Assignment of Paper 110: History of 20th Cen Literature: 1900 to 2000

 Paper 110: From Dadaist Chance to Prompt Poetics: Generative AI and the Reconfiguration of Literary Authorship.  

Assignment of Paper 110: History of 20th Cen Literature: 1900 to 2000 

From Dadaist Chance to Prompt Poetics: Generative AI and the Reconfiguration of Literary Authorship. 

Academic Details: 

•  Name: Chetna J. Bhaliya 
•  Roll No.: 03 
•  Enrollment No.: 5108250003 
•  Sem.: 2 
•  Batch: 2025-27 
•  E-mail: bhaliyachetna4112@gmail.com  

 

Assignment Details: 

• Paper Name: History of 20th Cen Literature: 1900 to 2000 
• Paper No.: 110 
• Paper Code:2 2403 
• Unit: 2 
• Topic: From Dadaist Chance to Prompt Poetics: Generative AI and the Reconfiguration of Literary Authorship. 
• Submitted To: Smt. Gardi, Department of English, Maharaja Krishnakumarsinhji Bhavnagar University 
• Submitted Date: 3rd May 2026 

 

The following information—numbers are counted using Quill Bot: 

•  Images: 2 
•  Words: 5209 
•  Characters: 40372 
•  Characters without spaces:35301 
•  Paragraphs:177 
•  Sentences: 381 
•  Reading time: 20 m 50 s 

 

Table of Contents: 

Abstract....................................................................................................................................... 3

Keywords: ............................................................................................................................... 4

Research Questions .................................................................................................................. 4

Hypothesis.............................................................................................................................. 4

1. Introduction ......................................................................................................................... 4

2. Historical Context: Avant-Garde Experimentation and the Crisis of Authorship .................. 5

2.1 The Dadaist Rejection of Traditional Authorship ............................................................ 5

2.2 Theoretical Debates on Authorship .................................................................................... 7

3. Digital Media and the Transformation of Literary Production ................................................. 8

3.1 Electronic Literature and Media-Specific Analysis................................................................ 8

3.2 Algorithmic Culture and Computational Creativity ................................................................ 8

4. Generative Artificial Intelligence and Literary Creativity .......................................................... 9

4.1 Large Language Models and Algorithmic Text Generation..................................................... 9

4.2 AI as a Collaborative Creative Tool ..................................................................................10

4.3 AI-Augmented Authorship ...........................................................................................10

5. Prompt Poetics and the Reconfiguration of Authorship .........................................................11

5.1 Prompt Design and the Emergence of Prompt Poetics...........................................................11

5.2 Collaborative Creativity and the Transformation of the Author’s Role...................................12

5.3 Case Study: AI-Generated Poetry and Prompt-Based Composition .......................................13

6. Continuities Between Avant-Garde Experimentation and AI Creativity ..............................14

6.1 Dadaist Chance Operations and Experimental Authorship ................................................14

6.2 Algorithmic Creativity and the Legacy of the Avant-Garde ........................................16

7. Contemporary Relevance: AI, Authorship, and Cultural Production in the Digital Age ..........17

8. Conclusion ........................................................................................................................18

References: .....................................................................................................................19

Abstract 

This paper examines the transformation of literary authorship in the context of generative artificial intelligence by tracing a historical and conceptual lineage from avant-garde chance-based experimentation to contemporary prompt-based writing practices. Early twentieth-century avant-garde movements, particularly Dadaism, challenged traditional notions of authorship by introducing randomness, procedural techniques, and anti-art strategies that undermined the idea of the author as a singular creative genius. These experiments anticipated later theoretical debates about the instability of authorship within literary and cultural production. 

Building on this historical framework, the study analyzes how recent developments in generative AI systems—such as large language models including GPT-3 and ChatGPT—have introduced new forms of algorithmic text generation. These systems produce literary outputs through probabilistic prediction based on large textual datasets, enabling users to generate poems, narratives, and essays through textual prompts. As a result, the creative act increasingly involves the design of prompts that guide algorithmic generation rather than the direct composition of textual content. 

The paper proposes the concept of “prompt poetics” to describe this emerging literary practice in which writers function as designers of generative instructions and curators of machine-generated outputs. Drawing on theoretical perspectives from digital humanities, media studies, and AI research, the study argues that generative AI does not represent a complete break from earlier literary experimentation but rather extends a long tradition of procedural creativity first explored by avant-garde artists such as Tristan Tzara within the movement of Dadaism. 

Ultimately, the paper demonstrates that generative AI reconfigures literary authorship by redistributing creative agency among human authors, algorithmic systems, and digital infrastructures. By situating contemporary AI writing technologies within a broader historical continuum of experimental artistic practices, the study contributes to ongoing debates about creativity, originality, and the evolving role of the author in the digital age. 

Keywords: Generative artificial intelligence, literary authorship, prompt poetics, algorithmic creativity, avant-garde literature. 

Research Questions 

• How did avant-garde literary movements such as Dadaism challenge traditional concepts of literary authorship through chance-based experimentation? 

• In what ways do generative AI system such as ChatGPT transform literary production through prompt-based text generation? 

• How does the emergence of “prompt poetics” reshape the role of the author within contemporary digital literary culture? 

Hypothesis 

The emergence of generative artificial intelligence reconfigures literary authorship by shifting creative agency from the individual human author to a collaborative process involving human prompts, algorithmic systems, and digital infrastructures, thereby extending earlier avant-garde experiments with chance-based creativity developed within Dadaism. 

1. Introduction 

 

The concept of authorship has undergone profound transformations throughout literary history. Traditionally, authorship was associated with individual creativity, originality, and intentional expression. In the Romantic literary tradition, the author was frequently imagined as a singular creative genius whose imagination shaped the meaning and aesthetic form of literary works. However, twentieth-century literary theory destabilized this concept by emphasizing the role of language, culture, and interpretation in the production of meaning (Montrose). With the emergence of generative artificial intelligence (AI), authorship is once again undergoing a major transformation. 

Generative AI systems are capable of producing poems, narratives, essays, and scripts through machine-learning algorithms trained on vast textual datasets. These systems do not simply store texts but generate new linguistic sequences through probabilistic prediction. As a result, the boundaries between human creativity and machine production have become increasingly blurred. Contemporary debates surrounding AI-generated literature therefore raise fundamental questions about creativity, originality, and the role of the author (Bender et al.). 

However, the idea that literature can be produced through non-human or mechanical processes is not entirely new. Early avant-garde artistic movements, particularly Dadaism, experimented with randomness, fragmentation, and procedural creativity. These artistic practices challenged the traditional belief that literary works originate solely from the intentional creativity of an individual author. In many ways, the experimental techniques of the avant-garde anticipated contemporary algorithmic writing practices (Bürger; Wilkinson). 

This paper argues that generative AI represents both a continuation and transformation of earlier avant-garde experiments with chance and procedural creativity. By tracing the conceptual lineage from Dadaist chance operations to contemporary “prompt poetics,” the study demonstrates how generative AI reconfigures literary authorship by redistributing creative agency among humans, machines, and digital infrastructures. Drawing on theoretical perspectives from literary studies, media theory, digital humanities, and artificial intelligence research, this study explores how algorithmic writing technologies challenge established understandings of authorship and creativity (Manovich; Hayles). 

 

2. Historical Context: Avant-Garde Experimentation and the Crisis of Authorship 

2.1 The Dadaist Rejection of Traditional Authorship 

Dadaism emerged in the early twentieth century as a radical artistic response to the social and cultural upheavals produced by the First World War. Rejecting traditional aesthetic values, Dadaist artists embraced absurdity, randomness, and anti-art practices as a way of critiquing established cultural institutions. In literary production, Dadaists frequently experimented with techniques that deliberately disrupted conventional forms of authorship. 

One of the most famous examples of Dadaist literary experimentation was the chance-based poetry technique proposed by Tristan Tzara. His method involved cutting words from a newspaper, placing them in a bag, and randomly selecting them to create a poem. In this process, the structure of the poem was determined not by authorial intention but by chance. Such experiments fundamentally questioned the assumption that literary works must originate from deliberate creative control. 

The theoretical significance of these practices has been explored extensively in avant-garde scholarship. According to Peter Bürger in Theory of the Avant-Garde, movements such as Dada sought to dismantle the institutional separation between art and everyday life (Bürger). By rejecting the traditional role of the artist as a creative genius, avant-garde movements challenged the ideological structures that defined artistic value. 

Similarly, Susanne Knaller’s analysis of Walter Benjamin’s reflections on avant-garde aesthetics emphasizes how experimental literary forms destabilized the authority of traditional artistic institutions (Knaller). Knaller argues that avant-garde literature often used fragmentation and destruction as aesthetic strategies to critique dominant cultural structures. Sherwin Simmons’s research on early twentieth-century modernist art further highlights how avant-garde movements employed provocative artistic strategies to challenge social and moral conventions (Simmons). 

More recent scholarship has continued to explore the radical potential of avant-garde textual experimentation. Bruce Wilkinson’s analysis of underground literary magazines demonstrates how avant-garde writers used obscenity, fragmentation, and unconventional language to challenge established literary norms (Wilkinson). These experimental strategies reveal that challenges to authorship have long been embedded within the history of modern literary culture. 

2.2 Theoretical Debates on Authorship 

The avant-garde critique of authorship significantly influenced later developments in literary theory. Twentieth-century theorists increasingly questioned the assumption that authors are the primary source of meaning within literary texts. 

Louis Montrose’s concept of the “textuality of history and the historicity of texts” illustrates how literary works are shaped by cultural and ideological forces rather than by individual creativity alone (Montrose). Montrose argues that literary texts must be understood within the broader historical and cultural contexts in which they are produced. 

Similarly, W. J. T. Mitchell has emphasized the agency of images and representations within cultural systems. In his influential essay “What Do Pictures ‘Really’ Want?” Mitchell suggests that images and texts operate within complex networks of meaning that extend beyond the intentions of their creators (Mitchell). 

Discussions surrounding authorship have also appeared across multiple intellectual disciplines. Interviews with architectural theorists Denise Scott Brown and Robert Venturi, for example, reveal how collaborative creative practices challenge traditional notions of individual authorship in architecture (Barriere et al.). Likewise, film scholarship has explored debates about authorship in cinematic production. Rosenzweig and Liebman’s discussion of Helke Sander’s film criticism illustrates how creative works often emerge from collective processes rather than singular artistic vision (Rosenzweig and Liebman). 

Philosophical reflections on authorship have similarly emphasized the complexity of creative production. A lecture by Jean-Paul Sartre, published in the journal October, explores the relationship between individual subjectivity and cultural production (Derins et al.). Such philosophical debates reinforce the idea that authorship is not a fixed or universal concept but rather a historically constructed cultural category. 

These theoretical developments provide an important foundation for contemporary discussions about algorithmic creativity. If literary texts are shaped by cultural, linguistic, and technological systems, then the emergence of computational writing technologies represents a continuation of this broader transformation in the concept of authorship (Hayles). 

3. Digital Media and the Transformation of Literary Production 

3.1 Electronic Literature and Media-Specific Analysis 

The development of digital technologies has fundamentally altered the ways in which literary texts are produced, distributed, and interpreted. Scholars in digital humanities have emphasized that literature must be understood within the technological environments that shape its form. 

One of the most influential theorists of electronic literature is N. Katherine Hayles. Hayles argues that digital literature cannot be analyzed using traditional print-based literary frameworks because digital texts operate through computational processes (Hayles). 

Electronic literature often incorporates interactive elements, algorithmic generation, and multimedia features that challenge conventional notions of textual stability. Unlike printed books, digital texts may evolve dynamically through user interaction or automated computational processes. 

Research on experimental digital narratives further illustrates these transformations. Hilda Forss’s study of the work of digital poet Johannes Heldén demonstrates how speculative digital narratives blur the boundaries between authorship, technology, and world-building (Forss). In such works, the author functions less as a traditional storyteller and more as a designer of systems that generate narrative experiences. 

3.2 Algorithmic Culture and Computational Creativity 

The emergence of algorithmic technologies has significantly influenced contemporary cultural production. Digital media theorist Lev Manovich describes modern cultural production as operating within an “age of algorithms,” where computational processes play a central role in shaping artistic and cultural practices (Manovich). 

Algorithms now influence a wide range of creative activities, including film editing, music production, visual design, and literary writing. These developments challenge the assumption that creativity is exclusively human. Instead, creative production increasingly involves interactions between human imagination and computational systems. 

In this context, the role of the author becomes more complex. Rather than producing texts entirely through individual effort, contemporary writers often rely on digital tools, databases, and algorithmic systems that assist with the creative process. This transformation sets the stage for the emergence of generative artificial intelligence as a new form of computational creativity. 

4. Generative Artificial Intelligence and Literary Creativity 

4.1 Large Language Models and Algorithmic Text Generation 

Recent developments in artificial intelligence have led to the creation of powerful generative models capable of producing human-like text. These systems, commonly known as large language models, are trained on massive datasets containing books, articles, and online texts. Through machine-learning techniques, they learn statistical patterns within language and generate new textual sequences based on probabilistic prediction. 

Despite their impressive linguistic capabilities, researchers emphasize that these models do not possess genuine understanding. In their influential paper “Stochastic Parrots,” Emily M. Bender and her colleagues argue that large language models generate language by recombining patterns from their training data rather than by understanding meaning (Bender et al.). 

Nevertheless, generative AI systems have rapidly become integrated into creative practices. Writers, artists, and designers increasingly use AI tools to generate ideas, draft texts, and explore experimental forms of artistic expression. 

4.2 AI as a Collaborative Creative Tool 

Scholars across multiple disciplines have begun to examine how AI technologies function as collaborative creative partners. Susan Cake’s research on AI in script development demonstrates that generative AI tools can assist writers by generating narrative ideas, dialogue, and structural suggestions during the creative process (Cake). 

Similarly, Cheng-Wen Huang and his colleagues emphasize the importance of co-creation in AI-supported literacy practices (Huang et al.). 

User interaction with generative AI has also been studied within human-computer interaction research. Chiara Di Lodovico and her collaborators analyze how users develop intuitive “folk theories” about generative AI systems (Di Lodovico et al.). 

These interactions demonstrate that generative AI writing is not simply an automated process but rather a form of collaborative creativity involving both human and machine contributions. 

 

4.3 AI-Augmented Authorship 

The integration of AI into academic and creative writing has led scholars to propose new models of AI-augmented authorship. Qiangqiang Gu and his colleagues discuss how generative AI technologies are transforming academic publishing by assisting researchers with drafting and editing scholarly texts (Gu et al.). 

Similarly, G. Raghavendra and his collaborators provide a systematic review of generative AI research in library and information science (Raghavendra et al.). 

Research in design studies has also explored the creative potential of AI tools. Savindu Herath and his colleagues examine how generative AI systems can function as creativity support tools for visual design (Herath et al.). 

5. Prompt Poetics and the Reconfiguration of Authorship 

 

5.1 Prompt Design and the Emergence of Prompt Poetics 

One of the most distinctive features of generative AI writing is the central role played by prompts. Instead of composing texts directly in the traditional sense, users provide instructions or prompts that guide artificial intelligence systems in generating linguistic outputs. These prompts may take the form of descriptive commands, stylistic guidelines, narrative frameworks, or thematic suggestions. The design of prompts therefore becomes an important creative practice in itself, shaping the structure, tone, and thematic direction of the generated text (Di Lodovico et al.). 

In this context, the act of writing increasingly involves experimentation with language at the level of instruction rather than composition. Writers often refine prompts iteratively, testing different formulations to observe how the AI system responds. This process resembles an interactive dialogue between human intention and computational generation. Researchers studying human–AI interaction note that users develop intuitive strategies for communicating with generative systems, gradually learning how specific phrasing, structure, and contextual cues influence the outputs produced by AI models (Di Lodovico et al.). As a result, prompt design emerges as a new form of literary craftsmanship. 

This process can be described as a form of “prompt poetics,” in which the creative act lies not simply in writing words but in shaping the conditions under which algorithmic systems generate language. Rather than functioning as direct producers of textual content, writers design prompts that establish conceptual parameters for algorithmic generation. Through this method, the author constructs a framework within which the AI system produces multiple possible textual variations. 

5.2 Collaborative Creativity and the Transformation of the Author’s Role 

Prompt-based writing therefore transforms the role of the author in significant ways. In traditional literary practice, the author is responsible for crafting each sentence, controlling narrative structure, stylistic expression, and thematic development. In prompt-based writing, however, the author becomes a curator or orchestrator of computational processes. Instead of determining every word in advance, the author establishes guiding parameters and then selects, edits, or refines the outputs generated by the AI system. 

This shift suggests that authorship in the age of generative AI involves a more distributed form of creativity. The final text is not solely produced by the human writer nor entirely generated by the machine. Instead, it emerges from a collaborative interaction between human intention, algorithmic processing, and digital datasets. Scholars studying AI-supported creative workflows argue that such collaborations represent a new model of creative production in which humans and machines participate in complementary roles (Cake; Huang et al.). 

The concept of prompt poetics also reflects broader transformations within digital media culture. As computational technologies increasingly influence cultural production, creative practices are becoming deeply intertwined with algorithmic infrastructures. Media theorists argue that contemporary cultural expression is shaped by the interaction between human creativity and algorithmic systems embedded within digital platforms (Manovich). In this context, literary production must be understood not only as an individual artistic activity but also as a technological process mediated by software, databases, and machine learning models. 

Scholars of electronic literature have similarly emphasized that digital media fundamentally alter the conditions under which texts are produced and interpreted. According to theories of media-specific analysis, digital texts operate within computational environments that shape their structure, distribution, and reception (Hayles). Prompt-based writing exemplifies this transformation by demonstrating how literary creativity can emerge from interactions with algorithmic systems rather than from purely human authorship. 

Consequently, prompt poetics represents a significant development in the evolving history of literary creativity. By shifting the focus from direct textual production to the design of generative instructions, it challenges traditional assumptions about authorship, originality, and creative control. Rather than diminishing the role of the author, prompt-based writing redefines authorship as a form of technological collaboration in which human creativity is expressed through the design and interpretation of algorithmic processes. 

5.3 Case Study: AI-Generated Poetry and Prompt-Based Composition 

Recent developments in generative artificial intelligence provide concrete examples of how prompt-based writing operates in contemporary literary practice. Large language models such as GPT-3 and conversational systems like ChatGPT are capable of generating poems, narratives, and essays in response to textual prompts provided by users. In these systems, the prompt functions as a conceptual framework that guides the generation of linguistic output. By specifying themes, stylistic features, or poetic structures, users can shape the form and tone of the generated text while the algorithm produces the actual linguistic sequence. The resulting poem therefore emerges from an interaction between human instruction and computational generation rather than from a single authorial source. 

Creative writing platforms such as Sudowrite further illustrate how prompt-based systems are integrated into literary practice. These tools allow writers to generate poetic lines, experiment with metaphorical language, or expand narrative fragments through algorithmic suggestions. In many cases, authors use AI-generated outputs as drafts or creative stimuli, selecting and editing the generated material to produce a final literary work. This process reflects the dynamics of “prompt poetics,” in which the writer designs generative instructions and curates the resulting outputs. Much like the chance-based techniques employed by Dadaist artists, AI-assisted poetry introduces elements of unpredictability into the creative process. However, instead of relying on physical randomness, generative AI operates through computational probability, demonstrating how contemporary literary experimentation extends earlier avant-garde explorations of procedural creativity. 

6. Continuities Between Avant-Garde Experimentation and AI Creativity 

The emergence of generative artificial intelligence may appear to represent a radical technological innovation in literary production. However, when examined within a broader historical perspective, many of the creative principles underlying AI-generated writing can be traced back to earlier experimental movements in modern art and literature. In particular, the avant-garde movements of the early twentieth century introduced artistic techniques that challenged traditional assumptions about authorship, intentionality, and creative control. 

These movements experimented with chance, fragmentation, procedural creativity, and collaborative artistic practices. Such approaches disrupted the conventional belief that literary works must originate from the deliberate expression of an individual author. By emphasizing randomness and experimentation, avant-garde artists questioned the idea that creativity is exclusively the product of conscious artistic intention. 

Generative AI systems introduce a new technological dimension to these earlier experiments. While avant-garde artists relied on manual procedures and chance operations, AI systems use computational algorithms and large datasets to generate textual outputs. Despite this technological difference, both practices share a common objective: to explore alternative forms of creativity that extend beyond traditional models of individual authorship. 

6.1 Dadaist Chance Operations and Experimental Authorship 

One of the most influential examples of avant-garde experimentation with authorship can be found in the literary practices of the Dada movement. Emerging during the early twentieth century in response to the cultural devastation of the First World War, Dadaism rejected conventional aesthetic standards and embraced radical artistic experimentation. Dadaist artists viewed traditional cultural institutions as complicit in the social and political structures that had produced the war. As a result, they sought to undermine established artistic conventions through disruptive and unconventional creative methods. 

In literary practice, Dadaist writers frequently experimented with techniques that replaced deliberate composition with random procedures. One of the most famous examples is the method proposed by Tristan Tzara, who suggested creating poetry by cutting words from a newspaper, placing them into a bag, and randomly selecting them to form a poem. This technique eliminated the author’s direct control over the arrangement of words, allowing chance to determine the structure of the text. 

Such experiments fundamentally challenged the traditional image of the author as a creative genius who consciously shapes the meaning of a literary work. Instead, they suggested that literary creativity could emerge from procedural systems that operate independently of individual intention. Scholars of avant-garde aesthetics argue that these practices were intended to dismantle the institutional authority of art and expose the arbitrary nature of cultural conventions (Bürger). 

Theoretical discussions of avant-garde experimentation have also emphasized the role of destruction and fragmentation as aesthetic strategies. Analyses of modernist cultural theory suggest that avant-garde movements deliberately destabilized traditional artistic forms in order to challenge dominant cultural structures (Knaller). Through techniques such as collage, montage, and chance composition, avant-garde artists created works that resisted conventional narrative coherence and authorial control. 

Further research into early twentieth-century artistic movements demonstrates how avant-garde experimentation often provoked controversy by confronting social and cultural norms. Studies of modernist visual art reveal that artists frequently used provocative imagery and unconventional artistic methods to critique bourgeois morality and cultural authority (Simmons). These practices illustrate how avant-garde experimentation extended beyond literature into broader cultural and artistic contexts. 

Later analyses of experimental literary magazines and underground publishing cultures also demonstrate how avant-garde techniques influenced alternative forms of textual production. Scholars examining avant-garde publications have shown how experimental writing often employed fragmentation, obscenity, and unconventional typography to disrupt traditional literary conventions (Wilkinson). These practices reinforced the avant-garde commitment to challenging established ideas of authorship and artistic value. 

6.2 Algorithmic Creativity and the Legacy of the Avant-Garde 

Although generative AI technologies differ significantly from the manual techniques used by early avant-garde artists, they share several important conceptual similarities. Both practices challenge the assumption that creativity must originate exclusively from human intention. Instead, they demonstrate how creative outputs can emerge from systems, procedures, or algorithms that operate according to predetermined rules. 

In the case of generative AI, these systems rely on machine learning models trained on vast collections of textual data. By analyzing patterns within language, AI systems generate new textual sequences through probabilistic prediction. The resulting outputs may resemble human writing while still reflecting the statistical processes underlying their generation (Bender et al.). 

From a theoretical perspective, this process can be understood as a continuation of earlier avant-garde experiments with procedural creativity. Just as Dadaist artists used chance operations to disrupt conventional artistic control, AI systems introduce elements of unpredictability into textual production through algorithmic processes. While the technological mechanisms differ, the underlying creative principle—allowing external systems to influence artistic outcomes—remains similar. 

Digital media scholars argue that contemporary cultural production increasingly operates within algorithmic environments where computational systems shape creative practices (Manovich). In such environments, artists and writers frequently interact with digital tools that generate, modify, or curate creative content. Generative AI represents an advanced stage of this development by enabling machines to produce complex linguistic outputs that can be incorporated into literary works. 

Furthermore, research on digital literature demonstrates that computational technologies often blur the boundaries between authors, texts, and readers. In many forms of electronic literature, the author designs a system that generates or transforms texts dynamically rather than producing a fixed narrative (Hayles). This approach closely resembles the structure of AI-assisted writing, where authors guide algorithmic systems through prompts and instructions rather than composing every element directly. 

Consequently, generative AI can be interpreted as part of a broader historical continuum of experimental creativity. From avant-garde chance techniques to digital algorithmic systems, each stage in this evolution has expanded the possibilities of artistic production by challenging conventional ideas of authorship and originality. 

The connection between avant-garde experimentation and AI creativity therefore highlights the enduring relationship between technological innovation and artistic transformation. Rather than representing a complete break with the past, generative AI extends a long tradition of experimental practices that seek to redefine the boundaries of literary creativity. 

7. Contemporary Relevance: AI, Authorship, and Cultural Production in the Digital Age 

The rapid expansion of generative artificial intelligence has intensified debates about creativity, intellectual ownership, and cultural production. While earlier avant-garde movements challenged the authority of the artist through experimental techniques, contemporary AI systems raise more complex questions because they operate within large-scale technological infrastructures that rely on massive datasets and computational power (Manovich). 

One major concern involves the issue of creative ownership and intellectual property. Generative AI systems are trained on enormous corpora of texts produced by human authors, which raises ethical questions regarding the appropriation of creative labor. Scholars have argued that these datasets often incorporate copyrighted material without explicit permission, complicating the relationship between original authors and algorithmic systems (Bender et al.). In academic and professional environments, similar concerns arise regarding the attribution of authorship when AI tools assist in writing processes (Gu et al.). 

At the same time, several researchers emphasize the productive potential of AI-assisted creativity. Studies on generative AI within educational and research environments suggest that these tools can enhance creative experimentation, brainstorming, and interdisciplinary collaboration (Raghavendra et al.; Huang et al.). Rather than replacing human authors, AI systems may function as cognitive partners that support complex creative workflows (Cake). 

Research on human–AI interaction further illustrates how creative processes are evolving in response to algorithmic systems. Users often develop intuitive strategies for guiding AI outputs through prompts, experimenting with linguistic structures to achieve desired results (Di Lodovico et al.). This interactive process reflects a shift in creative practice, where authors increasingly operate as curators, editors, and designers of algorithmic systems rather than as sole producers of textual content. 

These transformations also reflect broader developments within digital media culture. According to media theorists, contemporary creative practices are deeply embedded within algorithmic infrastructures that shape how cultural products are generated, distributed, and interpreted (Manovich). As a result, literary production must now be understood as part of a larger technological ecosystem that includes databases, machine learning systems, and digital platforms. 

Moreover, developments in design research suggest that AI creativity tools are increasingly being developed with the explicit goal of supporting human innovation. Studies of generative AI design systems emphasize the importance of creating tools that encourage experimentation and collaboration rather than replacing human creative agency (Herath et al.). 

The contemporary cultural landscape therefore reveals a complex relationship between authors, machines, and audiences. While AI technologies challenge traditional definitions of authorship, they also open new possibilities for collaborative creativity. This dynamic situation resembles earlier historical moments in which technological innovations—such as the printing press or digital publishing—reshaped literary culture and redefined the role of authors within society (Forss; Hayles). 

8. Conclusion 

The evolution of literary authorship from Dadaist chance techniques to contemporary generative AI illustrates the dynamic relationship between creativity, technology, and cultural institutions. Avant-garde movements such as Dadaism challenged traditional artistic authority by embracing randomness, fragmentation, and procedural creativity (Bürger; Knaller). 

These experiments anticipated many of the debates that have emerged in response to algorithmic writing technologies. Generative AI systems produce texts through probabilistic models and prompt-based interactions, redistributing creative agency across networks of humans, machines, and digital infrastructures (Bender et al.; Gu et al.). 

Rather than replacing human creativity, generative AI invites a reconsideration of what it means to be an author in the digital age. Writers increasingly function as designers of prompts, curators of algorithmic outputs, and collaborators with computational systems (Cake; Huang et al.). 

Ultimately, the transition from Dadaist chance to prompt poetics demonstrates that literary authorship has always been shaped by technological and cultural contexts. As AI technologies continue to evolve, they will likely inspire new forms of literary experimentation that further redefine the boundaries between human creativity and machine production (Manovich; Hayles). 

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