refinement to support active learning in embodied robot systems.
The computability of this cognitive growth path relies on integrating these elements into a cohesive system. For instance, reinforcement learning and self-supervised learning provide the algorithmic basis for autonomous exploration and pattern discovery. In embodied robot applications, this might involve training robots to perform tasks like navigation or object sorting without explicit labels, using algorithms that maximize intrinsic rewards for novelty. Additionally, attention mechanisms filter redundant information, enabling robots to prioritize critical stimuli—a capability essential for real-world environments. Empirical studies have shown that such approaches lead to robust performance in dynamic settings, with embodied robots demonstrating improved causal inference and adaptive behavior compared to traditional models.
When comparing this cognitive growth path to traditional machine learning, several distinctions emerge. Conventional deep learning depends on large-scale labeled datasets and statistical pattern matching, resulting in systems that are data-hungry and lack interpretability. In contrast, the embodied robot approach emphasizes active knowledge construction through environmental interactions, reducing data dependency and enhancing causal reasoning. For example, while a standard AI model might require thousands of images to recognize objects, an embodied robot could learn from a handful of interactions by physically manipulating items. This shift from pattern matching to cognitive constructive learning fosters greater transparency, as the decision-making process in embodied robots can be traced through their action sequences and feedback loops. Moreover, this path addresses the black-box problem in AI by providing explainable models where reasoning steps are observable, which is crucial for applications in education and safety-critical domains.
The educational implications of this framework are profound. By simulating human cognitive development, the cognitive growth path offers a computable platform for studying learning processes. For instance, developmental robot models can embody constructivist theories, such as those by Piaget and Vygotsky, by replicating how children build knowledge through perception, attention, and imitation. In practice, this could inform the design of adaptive educational technologies that tailor instruction to individual learning trajectories. For example, embodied robot systems can be used to model student behaviors in virtual environments, allowing educators to test interventions for cognitive development or special needs. Additionally, the emphasis on intrinsic motivation in this path aligns with educational psychology principles, suggesting that learning is more effective when driven by curiosity and autonomy rather than external rewards. This could lead to innovative teaching strategies that incorporate exploratory activities and real-world problem-solving, fostering deeper understanding and retention.
Table 2 summarizes key educational applications and benefits of integrating embodied robot systems into learning environments.
| Application Area | Description | Potential Impact |
|---|---|---|
| Personalized Learning | Using embodied robots to simulate student cognitive paths and adapt content based on developmental stages. | Enhances engagement and effectiveness by aligning instruction with individual growth rates. |
| Cognitive Development Assessment | Leveraging robot models to evaluate children’s reasoning skills, such as object permanence or causal inference. | Provides objective, scalable tools for early intervention and support. |
| Teacher Professional Development | Training educators with simulated embodied robot scenarios to understand cognitive variability and instructional strategies. | Improves teaching practices through data-driven insights into learning mechanisms. |
| Special Education | Designing interactive embodied robot systems to support learners with disabilities by modeling adaptive behaviors. | Offers tailored interventions that promote inclusion and skill acquisition. |
Despite its advantages, the cognitive growth path has limitations. Computationally, it demands significant resources for real-time interactions and continuous learning, which can be prohibitive for widespread deployment. Moreover, while it simulates functional aspects of cognition, it does not fully capture the biological or neural underpinnings of human intelligence, limiting its applicability to higher-order processes like emotion or social cognition. Additionally, designing embodied robot systems that seamlessly integrate with diverse environments remains a challenge, requiring advances in hardware and software coordination. However, these limitations also present opportunities for future research, such as developing more efficient algorithms or hybrid models that combine embodied approaches with neural insights.
In conclusion, the embodied intelligence cognitive growth path represents a transformative approach to AI and education, emphasizing dynamic, situated, and developmental learning. By drawing on interdisciplinary theories and computational models, it provides a framework for building systems that learn and adapt like humans, with significant implications for personalized education, cognitive assessment, and teacher development. As we continue to refine this path, embodied robot systems will play an increasingly vital role in bridging the gap between artificial and human intelligence, fostering innovations that enhance learning and understanding across diverse contexts.