Generative AI is more than a technological advancement—it represents a profound shift in how meaning is created, shared, and understood across diverse contexts. As a tool capable of generating new content across various modalities, including text, images, and audio, generative AI doesn’t just provide access to information; it reshapes communication in an increasingly individualized and globalized world. This transformation brings with it both opportunities and challenges, especially in how we understand meaning and semantic structures across different languages and symbolic systems. As societies diversify, generative AI becomes essential, not merely a convenience, for navigating the complexities of communication. In this article, I outline some of the thinking in my forthcoming book Emergent Discourses in Generative AI in Education Society, which is expected to be published by Cambridge University Press in 2025.

Facilitating communication in a globalized, individualized world

Generative AI addresses a critical challenge of modern communication: how to navigate meaning across cultural, social, and linguistic divides. In an era where globalization and digital connectivity have connected people from vastly different backgrounds, the ability to communicate across contexts has become more difficult but increasingly necessary. Generative AI’s core function—modeling meaning across diverse contexts—makes it uniquely suited to bridge this gap.

In education, for instance, AI’s capacity for personalization enables it to generate content that aligns with individual learning styles, cognitive abilities, and cultural backgrounds. This helps learners engage with material that resonates with their specific needs, breaking down barriers to understanding that might arise from a one-size-fits-all approach to education. Similarly, in professional or social settings, generative AI can translate not just language but also meaning, facilitating clearer communication in cross-cultural collaborations.

This adaptability makes generative AI indispensable in a world where communication must be flexible enough to meet the demands of a diverse, globalized society. By tailoring content to the specific contexts in which it is consumed, AI ensures that communication remains fluid, relevant, and accessible, regardless of the complexity or diversity of the audience.

Modeling meaning and semantic structures across languages and symbolic systems

One of the most powerful features of generative AI is its ability to model meaning and semantic structures across languages and symbolic systems. AI systems learn by identifying patterns in how words, phrases, images, and symbols are used in different contexts. Through this process, AI develops an understanding of the relationships between symbols and the meanings they convey, creating a flexible model of meaning that can adapt across various contexts.

For example, in text-based communication, AI can recognize that certain words or phrases often appear together in specific contexts, helping it predict how language is typically structured. This capability extends beyond single languages. AI models trained on multilingual datasets can learn equivalent meanings in different languages, allowing AI to generate translations that capture not just the literal meaning but the contextual significance of phrases. This ability to map semantic structures across languages makes AI particularly effective in facilitating global communication, where linguistic and cultural barriers often impede understanding.

Moreover, generative AI extends this capacity to other symbolic systems, such as images, music, and even mathematical notation and computer code. For example, AI can take a textual description and generate a text that expands, explains, and exemplifies the text in greater detail and produces images that visually represent the same idea, preserving the meaning across different textual forms and modalities. This process, known as semantic transduction, allows AI to convert input from one form into another while maintaining the core meaning.

This capacity to navigate and transduce meaning across and within languages is invaluable in our increasingly interconnected world. Whether in education, business, or social communication, AI's ability to work across symbolic systems makes it a versatile tool for bridging gaps in understanding and facilitating more inclusive, adaptive interactions.

Addressing complexity and introducing new complexities

To fully appreciate generative AI’s role in communication, it’s helpful to turn to the work of sociologist Niklas Luhmann, who posited that all technologies address problems of complexity, but in doing so, they inevitably introduce new forms of complexity. Generative AI is no exception. By helping to manage the complexity of modern communication—especially in a diverse, globalized world—AI resolves certain issues, such as the need for personalization and adaptability, but also creates new challenges, like bias, ethical dilemmas, and the potential for miscommunication.

However, Luhmann’s theory reframes these challenges not as insurmountable problems but as the natural consequence of technological innovation. Complexity is a feature, not a flaw, of any meaningful technological innovation. As AI becomes embedded in communication systems, it will inevitably change how we understand and manage meaning. While these shifts may introduce new challenges, they also provide unprecedented opportunities for navigating the complexities of modern life in ways that were previously unimaginable.

Generative AI’s ability to model meaning across languages and symbolic systems exemplifies this dynamic. The AI simplifies communication by transducing meaning across contexts and forms, but it also introduces the challenge of ensuring that these transductions remain accurate, relevant, and culturally sensitive. Despite these challenges, the benefits of AI’s ability to facilitate communication across diverse contexts far outweigh the risks, particularly when viewed through Luhmann’s lens of technological progress.

Semantic transduction: the bridge between meaning and modality

Semantic transduction is a key feature of generative AI that allows it to bridge the gap between different forms of expression. This process involves taking input in one modality (e.g., text) and generating output in another (e.g., images or sound) while preserving the original meaning. By doing so, AI enables communication across multiple sensory and symbolic systems, making it easier for people to engage with content in ways that suit their individual preferences and needs.

For example, AI can take a simple textual description, such as "A cat is sitting on a sunny windowsill," and generate:

  • Detailed output: "The cat, with soft fur and gleaming eyes, lounges comfortably on the windowsill, basking in the warmth of the midday sun as it lazily observes birds fluttering outside."

  • Narrative output: "On a quiet afternoon, a cat settled on a sunlit windowsill, its body stretching out as it soaked in the golden light. Outside, the world moved at a slow pace, with birds flitting by, unnoticed by most but intently watched by the cat."

  • Metaphorical output: "Like a silent sentinel of peace, the cat rested on the windowsill, a symbol of tranquillity, while the sunlight poured over it like a warm embrace, and the distant birds hinted at a freedom just beyond reach."

  • Poetic output: "A sunbeam cradles the feline form,
    Eyes that flicker with distant skies,
    The windowsill, a perch to warm,
    Where time and wings together fly."

  • Descriptive output for instructions: "A tabby cat, curled up on a sun-washed windowsill, sits upright with its eyes following birds darting across the sky. The glass pane reflects the serene scene, while the cat remains still, intent on the world outside."

In each example, AI adapts the original text into various forms of written expression, expanding, narrating, or transforming the description to suit different styles, purposes, or emotional tones. This versatility illustrates the capacity of AI to handle different types of text outputs from the same initial input while still maintaining or expanding on the original meaning.

Each of these outputs reflects the same underlying meaning but presents it in different forms. This ability to transduce meaning across modalities is particularly valuable in education, where students may benefit from engaging with content through multiple sensory pathways. It also supports accessibility, providing alternative formats for learners with disabilities or those who may struggle with traditional forms of communication.

Generative AI offers powerful potential for transcending semantic boundaries, making it an essential tool in education and societal communication. By transforming text into various forms—detailed explanations, simplified narratives, metaphorical interpretations—AI enables users to tailor communication to diverse audiences and contexts. This ability to transduce meaning across different contexts and modalities helps bridge linguistic, cultural, and knowledge gaps.

In education, AI can personalize learning by adapting content to individual cognitive styles or simplifying complex material for beginners while offering advanced insights to experienced learners. For multilingual classrooms or cross-disciplinary studies, AI can be used to provide adapted content that accommodates varying levels of understanding, fostering collaboration and critical thinking.

In societal communication, AI helps translate technical information into accessible language, ensuring that diverse audiences can comprehend essential messages. It also allows for more inclusive communication by adapting content for different literacy levels, languages, or cultural contexts. Whether it’s simplifying government communications, enhancing public engagement in museums, or facilitating global collaboration, generative AI empowers users to transcend semantic barriers, making communication more relevant, adaptive, and inclusive in our increasingly complex world.

Ethical considerations and bias in AI-generated content

While the potential for generative AI to enhance communication across languages and modalities is clear, its ability to model meaning also introduces ethical considerations, particularly around bias. AI systems are trained on vast datasets that often reflect societal inequalities, which means that AI-generated content may unintentionally perpetuate or amplify these biases. In educational settings, for example, biased AI-generated material could reinforce harmful stereotypes or exclude certain perspectives, undermining the inclusive potential of the technology.

However, addressing these concerns involves not rejecting AI but refining its design and implementation. Transparency in how AI models are trained, coupled with efforts to ensure diverse and representative datasets, can help mitigate these risks. By recognizing that all technologies introduce new complexities, we can approach generative AI with a mindset that embraces its potential while remaining vigilant about its limitations.

A necessary evolution in communication

In a world characterized by increasing cultural diversity, linguistic variety, and individualized learning, generative AI is not just a convenience—it is a necessity. The complexity of modern communication, particularly in globalized contexts, cannot be addressed by static or conventional methods. AI’s ability to dynamically adapt meaning across contexts makes it a powerful tool for navigating this complexity. While challenges such as bias and ethical dilemmas exist, they should not overshadow the broader benefits that generative AI provides in facilitating more inclusive, adaptive, and context-sensitive communication.

As Luhmann suggests, all technological advancements introduce new complexities, but these complexities are a necessary part of progress. By helping us manage the intricacies of modern communication, generative AI plays a crucial role in our ability to engage with the world’s diversity. The task is not to eliminate these challenges but to navigate them thoughtfully, ensuring that AI’s potential is harnessed to enhance communication rather than hinder it (Emergent discourses in...).

Conclusion: generative AI as a catalyst for meaningful communication

Generative AI represents a pivotal shift in how we communicate and understand meaning across diverse contexts. Its ability to model semantic structures across languages and symbolic systems makes it an essential tool for navigating the complexities of modern communication. By facilitating meaning-making across modalities and contexts, AI enables more inclusive, flexible, and adaptive interactions, whether in education, business, or everyday life.

As we continue to integrate AI into our communication systems, the focus should not be on avoiding the new complexities it introduces but on embracing them as part of technological evolution. Generative AI, with all its inherent challenges, is ultimately a necessity for fostering meaningful communication in an increasingly interconnected and diverse world.