In an era marked by rapid advancements in artificial intelligence, systems have demonstrated remarkable capabilities in language comprehension, image recognition, and task execution. Yet, prevailing AI models predominantly depend on massive datasets and computational power, with cognitive frameworks rooted in symbolic or connectionist approaches, often characterized by static, non-developmental, and decontextualized limitations. These paradigms treat intelligence as a fixed representational system, overlooking the inherent “generative” and “growth-oriented” nature of human cognition. Within educational settings, this shortcoming becomes particularly evident, as existing AI struggles to replicate the incremental learning processes of children in authentic environments, lacking the ability to model learning trajectories, developmental stages, and social interactions. This gap hinders applications in personalized instruction, cognitive interventions, and developmental assessments, while also failing to address foundational questions in educational psychology about the mechanisms bridging perception and cognition. Consequently, equipping AI systems with developmental cognitive modeling capabilities has emerged as a pivotal challenge at the intersection of education and technology. Introducing the concept of “cognitive growth,” this article proposes a pathway grounded in embodied intelligence, synthesizing insights from developmental psychology, embodied cognition, and generative AI to forge a dynamic, context-aware, and interpretable cognitive modeling framework.
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Theoretical Foundations and Research Motivation
The genesis of human intelligence lies in early interactions with the world, encompassing not only language but also gaze, touch, movement, and the integration of multimodal environmental cues. Research indicates that infants build fundamental cognitive structures through these sensory and behavioral exchanges, a process that underscores the importance of embodied intelligence in shaping understanding.
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Piaget’s Cognitive Development Theory
Jean Piaget, in his observations of child development, posited that cognition is not a passive reception of information but an active process of construction, adjustment, and optimization. Intelligence, as an adaptive function, evolves through interactions between the individual and the environment, involving assimilation and accommodation to refine cognitive frameworks. Human cognitive development unfolds across four stages: the sensorimotor stage, pre-operational stage, concrete operational stage, and formal operational stage. The most foundational is the initial two-year sensorimotor stage, where infants, devoid of language and logical reasoning, establish basic comprehension of reality through direct engagement. Central to this phase is linking perception with action, transforming random movements into goal-oriented exploration. A key milestone is object permanence—the realization that objects continue to exist even when out of sight. In machine learning terms, this ability relates to memory construction and state inference; models equipped with object permanence can maintain continuity in dynamic scenes by retaining information about occluded objects, relying on short-term memory and spatiotemporal integration. Similarly, causal reasoning emerges as infants discern relationships between actions and outcomes, such as a toy ball rolling when pushed. Unlike machines that often rely on data patterns, human causal learning thrives on active exploration, suggesting that embodied robots must engage in hands-on experimentation to develop genuine causal cognition. Agency, or the sense of control over one’s environment, is another critical aspect, where infants learn that their actions influence the world. In robotics, fostering autonomy through exploration—allowing failures and strategy adjustments—is vital for adaptive learning, as it helps form internal body models and environmental mappings, reinforcing the connection between action and consequence. Translating Piaget’s theory into machine learning involves shifting from supervised data training to interactive knowledge discovery, where systems start with basic perception-action relationships and progressively build concepts like object permanence, causality, and agency through trial and error.
Embodied intelligence is central here, as it emphasizes that cognition is not confined to the brain but is deeply rooted in sensory, motor, and bodily experiences. Traditional AI often treats intelligence as a product of information processing, neglecting the structural role of the body. However, decision-making, reasoning, and abstract concept formation are intricately linked to action systems and physical feedback. For instance, walking relies on the synergy of body, gravity, and perception, rather than precise calculations, highlighting intelligence as an outcome of body-world coupling. This perspective advocates for a move from static data regression to interactive, goal-driven learning models in AI, where embodied robots engage in perception-action-feedback loops to generate realistic cognitive structures. James J. Gibson’s ecological perception theory, a precursor to embodied cognition, further argues that perception is not a passive representation but a dynamic interaction with the environment. The concept of “affordances” illustrates that an object’s meaning depends on the body’s structure and capabilities—for example, a chair “affords” sitting based on human anatomy and social practices. This shift from “brain-centric computation” to “body-world coordination” underscores that intelligent systems must actively explore to learn, as seen in developmental robotics, which aims to cultivate complex cognition through embodied interactions, starting with sensorimotor coordination and advancing to causal reasoning and symbolic expression.
The limitations of current machine learning underscore the need for embodied intelligence. Despite achievements in pattern recognition, AI often falters in causal reasoning, autonomous adaptation, and knowledge construction. Deep learning, based on statistical learning, excels at identifying input-output correlations through neural networks but lacks the active knowledge-building seen in humans, who generalize from few examples with structured understanding. This disparity highlights the divide between pattern matching and cognitive constructive learning. Symbolic AI, with its rigid rule-based systems, struggles in open environments, while connectionist AI, including deep learning, misses causal insights and environmental comprehension. In contrast, embodied intelligence posits that knowledge emerges from dynamic body-environment interactions, not static inputs. Human learning follows a progression from simple to complex—akin to mastering counting before calculus—suggesting that machines could benefit from a similar developmental sequence to enhance generalization and adaptability. Thus, integrating embodied intelligence into AI frameworks promises to address these shortcomings by fostering interactive, growth-oriented learning.
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Cognitive Growth Pathway
The cognitive growth pathway mimics the gradual development of human cognition, enabling machines to evolve from basic sensorimotor interactions to advanced reasoning capabilities. This approach is structured into core stages that reflect infant cognitive maturation, emphasizing the role of embodied intelligence in facilitating this progression.
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Framework Structure
The pathway comprises three pivotal stages, each aligning with human developmental phases. The first is the sensorimotor stage, where machine intelligence begins without abstract reasoning, relying solely on perception and movement to form environmental understanding through interaction. Similar to infants who learn via touch and gaze, machines in this phase establish perception-action mapping—adjusting actions based on sensory feedback through trial and error, such as learning to grasp objects without predefined rules. The second stage, goal-directed, focuses on causal reasoning and action selection oriented toward objectives. As infants increase autonomous exploration to test action-outcome relationships, machines can transcend passive pattern matching by actively constructing world models. The third stage, symbolic learning, involves forming higher-level concepts and abstract reasoning. Corresponding to human language acquisition, this phase allows machines to develop internal representations and generalize knowledge—for instance, recognizing “table” as a category beyond specific instances. The goal is conceptual generalization, enabling machines to derive universal principles from limited experiences, rather than task-specific matching. Overall, this pathway prioritizes active exploration and adaptation, fostering open, scalable knowledge structures that self-adjust through interaction, mirroring educational ideals where learning emerges from experience and reflection rather than rote instruction.
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Mechanisms of Cognitive Growth
For machines to genuinely “grow” in intelligence, learning must be a continuous, interactive process of knowledge construction. Human cognition begins with bodily试探 and feedback, not pre-set models, as highlighted by the sensorimotor contingency theory, where perception involves anticipating and controlling action outcomes. This has been applied in robotics; for example, self-perception systems enable embodied robots to distinguish “self” from “non-self” through perception-action-feedback loops, building world models and causal structures without supervised training. In developmental robotics, incremental learning simulates human progression, with models showing how reflexive behaviors evolve into stable skills like grasping, enhancing generalization. Staged learning—starting with simple tasks and escalating complexity—allows knowledge to accumulate progressively, avoiding restarts. However, growth requires balancing stability and plasticity; the brain adapts without forgetting, whereas AI often faces catastrophic forgetting, where new knowledge overwrites the old. Recent strategies, such as adaptive plasticity improvement, mitigate this by dynamically adjusting model flexibility, while active forgetting mechanisms and knowledge distillation maintain continuity without external storage. These approaches underscore that lifelong learning depends on preserving knowledge structures while remaining adaptable. By emulating human perception-action cycles, intelligent systems can employ active learning, progressive task building, and meta-learning to refine decisions and sustain sensitivity to new information, addressing generalization and long-term adaptation challenges. For education, this implies fostering dynamic cognitive construction through practice, not mere knowledge transfer.
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Computability of Cognitive Growth
Modeling the computability of cognitive growth is essential for advancing embodied intelligence, as it formalizes the transition from simple perception to complex reasoning. Early infant learning, characterized by perception-action-feedback loops, can be represented mathematically as a Markov Decision Process (MDP), where an agent perceives states, selects actions, observes outcomes, and optimizes strategies over time. This mirrors how infants build world models through activities like grasping and crawling, and has been implemented in robotics to simulate “from perception to cognition” pathways, using action feedback for environmental modeling. Theoretically, MDP-based cognitive systems model behavioral evolution under continuous stimuli, reinforcing their suitability for perception-action learning. Algorithmically, reinforcement learning and self-supervised learning form the foundation: reinforcement learning optimizes decisions through interaction, while self-supervised learning enables pattern discovery without labels, akin to infants learning object permanence through exploration. Attention mechanisms are crucial for filtering relevant information in complex environments; for instance, fusion-perception-to-action transformers use multi-scale visual attention to focus on key areas, improving perception and action strategies, while attention-augmented contrastive learning compresses representations to maintain performance with less data. Together, decision modeling (e.g., MDP), dynamic optimization (e.g., RL/SSL), and data streamlining (e.g., attention and compression) support the computability of cognitive growth, allowing machines to actively explore and form knowledge structures like humans, moving beyond passive learning limitations.
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Reflections and Insights
As the cognitive growth pathway is examined, critical questions arise about its potential to transcend current machine learning paradigms and bridge AI with human cognition. If machines can integrate perception-action cycles, developmental representation reorganization, and contextual meaning construction, this model could serve as a theoretical foundation for next-generation educational technologies, influencing intelligent system design, adaptive learning environments, and individual cognitive trajectory predictions. These considerations extend beyond technical aspects to core inquiries about the nature of intelligence itself.
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Comparison with Traditional Machine Learning
Traditional AI, particularly deep learning, relies on statistical learning, where neural networks are trained on vast datasets to map inputs to outputs, achieving breakthroughs in tasks like image recognition and natural language processing. However, this approach lacks active exploration, hindering adaptation to novel environments with limited experience. The cognitive growth pathway diverges fundamentally: instead of data-driven pattern fitting, it emphasizes interactive knowledge construction, enabling embodied intelligence to build hierarchical, adaptable cognitive structures through staged learning. This reduces dependence on large annotated datasets; humans generalize from few samples, whereas deep learning requires thousands, highlighting the contrast between pattern matching and cognitive constructive learning. Embodied robots, through exploration and few-shot self-construction, can develop transferable models, as validated in robotics research. Another distinction is interpretability: deep learning often operates as a “black box,” with opaque decision processes, whereas the cognitive growth pathway fosters causal reasoning and traceable inference paths through interaction, enhancing transparency and trustworthiness. This does not negate deep learning’s achievements but offers a complementary framework—prioritizing active exploration, knowledge building, causality, and explainability—that aligns more closely with human cognition, potentially guiding AI toward greater autonomy and contextual understanding.
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Educational Implications
The cognitive growth pathway provides a computable simulation platform for education research, bridging developmental psychology and AI. Developmental robot models can concretize constructivist theories, such as those of Piaget and Vygotsky, by simulating how children build knowledge through perception, attention, and imitation. These systems not only incorporate hierarchical cognitive mechanisms but also computationally model educational variables like motivation, feedback, and context, offering new tools for teaching mechanism studies. For instance, cognitive robot systems replicate infant language learning, where embodied attention facilitates word-object mapping, providing an explainable, debuggable experimental framework for educational process modeling. The pathway’s emphasis on intrinsic motivation mirrors the educational psychology concept of autonomy-driven learning; frameworks like autotelic reinforcement learning simulate student self-goal setting without external rewards, while socially guided intrinsic motivation systems model strategies balancing imitation and exploration. This informs educators that variables like novelty feedback and individual control can boost learner initiative. Additionally, the pathway’s knowledge construction mechanisms—where understanding emerges from perception-action loops—support “learning by doing” and constructivist curriculum design, emphasizing structural world comprehension over frequency-based induction. Early childhood studies show that learners acquire causality through unsupervised interaction, and developmental robotics models this as self-supervised causal learning, such as visual causal frameworks that establish light-switch relationships without labels. These models offer experimental paradigms for causal reasoning training and suggest “feedback-adjustment-construction” as a key instructional logic. Furthermore, the pathway provides controllable “virtual learner” platforms, like SEDRo environments that replicate infant development scenes with adjustable parameters, or social interaction modules simulating caregiver-infant dynamics. These tools support educational interventions, developmental assessments, and individualized teaching, especially in early childhood and special education, by enabling computational testing of cognitive theories. Ultimately, this cross-disciplinary approach links educational presuppositions with intelligent system modeling, advancing from descriptive to generative understanding and deepening the integration of education science with technological intelligence.
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Advantages and Limitations
The cognitive growth pathway, as a simulation of human cognitive development, prioritizes structured, growth-oriented, and explainable learning processes over mere performance optimization. Unlike statistical learning focused on efficiency and accuracy, this approach offers a methodological shift, providing new perspectives on machine learning through structural, staged, and active frameworks. A key advantage is its alignment with human cognitive mechanisms; traditional machine learning depends on fixed data inputs and training objectives, whereas the cognitive growth pathway introduces progressive learning from simple perception to reasoning, creating more rational and stable cognitive foundations. The emphasis on主动性 and environmental adaptability is another strength—most models require retraining for task or context changes, but this pathway encourages continuous试探 and strategy adjustment through interaction, driven by internal feedback rather than external labels. This endogenous mechanism reduces reliance on manual annotation and supports robust learning in open, dynamic settings. Additionally, the pathway’s structural explainability allows researchers to trace model state evolution across stages, facilitating diagnosis of learning bottlenecks, strategy optimization, and knowledge transfer, unlike the opaque intermediate layers of deep neural networks. However, limitations persist. Under current technological conditions, the pathway demands substantial computational resources, as it involves prolonged interaction, iterative training, and dynamic parameter adjustments in real-time environments, posing challenges for offline processing and requiring platforms with high concurrency and resource management. Theoretically, it faces insufficient mapping to psychological and physiological mechanisms; while inspired by human development, machine learning remains algorithm-based, lacking effective modeling of neural or biological structures. This gap impedes explanations of higher cognition, such as self-awareness, emotional regulation, or complex social behaviors, limiting the pathway’s ability to fully replicate biological cognitive processes. Despite these constraints, embodied intelligence’s cognitive growth pathway breaks from static AI simulations, highlighting intelligence’s generative, contextual, and developmental traits. It not only guides adaptive learning and cognitive evolution in AI systems but also serves as a theoretical instrument for education, connecting child cognitive mechanisms with AI implementation to model body-context-representation relationships. For teacher education and instructional design, it underscores that effective learning stems from “learning by doing” and “situated learning” interactions, positioning the pathway as a bridge between psychological theory and educational practice in fields like intelligent education, cognitive intervention, and teacher training, fostering deeper collaboration between educational psychology and AI.
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In summary, the cognitive growth pathway rooted in embodied intelligence represents a transformative approach to AI, emphasizing dynamic, interactive, and developmental learning processes. By integrating insights from cognitive science, developmental psychology, and machine learning, it addresses the limitations of traditional AI models and offers profound implications for education. As research progresses, this pathway could redefine how intelligent systems learn and adapt, ultimately enriching both technological and educational landscapes through a deeper understanding of embodied cognition and its applications.