From Author to Scriptor: Education After the End of Scarcity
 |
| AI image |
The current debate around artificial intelligence in education often unfolds as a moral drama. Students who use AI are accused of bypassing effort, outsourcing thought, or hollowing out learning itself. Universities respond by tightening rules, redesigning exams, or banning tools outright. Beneath these reactions lies a deeper problem: intelligence is still being measured according to a model shaped by scarcity, even though students now operate within an environment defined by informational excess.
For centuries, education developed around limited access to texts and slow circulation of knowledge. Mastery meant internalization. Writing an essay demonstrated that information had been absorbed, retained, and reproduced by an autonomous mind. This model assumed a Cartesian subject: bounded, self-sufficient, and fully present to itself. Under those conditions, authorship functioned as proof of understanding.
The End of the Scarcity Paradigm
That framework no longer corresponds to reality. Large language models, search engines, and digital archives have transformed writing into an abundant, iterative activity. Drafting, summarizing, and rephrasing now happen instantly. The central question is no longer whether a student can produce text unaided, but whether they can evaluate, contextualize, and take responsibility for what circulates through them.
The persistence of older assessment methods generates confusion rather than rigor. When students are tested on recall after tools are removed, what is being measured is not learning itself, but adherence to an obsolete epistemology. Such evaluations reward isolation in a world that no longer functions that way.
AI Literacy and AI Fluency
This mismatch has led to an emerging distinction between AI literacy and AI fluency. Literacy refers to a basic understanding of how AI systems function: their architecture, limitations, biases, and risks. It is necessary, but insufficient. Fluency names a more demanding capacity. It involves working with AI through sustained judgment, revision, and refusal.
A fluent user does not treat outputs as answers, but as provisional material. Fluency is enacted through questioning, editing, and contextual decision-making. It does not replace thinking; it redistributes it across a human–machine interaction in which responsibility remains irreducibly human.
Reading and Writing After the Author
This redistribution has direct consequences for how reading and writing should be taught. Reading comprehension has not lost its importance; it has become more exacting. In a world saturated with generated text, understanding now involves discerning relevance, coherence, and reliability under conditions of excess.
Roland Barthes’ notion of the scriptor becomes newly instructive in this context. Unlike the traditional author, the scriptor does not originate meaning from a privileged interiority. Meaning emerges through selection, arrangement, and recombination of existing signs. Writing becomes an act of editing rather than origination, of positioning rather than possession.
Editing, Verification, and Judgment
AI-generated text makes this shift visible. Students must learn to recognize repetition patterns, stylistic smoothing, and conceptual gaps. They must question whether cited works exist, whether quotations are accurate, and whether arguments actually hold together. These practices closely resemble what graduates encounter outside the classroom: editing reports, reviewing documents, evaluating automated drafts.
Teaching students to produce essays in isolation prepares them for a world that no longer exists. Teaching them to interrogate and reshape texts prepares them for the one they already inhabit.
AI as Sparring Partner
The pedagogical role of AI follows from this transformation. Treated as an oracle, AI undermines judgment. Treated as a sparring partner, it strengthens it. Used critically, AI can generate counterarguments, propose alternative framings, or expose unnoticed assumptions. The educational value lies not in the output, but in the interaction.
Students learn by contesting, revising, and sometimes rejecting what the system produces. Authority does not disappear; it relocates. Cognition becomes distributed across tools and processes, while judgment remains a human responsibility.
Conclusion: Responsibility Under Conditions of Abundance
What is at stake, then, is not ethics alone, but ontology. Efforts to preserve academic integrity by prohibiting AI often defend an image of intelligence rooted in presence, originality, and isolation. That image no longer governs how knowledge is produced, circulated, or evaluated beyond the classroom.
The shift from author to scriptor does not signal intellectual decline. It marks a transformation in responsibility. Intelligence today consists less in possessing information than in navigating it. Teaching students how to read, edit, verify, and decide within dense networks of text is not a concession to technology. It is an acknowledgment of the world they are already expected to understand.
References
Barthes, R. (1977). The death of the author. In Image–Music–Text (pp. 142–148). Hill and Wang.
Chomsky, N. (1965). Aspects of the theory of syntax. MIT Press.
Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7
Derrida, J. (1976). Of grammatology (G. C. Spivak, Trans.). Johns Hopkins University Press. (Original work published 1967)
Hutchins, E. (1995). Cognition in the wild. MIT Press.
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing tasks (arXiv:2506.08872) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2506.08872
Comments
Post a Comment