The Mutual Empowerment of Artificial Intelligence and Humanities

Generative artificial intelligence is profoundly changing various fields such as education, employment, entertainment, healthcare, transportation, and elder care, becoming a hot topic. The relationship between the humanities and generative AI is complex and symbiotic. AI is reshaping the forms and future development paths of the humanities, while the demands of AI development highlight the value and functions of the humanities. In this sense, the development of the humanities will fundamentally influence the cognitive heights and social acceptance of AI.

Bridging Humanities Scholars to Multidisciplinary Fields

As modern disciplines become increasingly specialized, the barriers between the humanities and natural sciences, as well as between the humanities and social sciences, are widening, potentially leading to a “knowledge dilemma.” It is challenging to find scholars within the humanities who can bridge literature, art, philosophy, history, and language, resulting in a limitation of “partial profundity” in contemporary humanities. The emergence of AI can provide new solutions to this issue.

Large language models are constructed through deep learning on vast amounts of text, forming a distributed representation system of language and knowledge, highly condensing human written knowledge. Driven by neural network architectures and probabilistic predictions, these models achieve context awareness and human-like logical reasoning under specific prompts, facilitating knowledge output. In this sense, AI can serve as a powerful assistant for humanities scholars, bridging them to multidisciplinary fields and empowering the production of humanistic knowledge through information search, literature screening, semantic analysis, and cross-disciplinary integration.

Currently, the influential “distant reading” method, based on the overall framework of world literature, utilizes AI models to establish interdisciplinary literary criticism and research models. Unlike traditional literary studies that advocate close reading of a few classics, distant reading involves data mining and quantitative analysis of large-scale text collections, systematically revealing the themes, emotional tendencies, plot structures, and linguistic features contained within, thus macro-describing the overall development of human literature. This effectively addresses the technical challenges of processing massive texts and the cross-cultural and interdisciplinary knowledge issues that qualitative analyses in traditional literary history and world literature studies cannot solve.

Updating Methods and Paradigms in the Humanities

China has a long and rich tradition in the humanities, but the concept of “humanities” emerged in the twentieth century. During the Enlightenment in the West, humanities scholars sought to find their unique nature and methods outside of the natural sciences. They viewed the humanities as a “new science” concerning human thoughts and behaviors, distinct from natural sciences, emphasizing the use of “individualized methods” related to values to construct the epistemology and methodology of the humanities.

Overall, within this logic, criticized later as the “spirit-nature dichotomy,” the humanities emphasize “thought of existence,” studying objects that exist in symbolic forms such as language, text, images, and rituals, involving beliefs, conscience, emotions, aesthetics, values, and ideals—elements of spiritual culture that are difficult to quantify. This encompasses deep individual psychology, instincts, consciousness and unconsciousness, as well as historical cultural memory and collective unconsciousness, possessing inherent qualities of value, culture, individuality, spirituality, emotion, thought, and symbolism that are inseparable from humanity. Methodologically, the humanities focus on internalized approaches such as empathetic understanding, contemplative experience, and intuitive insight, aiming to reveal unique individual experiences, complex spiritual worlds, and deep cultural meanings that cannot be captured by the replicable, quantifiable, and verifiable techniques of natural sciences.

As disciplines continue to evolve, this binary oppositional thinking model is also being continually reflected upon. Marx stated, “Natural science will eventually include the science of man, just as the science of man includes natural science: this will be one science.” Emerging digital humanities research not only deeply examines the humanistic concerns and governance challenges brought by digital technology but also actively explores new research methods and paradigms from digital technology, reshaping the landscape of humanistic research. Various literary laboratories and beneficial attempts at quantitative humanities research are continually emerging. AI has evolved from an auxiliary tool to a key force driving paradigm innovation, providing humanities scholars with new interdisciplinary research perspectives and theoretical innovation support, greatly expanding the breadth and depth of humanistic research experiences.

Enhancing Critical Thinking and Writing Skills through Human-Machine Collaboration

A unique aspect of the humanities is that its knowledge forms often manifest as narrative or speculative texts, expressing researchers’ unique insights and profound reflections on human existence, values, and meanings through written language. This differs from the natural sciences, which employ formulaic deductions, data charts, and repeatable experimental validations, and from the social sciences, which extensively use surveys and statistical models for empirical paths. Humanistic writing is not only an expression of thoughts and emotions but also a comprehensive cognitive exercise that integrates creativity, criticality, and reflexivity. “Writing is thinking”—it is a process of generating and deepening thoughts and emotions. Writing can stimulate creative vitality, enhance self-reflection, and expand expressive boundaries, where linguistic sensitivity, cognitive penetration, and cultural insight merge. Scholars have pointed out that writing style itself carries the unique emotional color, academic judgments, and value positions of the researcher. In this sense, humanistic academic writing is a core aspect of academic research; writing is not only a means of knowledge production in the humanities but also reflects its cognitive methods and disciplinary characteristics, serving as a fundamental vehicle for maintaining the discipline’s existence and promoting academic exchange, as well as a vital source of the discipline’s vitality. Whether expressing philosophical thoughts and probing ultimate meanings, describing historical contexts and narrating events, or constructing values and poetic insights in literary criticism and research, the organization and structural integration of materials, logical reasoning and argumentation, as well as deepening thoughts and refining spiritual experiences, all occur within the creative writing process.

Currently, AI models can transfer the language structures, argumentative patterns, and disciplinary terminology learned from large-scale corpora into specific fields of knowledge production in the humanities, promoting human-machine collaboration and achieving a holistic leap in humanistic writing. On one hand, in humanistic academic writing, researchers can fully utilize AI’s powerful data processing capabilities to efficiently collect, systematically organize, and deeply analyze literature before writing. Moreover, during the writing process, through human-machine collaboration and dialogue, dispersed knowledge can be organically integrated, constructing new knowledge maps and cognitive frameworks that help researchers break through existing theoretical and cognitive limitations, excavate deep thoughts and internal logical structures from complex texts, reveal the laws of development, distill core concepts, and ultimately give birth to new knowledge outcomes. This process is far from a simple accumulation of knowledge; it is an innovative mechanism capable of generating specific theoretical results, opening new paths for academic research and knowledge innovation. On the other hand, AI can perform localized polishing and overall optimization of professional academic expressions. This can correct, adjust, and enhance the quality of humanistic academic expressions in terms of knowledge, norms, logic, and systematics, even compelling low-quality academic research to withdraw from relevant fields. Sometimes, academic disputes in the humanities are significantly hampered by insufficient materials, unclear concepts, and weak logic; AI assistance can greatly improve the quality of academic debates and enhance their value.

The involvement of AI is not a simple process of machine-assisted writing but rather a continuous deepening of thought, inspiration, and expression optimization through human-machine interaction and back-and-forth dialogue. This process places high demands on researchers’ AI literacy, especially regarding the correct input of commands, providing high-level prompts, and deeply interpreting output results. These abilities determine the effectiveness of using AI tools. Here, the ability to pose genuine, good, and new questions becomes extremely important, returning to the essence of academic research. At the same time, as some studies have pointed out, AI excels at knowledge inheritance but falls short in creative thinking, making it difficult to replace human involvement in theoretical construction, critical reflection, value selection, and aesthetic judgment. Human intuition-based judgments that discover subtle connections among vast information, strategic choices made based on value positions, and unique expressions arising from aesthetic tastes are all of significant importance. Without human verification, modification, and deepening, the content generated by AI will carry a strong “machine flavor,” presenting as uniform and homogenized expressions.

To ensure independent academic thinking, unique insights, and distinctive academic styles, the personal qualities of researchers—“talent, courage, insight, and capability”—should not be diminished by machine assistance. It is essential to prevent dependency thinking and intellectual inertia; otherwise, research outcomes may lose the dynamism inherent in humanistic inquiry. Humanistic research must always reflect “humanity,” integrating personal life experiences into academic exploration, responding to contemporary issues with keen perception, unique creativity, and a critical spirit in pursuit of truth. People should be able to sense the emotional investment and value care of the researcher, embodying both depth of thought and warmth of emotion.

The Development of AI Relies on the Humanities’ Understanding of Humanity

As a mirror of human intelligence, artificial intelligence can help humanity understand the essence of what it means to be human more profoundly. At the same time, human understanding of itself becomes the fundamental basis for the future development and governance of AI technology. Marx pointed out that “conscious life activities distinguish humans from animal life activities directly.” Thus, human strength lies in its possession of intellect, practical creativity, and the ability to continuously acquire knowledge, master skills, and apply them to achieve goals.

At this stage, AI still mimics human intelligence, displaying behaviors similar to humans, and its developmental goal should gradually approach the internal mental structures and creative mechanisms of humanity, rather than merely replicating external behaviors. The emergence of generative AI is not accidental; it is a product of human creativity and self-awareness reaching a certain stage of development. Although currently focused vertical models have shown execution efficiency and precision surpassing humans in specific tasks and fields, they fundamentally remain tools of humanity. So far, “general models” that autonomously adapt to different environments and needs often perform worse than human infants when faced with new situations, counterfactual problems, or common-sense reasoning. Essentially, current AI knows what to do but may not understand the principles and logic behind it; the AI black box has yet to be opened, and it cannot evolve from a mere imitator to an understanding entity. Questions about the generative mechanisms and operational methods of human intellect become particularly important in this context. Human reflections on AI also represent a re-evaluation and introspection of humanity as a complex intelligent agent, making a groundbreaking effort to uncover the deep essence of humanity and understand “what makes us human” by comparing it with non-human intelligences.

Both natural sciences and humanities and social sciences oscillate between “disenchantment” and “enchantment” regarding humanity, with the core of “enchantment” always being the secrets of humanity itself. Without a profound understanding of their own intellect, the emergence of a “general model” is impossible. As Marx stated, “The dissection of the human body is the key to the dissection of the monkey body;” the signs of higher animals displayed in lower animals can only be understood after higher animals themselves have been recognized. Knowing and understanding humanity is the fundamental nature and basic value goal of the humanities. Today, the many “explainability issues” in AI are largely due to humanity’s insufficient understanding of its own intellect. Breakthroughs in human creativity, technological governance, and value alignment in AI all require a foundational understanding of humanity’s essence. The level of development in the humanities determines the future possibilities for the development of general models.

From the perspective of the relationship between the humanities and social life, the humanities cannot be replaced by AI, as they possess reflexivity. Every emergence and change of humanistic cognition and understanding intervenes in the development of social life and the construction of public sentiment, embodying the characteristic of “establishing hearts for heaven and earth, and establishing destiny for the people.” In this sense, the development of the humanities is not a linear process of progress; various humanistic thoughts cannot simply be added together to form a single ultimate truth but coexist in a pluralistic manner, collectively shaping the rich spiritual world of society and individuals. It can be said that the advancement of humanistic scholarship alters humanity’s understanding of the world, thereby exerting a significant influence on generative AI. Simultaneously, the impact of new technologies like AI on society and humanity itself also constitutes a focus of humanistic scholarship, and related reflections become part of the human spiritual world. The humanities and AI are always in a dynamic interplay of coexistence and mutual promotion. It is essential to remember that AI is created by humanity, and humans must possess the ability to truly understand and effectively harness their creations. In this sense, we are fully confident that humanistic thought can illuminate the future path of AI.