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Achmad Fawaid
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Lingua Technica: Journal of Digital Literary Studies
Published by Asosiasi Relawan dan Pengelola Jurnal
ISSN : -     EISSN : 31093264     DOI : https://doi.org/10.64595/lingtech
Core Subject : Humanities, Science, Education, Art,
Arts Humanities Computer Science & IT Languange, Linguistic, Communication & Media
This journal covers a wide range of fields, including digital literature, e-poetry, and the relationship between language, literature, and technology in diverse contexts.
Arjuna Subject : Seni dan Humaniora - Sastra dan Teori Sastra
Articles 37 Documents
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Tables of Content Achmad Fawaid
Lingua Technica: Journal of Digital Literary Studies Vol. 1 No. 1 (2025): Foundations of digital literary studies: concepts, textuality, and pedagogical
Publisher : Asosiasi Relawan dan Pengelola Jurnal LPTNU (ARJUNU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64595/p1dx7x16

Abstract

When the machine writes back: generative AI, literary agency, and the reconfiguration of digital authorship Aisultanova Karlygash Abdukalykowna; Syaiful Islam
Lingua Technica: Journal of Digital Literary Studies Vol. 2 No. 2 (2026): Meta-literature: between authorship and machine-mediated literary creativity
Publisher : Asosiasi Relawan dan Pengelola Jurnal LPTNU (ARJUNU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64595/y9tsnr38

Abstract

Background: Generative artificial intelligence has destabilized conventional literary authorship by separating textual production from ownership, responsibility, and institutional recognition across digital publishing environments. Objective: This study examines how human writers, generative systems, editors, publishers, platforms, and cultural institutions redistribute literary agency, attribution, disclosure, and legitimacy. Method: Using a qualitative comparative corpus design, this study analyzed 42 public documents published between 2021 and 11 July 2026, comprising 15 primary, 13 secondary, and 14 contextual sources through distributed-authorship mapping, workflow-chain analysis, and paratextual governance analysis. Results: Human actors retained legal, editorial, and ethical primacy, although machines increasingly occupied visible roles in drafting, stylistic production, and symbolic co-authorship. Literary agency operated across prompting, generation, selection, revision, arrangement, performance, and publication rather than residing exclusively in sentence production. Disclosure functioned constitutively, legitimizing experimentation in some contexts while enabling suspicion, restriction, or prohibition in others. Implication: Digital authorship should therefore be evaluated as a governed attribution network in which distributed creativity remains asymmetrical because accountability continues to rest with identifiable human and institutional actors. Novelty: This study integrates literary texts, creator accounts, publisher paratexts, media reception, platform rules, and policy documents within one auditable framework connecting attribution, workflow, and governance across contemporary transnational literary ecosystems.
Prompting as poetic practice: human–AI collaboration and the making of algorithmic literary voice Muhammad Khairul Umam
Lingua Technica: Journal of Digital Literary Studies Vol. 2 No. 2 (2026): Meta-literature: between authorship and machine-mediated literary creativity
Publisher : Asosiasi Relawan dan Pengelola Jurnal LPTNU (ARJUNU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64595/kz2knf55

Abstract

Background: Generative language models have transformed poetry writing into an iterative practice in which prompts, outputs, revisions, interfaces, and platforms jointly shape literary production. Objective: This study examines how prompting functions poetically, how creative agency is negotiated during human–AI collaboration, and how algorithmic literary voice emerges across documented digital writing practices. Method: A qualitative multiple-case corpus design analyzed 34 verified public documents, 41 text units, and 41 source-linked coding units through a Prompt–Rhetoric Coding Matrix, Human–AI Negotiation Sequence Analysis, and Algorithmic Literary Voice Profile. Results: Dialogic and evaluative prompting formed the largest prompting configuration, indicating that poetic composition depended on reformulation, assessment, and constraint rather than single-turn instruction. Human-directed control remained prominent across interaction sequences, although automation, platform mediation, and distributed participation produced several asymmetrical forms of collaboration. Hybrid and human-curated voice profiles exceeded purely model-conditioned patterns, demonstrating that literary voice developed through combined traces of prompting, editing, stylistic recurrence, attribution, code, and circulation. Implication: These findings reposition authorship as a traceable distribution of compositional decisions rather than a binary division between human and machine production. Novelty: This study integrates prompt rhetoric, interactional agency, and platform-mediated stylistics within one auditable framework for analyzing AI-assisted poetry in contemporary digital literary culture.
The style that was learned: large language models, imitation, and the ethics of literary influence Nomvuselelo N. Nxumalo; Seroja Ainun Nadhifah
Lingua Technica: Journal of Digital Literary Studies Vol. 2 No. 2 (2026): Meta-literature: between authorship and machine-mediated literary creativity
Publisher : Asosiasi Relawan dan Pengelola Jurnal LPTNU (ARJUNU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64595/80qxyz22

Abstract

Background: Large language models have transformed literary imitation into a scalable practice, complicating distinctions among stylistic influence, textual reproduction, authorship, and ethical appropriation. Objective: This study examines how author-conditioned generation produces stylistic convergence, preserves or transforms source relations, and acquires ethical legitimacy under documented research conditions. Method: This study applies paired stylometry, intertextual reproduction auditing, and an ethical literary influence matrix to ten public-domain literary excerpts, ten controlled model outputs, five methodological documents, and seven contextual sources. Results: Stylometric comparison reveals graded convergence, with recognizable authorial cues coexisting with substantial divergence in sentence architecture and other distributed features. Reproduction auditing identifies low lexical overlap, no shared four-grams, no source-character or source-event transfer, and consistently high transformation across all pairs. Ethical coding classifies risk as low because sources are public domain, authors are deceased, prompts and exemplars are disclosed, and human control is documented. Implication: These findings indicate that literary resemblance should not be treated automatically as authorship, memorization, plagiarism, or infringement, but evaluated through layered textual and contextual evidence. Novelty: This study advances a multidimensional framework that theorizes learned style as mediated literary influence shaped by convergence, transformation, provenance, and accountable use across computational, literary, legal, and contemporary ethical domains.
Authorship after automation: originality, attribution, and creative responsibility in AI-assisted literary production Lam H. Tran
Lingua Technica: Journal of Digital Literary Studies Vol. 2 No. 2 (2026): Meta-literature: between authorship and machine-mediated literary creativity
Publisher : Asosiasi Relawan dan Pengelola Jurnal LPTNU (ARJUNU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64595/tcjxnn53

Abstract

Background: Generative artificial intelligence has unsettled conventional literary authorship by separating textual production from creative control, public attribution, and accountability across writers, platforms, publishers, and institutions. Objective: This study examines how originality, credit, disclosure, and responsibility are configured in publicly documented cases and governance materials concerning AI-assisted literary production. Method: A qualitative multiple-case documentary analysis was conducted on 27 verified public records, comprising primary, secondary, and contextual sources coded through matrices of creative control, attribution–disclosure alignment, and responsibility. Results: Stronger authorship claims appeared where human actors directed narrative purpose, selected alternatives, transformed generated material, and authorised publication. Attribution was most credible when disclosure specified the extent, function, timing, and audience of AI involvement, whereas delayed, private, or absent disclosure weakened correspondence between contribution and credit. Responsibility was distributed across authors, publishers, platforms, professional bodies, and model providers according to their control over production, classification, access, remuneration, and enforcement. Implication: Literary governance should align authorship claims and accountability obligations with demonstrable contribution, institutional capacity, and remedial power rather than with bylines or tool use alone. Novelty: This study offers an integrated, process-based framework that distinguishes textual generation, transformative originality, attributional transparency, and layered creative responsibility within one comparative, publicly available corpus.
Reading synthetic fiction: narrative coherence, aesthetic judgment, and reader trust in AI-generated literature Devita Septian Dwi Hidayati
Lingua Technica: Journal of Digital Literary Studies Vol. 2 No. 2 (2026): Meta-literature: between authorship and machine-mediated literary creativity
Publisher : Asosiasi Relawan dan Pengelola Jurnal LPTNU (ARJUNU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64595/yhrhqe47

Abstract

Background: AI-generated fiction has become increasingly readable, yet its literary acceptance depends on more than fluency, because readers must also perceive coherent organization, aesthetic purpose, and trustworthy narrative agency across platforms, classrooms, publishing experiments, and increasingly ordinary digital reading environments worldwide today. Objective: This study investigates how narrative coherence, aesthetic judgment, and reader trust interact in the reception of synthetic fiction. Method: This study applies a qualitative-dominant comparative design to twelve English-language AI-generated short stories, combining a six-dimensional coherence matrix, a six-dimensional aesthetic rubric, and a six-dimensional trust framework with preserved reader-ranking metadata. Results: Most narratives sustained event sequence, temporal continuity, entity stability, and closure, although causal linkage remained comparatively uneven. Aesthetic evaluation was weaker than coherence, particularly in emotional resonance, interpretive depth, and imagery, while originality and stylistic control varied across models. Reader trust was strongest at the level of narrative reliability but declined when authenticity, intentionality, literary legitimacy, continuation, and recommendation were considered. Implication: These findings indicate that readable synthetic fiction may remain only conditionally literary when formal coherence is not accompanied by aesthetic distinction and perceived purpose. Novelty: This study advances an integrated reader-centered framework that separates intelligibility, literary valuation, and trust while preserving their analytical interdependence.
The Algorithmic muse reconsidered: creativity, constraint, and the politics of machine co-authorship Inero Valbuena Ancho; Ahmad Dafa Asyaddad
Lingua Technica: Journal of Digital Literary Studies Vol. 2 No. 2 (2026): Meta-literature: between authorship and machine-mediated literary creativity
Publisher : Asosiasi Relawan dan Pengelola Jurnal LPTNU (ARJUNU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64595/2h40cg81

Abstract

Background: Generative artificial intelligence has transformed literary production across publishing, education, cultural industries, and digital platforms while intensifying disputes over creativity, attribution, ownership, accountability, and platform power. Objective: This study examines how creative agency, constraint regimes, and authorship governance are configured across contemporary machine co-authorship discourse and what these configurations reveal about institutional legitimacy. Method: Using qualitative comparative documentary analysis, this study coded forty publicly accessible records comprising thirteen primary documents, seventeen secondary sources, and ten contextual materials published or current between 2021 and 2026 through explicit decision rules and evidence notes. Results: Creative agency was most frequently represented as an integrated human–machine–platform chain combining initiation, prompting, generation, revision, and mediation. Institutional constraints dominated the corpus, while aesthetic, technical, legal, economic, and data-related conditions operated as enabling, limiting, or simultaneously ambivalent forces. Human accountability and platform power were substantially more prevalent than contractual output ownership, indicating that creative participation, legal authorship, and institutional control remain unevenly distributed. Implication: Legitimate machine co-authorship requires transparent contribution, proportionate attribution, human editorial responsibility, accountable training practices, and governance capable of protecting creative diversity. Novelty: This study contributes an integrated framework that connects distributed agency, creativity-under-constraint, and authorship politics within a single document-level analytical architecture.

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