In the rapidly evolving landscape of artificial intelligence, the philosophy of language is encountering unprecedented opportunities and intricate challenges that prompt a fundamental reconsideration of linguistic theory and AI capabilities. Recent research integrating Noam Chomsky’s biolinguistic framework with Michael Tomasello’s social cognitive approach offers a nuanced multi-dimensional analysis of artificial intelligence language skills. At the heart of this endeavor lies the Embodied Constructional Cognitive Model (ECCM), a multi-tiered framework shedding light on AI’s strengths and inherent limitations in language acquisition, understanding, and creativity.
At the most foundational level, the nature of language itself is brought into sharp focus. The rationality manifest in AI-generated language is essentially derivative, rooted firmly in human-originated creative processes rather than in genuine original thought. While generative AI systems boast data processing speeds and computational efficiency unmatched by humans, this computational prowess does not equate to original rational reasoning. Unlike human cognition, which is marked by inventive analogy and originality, AI’s inferential capabilities are bound by the parameters of its programming and training data. The ECCM particularly emphasizes this distinction by identifying the absence of analogical reasoning and embodied linguistic experience in AI, which are crucial for authentic linguistic creativity.
Language understanding and processing further reveal substantial gaps between artificial agents and human interlocutors. Advances in AI-generated content (AIGC) have improved surface-level language comprehension, producing text with remarkable accuracy and fluency that often mimics human expression convincingly. However, deep linguistic and contextual understanding—especially dimensions involving moral values, social norms, and ethical implications—remains elusive. AI operates on algorithmic logic which, by nature, lacks genuine awareness of cultural nuance and shared intentionality essential for meaningful communication. The ECCM’s Social Interactional Level (SIL) underscores this deficiency, illustrating how AI systems are incapable of grasping language as a vessel of normative social engagement, thus limiting their ability to participate effectively in culturally embedded discourse.
One of the most intriguing yet paradoxical contemporary findings is AI’s proficiency in emotional interaction. Studies have demonstrated that AI-generated messages can make human recipients feel more understood and emotionally supported than messages from other humans. This phenomenon may be elucidated by the form-meaning interface in the ECCM, which allows AI to replicate formal linguistic patterns and emotional markers at a superficial level. Despite this, AI’s lack of shared intentionality at the SIL means that this emotional responsiveness is mimetic rather than empathetic. It simulates empathy without the experiential substrate, leading to interactions that can appear genuine yet devoid of authentic social understanding.
From a cognitive complexity standpoint, AI has emerged as a powerful tool for analyzing and integrating multi-modal data—from textual corpora and audio to visual stimuli—offering capabilities that greatly surpass human memory and processing speed. Nevertheless, its prowess is mainly confined to formal linguistic competencies, such as syntax and pattern recognition. Functional language abilities that require contextual inference, creativity, and embodied understanding remain markedly underdeveloped. This deficit is directly linked to the lack of corporeal and social dimensions of cognition outlined in the ECCM’s Real-World Interactional Level (RIL) and SIL, wherein lived bodily experience and social interaction shape meaningful language use.
The philosophical ramifications of these technological advances call for a reexamination of the relationship between AI development and human values. Although science progresses through specialization and methodical expansion, holistic comprehension of language and cognition requires philosophical reflection. This reflection is vital not only for guiding AI innovations but also for ensuring their ethical integration into society. The fusion of Chomskyan and Tomasello’s perspectives within the ECCM foregrounds how moral emotions and unique human social motivations form the bedrock of language as a bearer of shared cultural values. AI, devoid of these core attributes, faces inherent challenges in navigating value-laden language tasks.
Moreover, uncertainties inherent to AI systems—such as black-box decision-making, uncontrollability, and emergent complexity—underscore the urgency of integrating ethical considerations into AIGC research and deployment. These systems do not merely relay information but propagate cultural memory and influence social norms. Any misalignment or error in this transmission risks exacerbating cultural fragmentation or ethical breaches. This critical intersection between technology and philosophy highlights the indispensability of value-driven governance and the continuous engagement of philosophical inquiry.
While AI’s role in language processing often appears revolutionary, it is fundamentally transformative only when contextualized within the embodied and socially situated nature of human language. The ECCM’s layered approach reveals that beneath AI’s surface fluency lies an absence of authentic embodiment—lack of sensory-motor experience and a social life that human language emerges from. This insight calls into question claims of AI achieving true linguistic creativity, as the generative capacities of AI do not emerge from lived experience or intentional social engagement but from computational manipulation of symbolic input.
The implications extend to how AI-generated content shapes and recreates culture. Training AI on vast corpora of human-generated texts inevitably replicates cultural biases and normative frameworks. Without a mechanism for autonomous cultural innovation or ethical reflection, AI models risk ossifying existing inequalities or perpetuating misunderstandings. This conundrum aligns with criticisms that current AI systems are fundamentally limited by their reliance on historical and cultural data, lacking autonomous normative reasoning capacities evident in human language communities.
Importantly, human language is not a mere technical instrument for transmitting data but a deeply intertwined cognitive and social phenomenon. Through embodied cognition and shared intentionality, language becomes a medium for constructing reality, negotiating social norms, and communicating values. These dimensions are integral to the ECCM and serve as a yardstick for evaluating AI’s linguistic competence beyond syntactic proficiency. AI’s failure to “live” language in these terms restricts its effectiveness as a fully autonomous language-being and instead positions it as a quasi-social tool.
The nuances of AI’s apparent emotional intelligence—a frontier often touted as a breakthrough—deserve critical scrutiny. While some AI models excel at detecting emotional cues and responding in socially attuned ways, this proficiency is algorithmic and lacks genuine affective experience. As per the ECCM framework, the difference between simulated social-emotional interaction and embodied social cognition is profound. Without embedded social motivation and moral engagement, AI cannot fully partake in the ethical dimensions that human dialogues entail.
Finally, this evolving understanding directs attention to future research priorities. Enhancing AI’s language capacities requires integrated approaches that accommodate not only syntactic and semantic features but also embodied engagement, analogous reasoning, and social intentionality. Interdisciplinary efforts spanning linguistics, cognitive science, philosophy, and AI are essential to navigate this complex terrain. As AI continues to intertwine with socio-cultural fabrics, fostering models that respect and reflect human values becomes paramount to ensuring beneficial technological co-evolution.
In conclusion, the ECCM provides a comprehensive conceptual lens to critically assess AI’s linguistic capabilities. It elucidates the multi-level nature of language cognition—highlighting areas where AI excels and where it fundamentally falls short. AI’s current proficiency is remarkable, yet its underlying lack of original rationality, embodied experience, and social intentionality signifies intrinsic limitations. Addressing these gaps remains a central challenge as AI endeavors to participate in the human linguistic and cultural ecosystem without undermining the ethical and creative essence that defines human language.
Subject of Research: Philosophical and cognitive analysis of AI language capabilities through biolinguistic and social cognitive frameworks using the Embodied Constructional Cognitive Model (ECCM).
Article Title: Beyond binary opposition: philosophical reflections on a multi-level Language cognitive model from an embodied constructional perspective
Article References:
Wu, J., Cheng, L. Beyond binary opposition: philosophical reflections on a multi-level Language cognitive model from an embodied constructional perspective.
Humanit Soc Sci Commun 12, 1495 (2025). https://doi.org/10.1057/s41599-025-05690-2
Image Credits: AI Generated
