Recognizing the artificial: A comparative voice analysis of AI-Generated and L2 undergraduate student-authored academic essays
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AVZGBB7J8" target="_blank" >RIV/00216208:11320/26:VZGBB7J8 - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216954481&doi=10.1016%2Fj.system.2025.103611&partnerID=40&md5=89ebf07bd9360c5cba262c94e5fb137e" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216954481&doi=10.1016%2Fj.system.2025.103611&partnerID=40&md5=89ebf07bd9360c5cba262c94e5fb137e</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.system.2025.103611" target="_blank" >10.1016/j.system.2025.103611</a>
Alternative languages
Result language
angličtina
Original language name
Recognizing the artificial: A comparative voice analysis of AI-Generated and L2 undergraduate student-authored academic essays
Original language description
Recent developments in AI content generation software have rendered human-authored texts indiscernible from AI-generated ones. This progression introduced challenges in the academic field, raising concerns over the devaluation of academic integrity. As an inevitable aspect of written texts and an indicator of writing identity, authorial voice is a potential distinguishing factor between both text types. Given this, the researchers examined the differences in the authorial voice of student-written and AI-generated essays using 12 student-written academic essays and 12 AI-generated academic essays. The samples were coded and analyzed using Lehman and Sułkowski's (2020) Voice Analytic Rubric. The findings revealed that Collective (C) voice was the dominant voice in student-written essays whereas AI-generators primarily employed Individual (I) voice. Further comparison and closer analysis showed that: a) I-voice is not the consistent dominant textual identity for AI-generated texts; b) AI-generated text is closer to an expert's writing whereas student-written are closer to a novice's; and c) the writing style of AI-generated texts lean toward predictability. These findings contribute to understanding students' authorial voice construction vis-a-vis the relatively underexplored authorial voice of large language systems. © 2025 Elsevier B.V., All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
System
ISSN
0346251X
e-ISSN
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Volume of the periodical
130
Issue of the periodical within the volume
2025
Country of publishing house
US - UNITED STATES
Number of pages
34
Pages from-to
1-34
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85216954481