Introduction
Artificial intelligence is triggering a crisis of authorship, altering how we perceive creativity and the responsibility behind the written word. A pivotal question emerges: does the value of a text lie in the final product itself, or in the process of its creation?
In this article, we will analyze the relationship between agency and communicative function. You will discover why automation is beneficial for utilitarian texts but problematic in personal and academic spheres, and how AI influences our creativity.
Textual Value Depends on Function
Not every text requires a human author to the same degree. In the case of a receipt or a user manual, what matters is function, accuracy, and utility; therefore, automation is an advantage here.
The situation changes with indexical utterances, such as personal letters or testimonies. Here, a text that mimics human experience becomes problematic because its meaning depends on the relationship between the words and a specific person—and their accountability.
A farewell letter serves as a prime example: AI's stylistic fluency cannot replace a human's conscious choice of words. In such instances, the lack of authentic agency renders the text an empty symbol.
Textual Value Depends on Communicative Function and Agency
A problem arises when AI imitates speech acts, such as apologies or promises. Although a machine can formulate them correctly, they lack genuine remorse or intention, which fundamentally alters the nature of the communication.
The value of an author to the recipient depends on whether the text is merely a vehicle for information or a form of encounter with another mind. In literature and essays, we seek traces of human existence, not just an efficient composition of characters.
Authenticity in the age of AI is the alignment between the declared source and the actual creative process. A text becomes inauthentic when it is presented as an expression of experiences that the author has not actually lived.
Textual Value Depends on Process and Resistance
AI-generated texts are often rated higher than human ones because models optimize for clarity and accessibility. However, they lack creative resistance—the fractures and idiosyncrasies that, in great literature, compel the reader to engage in interpretive work.
True creativity differs from the generative recombination of symbols. Human creativity stems from a confrontation with the resistance of the world and carries an existential cost of choice, which a machine does not bear while effortlessly generating thousands of variants.
In education, this leads to a crisis in competency assessment. Because AI creates synthetic coherence, detectors are insufficient. It is necessary to shift from evaluating the finished product toward verifying the thought process and the triangulation of evidence.
Summary
Collaboration with AI can be a symbiosis, but it can also become cognitive parasitism when an improved result comes at the cost of losing the author's independent judgment and craft.
The greatest threat is not the machine taking over the pen, but the loss of our ability to distinguish between written and generated text. In a world of perfect simulations, error and fracture remain the only honest traces of human presence.
Ultimately, the question of authorship is a question of whether we still wish to bear the burden of being the subject behind our own words.
Frequently Asked Questions
Is every text written by AI as problematic as one pretending to be a personal statement?
No, the problematic nature of an AI text depends on its function. In the case of utilitarian texts, such as instructions or reports, the correct result and standardization are key, whereas in personal utterances, it is the author and the creation process that define the meaning of the message.
Why is the fact that a text was written by AI a problem in some cases, while in others it does not matter?
In the case of texts that are merely conveying information (e.g., about temperature), the AI origin is irrelevant because factual accuracy is what counts. It becomes a problem in situations where the text is an act of a person, a testimony, or an expression of responsibility and experience (e.g., apologies, diaries), because then AI creates only a linguistic simulation of the relationship between the utterance and the actual experience.
Why might AI texts be rated higher than human ones, despite their lack of authenticity?
AI texts may be rated higher because they often surpass human works in characteristics such as clarity, accessibility, and the direct communication of emotions and themes. As a result, they are easier for a mass audience to interpret and consume than complex human works, which require more effort.
What is text authenticity in the age of AI, and what does the author's value to the recipient depend on?
Text authenticity is the consistency between the declared source and the actual process of its creation, rather than biological origin itself. The author's value to the recipient depends on the type of text: it is negligible in functional content, significant in analysis and journalism, and crucial in personal forms (e.g., diaries), where the sender's identity is integral to the meaning of the message.
Does the fact that AI can create something new and valuable mean that it is truly creative?
The answer depends on the adopted definition of creativity. If we consider it solely through the prism of the product (novelty and value), then AI creations are creative; however, if intention, self-awareness, or a personal process of discovery are taken into account, this issue ceases to be obvious.
Does the ability of AI to imitate the style of a specific artist mean that the machine possesses human creativity?
The ability of AI to imitate an artist's style does not mean it possesses human creativity, but rather indicates that certain elements of style are formalizable and capable of being modeled. The machine can reproduce the surface of creativity by identifying regularities (e.g., harmonic or rhythmic ones) without the need to reconstruct the author's psychology.
Can artificial intelligence be considered creative, and how does it affect human creativity?
Artificial intelligence can be perceived as part of a systemic creative framework or an active tool generating new possibilities, although the issue of its independent authorship remains contentious. The impact of AI on human creativity is ambivalent: on one hand, it improves efficiency and the quality of artifacts; on the other, it may weaken intrinsic motivation and limit user input by providing ready-made solutions too quickly.
How does human creativity differ from the generative capabilities of AI in the context of the creative process and the author's experience?
Human creativity differs from AI through the capacity for transformational discovery—the ability to perceive anomalies and create new rules rather than merely recombining existing knowledge. Human creativity stems from a living subject's confrontation with the world and carries an existential cost and a sense of authorship that AI systems do not experience.
Can artificial intelligence be part of the creative process without assuming the role of the author?
Yes, AI can serve as a brainstorming partner, an editor, or a catalyst for discovery, helping to break through creative blocks and generate variants. In such an arrangement, the human retains the key role, deciding on the meaning of the work and determining which possibilities are worth realizing.
Why is the mere statement that AI was used in a text insufficient for assessing authorship?
A simple statement about the use of AI is too sparse because it does not distinguish between basic typo corrections and the design of the thesis and argumentation of the entire text. To reliably assess authorship, one must describe the collaboration through the distribution of initiative, control, and responsibility, rather than just the presence of the tool.
Does the presence of a human in the process of AI text generation guarantee the maintenance of control and responsibility for the final result?
No, the mere presence of a human in the loop can mask an actual loss of agency and may be nothing more than ceremonial or illusory control. The phenomenon of automation bias and the specific style of generative AI can lead to over-reliance on incorrect system recommendations, which hinders the calibration of trust and may worsen task outcomes compared to working independently.
How do AI suggestions affect the diversity and originality of texts written by humans?
AI suggestions can lead to language homogenization and the imposition of formulaic clichés, which limits the author's creative space and blurs cultural differences. Although an individual may feel their own creativity has increased, on a collective scale, texts drift toward a common center, reducing the overall diversity and originality of expression.
Does collaborating with AI always benefit the author and their craft?
Not always; while AI can increase efficiency and improve the final product, it can also lead to the author's alienation and a weakened sense of connection to the text. Depending on the user, this collaboration can be beneficial, neutral, or even parasitic, when the improvement of the result comes at the cost of losing human independent judgment and competence.
How should the degree of human agency be determined in a text co-created by AI?
The degree of human agency is determined not by the percentage of text written by AI, but through an analysis of who made the constitutive decisions (e.g., establishing the thesis or choosing sources). The key is maintaining meaningful human control, which includes the competence to evaluate the output, the ability to refuse it, and responsibility for the text.
Why can AI-generated texts be convincing even when they are completely false?
AI texts are convincing because they utilize signals of competence, such as correct syntax, expert terminology, and confident phrasing, separating the form of knowledge from its actual truthfulness. These systems generate sequences of content that are probable within a given context, allowing them to create plausibly sounding response structures even in the total absence of reliable sources.
Why do AI models confabulate, and what are the consequences of evaluating texts based on their form?
Confabulation results from the generative ability of models to creatively fill information gaps, and evaluation mechanisms often reward guessing instead of admitting ignorance. Evaluating texts based on form rather than meaning leads to situations where grammatically and stylistically correct texts may receive high marks despite lacking coherent sense.
Why are AI-generated errors harder to detect than complete gibberish, and what risks does this pose?
AI errors are harder to detect because models create a synthetic appearance of coherence by linking true elements (e.g., names or dates) with false relationships, which leads people to trust content containing familiar components. This poses a risk in the form of automation bias, where the user ignores error signals and alters their own interpretation of evidence to protect the assumption that the tool is reliable.
Why is simply recommending the verification of AI content insufficient, and how can the problem of model confabulation be solved?
Simply recommending the verification of responses is insufficient because users lacking expert knowledge in a given field may not recognize the model's errors. The problem of confabulation can be mitigated by using external knowledge bases, RAG systems, uncertainty measurement (e.g., semantic entropy), and building systems capable of distinguishing situations that require content generation from those that require proof.
Why is AI language fluency misleading, and how does this affect the value of text as evidence of knowledge?
AI language fluency is misleading because it serves as a strong stimulus for anthropomorphism and attributing understanding or consciousness to the system, which it does not possess. As a result, the high quality of the linguistic product becomes decoupled from the cognitive process, meaning the text ceases to be reliable evidence of possessing knowledge or performing intellectual work.
How does the use of AI in the academic writing process affect the ability to reliably assess a student's skills?
The use of AI leads to the phenomenon of measurement contamination, meaning that a high grade for a text no longer necessarily indicates high competence of the student across the entire studied area. This is because the result depends not only on the person's abilities but also on the efficiency of the AI system they are able to steer.
Why are AI detectors insufficient in education, and how should methods for grading student work change?
AI detectors are insufficient because they analyze only the properties of the text rather than its creation history, while demonstrating low effectiveness in the case of hybrid texts and those edited by humans. Instead of relying on the detection of the final product, assessment methods should shift toward evaluating the process of creating the work.
How can a student's knowledge and skills be reliably assessed in the age of AI without relying solely on written text?
Student knowledge can be verified through triangulation of evidence, including documenting the creation process, oral defense, and analysis of portfolios and reflections. It is also possible to assess collaboration with AI by critiquing generated content, identifying hallucinations, and justifying the acceptance or rejection of the model's suggestions.
What are the real benefits and risks of using AI in writing scientific and academic texts?
The benefit of AI is the improvement of grammar and text structure, which reduces linguistic inequalities for non-native speakers. Risks include the homogenization of the academic voice, the loss of scholarly rhetorical caution, and the risk of weakening the author's research independence. Furthermore, this technology may reinforce pathologies in the publishing system by drastically lowering the cost of producing texts that resemble science.
How should education and the assessment of texts change in light of the ability to generate them using AI?
Education should adapt the rules for using AI to specific learning outcomes, applying a ban where the goal is independent mastery of a task, and integration when learning how to work with tools. It is crucial to link assessment to the reasoning process and the author's responsibility, rather than just the final product in the form of a text.
