Artículo 1

He holds an MA in English teaching and a PhD in English language. He has authored and edited several

books, book chapters, and research articles on Globality Studies, Hybridity studies, ELT materials, English

language teaching/learning, genre analysis, and assessment. Senior Fellow of the Higher Education Academy

(SFHEA), Assistant Professor of English, and Head of the English Department at the Faculty of Arts and Humanities

Kairouan, Tunisia.

DOI: https://doi.org/10.61604/dl.v16i29.377

Using Genre Analysis to Detect AI-

Generated Academic Texts

Uso del Análisis de Género para Detectar

Textos Académicos Generados por IA

ISSN: 1996-1642

e-ISSN: 2958-9754

Año 16, N° 29, julio-diciembre 2024 pp. 9-27

Revista de Educación

Universidad Don Bosco - El Salvador

Mimoun Melliti

University of Kairouan, Tunisia

Correo: mimoun_melliti@yahoo.com,

ORCID: https://orcid.org/0000-0002-2791-4051

Recibido: 07 de julio de 2024

Aceptado: 14 de septiembre de 2024

Para citar este artículo: Melliti, M. (2024). Using Genre Analysis to Detect AI-Generated Academic Texts,

Diá-logos, (29), 9-27

Nuestra revista publica bajo la Licencia

Creative Commons: Atribución-No

Comercial-Sin Derivar 4.0 Internacional

10.

Resumen

Este estudio investiga las características distintivas

entre resúmenes escritos por humanos y resúmenes

generados por inteligencia artificial mediante

técnicas de análisis de género. La investigación

examina resúmenes tipo mini-memoria elaborados

por estudiantes de Segundo Año de Máster en

Inglés (MA2) en la FLSH Kairouan y los compara

con resúmenes generados por IA utilizando

el Chat Generative Pre-trained Transformer 3

(ChatGPT). El análisis se centra en la recurrencia

de las funciones del texto, específicamente en

la frecuencia y calidad de elementos como

las declaraciones de propósito, metodología,

resultados y contextualización. Los hallazgos

revelan que los resúmenes escritos por humanos

presentan una presentación más equilibrada y

detallada, destacando la contextualización y

los resultados comprensivos, mientras que los

resúmenes generados por IA tienden a priorizar

declaraciones de propósito claras y explícitas,

con menos profundidad en los resultados y la

información contextual. El estudio propone métodos

avanzados de detección, incluyendo herramientas

mejoradas de análisis de texto y evaluaciones de

contextualización, para diferenciar el contenido

generado por IA. También destaca la necesidad

de una formación específica para docentes y

criterios de evaluación rigurosos para mantener la

integridad académica y abordar los desafíos que

plantea la IA en la redacción académica.

Palabras clave

Análisis de género, textos generados por IA,

resumenes académicos, comparación humano-IA.

Abstract

This study investigates the distinguishing

characteristics between human-written and AI-

generated abstracts through genre analysis

techniques. The research examines mini-memoir

abstracts authored by Second Year Master in English

(MA2) students at FLSH Kairouan and compares

them to AI-generated abstracts created using Chat

Generative Pre-Trained Transformer 3 ChatGPT.

The analysis focuses on text function recurrence,

specifically the frequency and quality of elements

such as purpose statements, methodology,

results, and contextualization. Findings reveal

that human-written abstracts exhibit a more

balanced and detailed presentation, emphasizing

contextualization and comprehensive results, while

AI-generated abstracts tend to prioritize clear

and explicit purpose statements with less depth

in results and contextual information. The study

proposes advanced detection methods, including

enhanced text analysis tools and contextualization

assessments, to differentiate AI-generated content.

It also highlights the need for targeted teacher

training and rigorous assessment criteria to uphold

academic integrity and address the challenges

posed by AI in scholarly writing.

Keywords

Genre analysis, AI-generated texts, academic

abstracts, Human-AI Comparison.

Introduction

The software TurnItIn is widely known for its role in detecting plagiarism by analyzing

textual content submitted by students against a vast database of academic papers

and online sources. Similarly, this study employs genre analysis techniques to determine

the distinctive features between human-written and AI-generated texts. Just as TurnItIn

scrutinizes linguistic patterns and semantic similarities to identify potential instances of

plagiarism, GenreItIn scrutinizes the generic structures and stylistic nuances of abstracts

in an attempt to distinguish between content authored by humans and that generated

by AI. GenreItIn is a name assigned by the researcher to the process of identifying

similar linguistic and stylistic structures in the generated texts and the human-written

texts.

Genre analysis offers a lens through which to examine the complexities of written

communication. Rooted in the exploration of textual conventions and structures, genre

analysis provides a systematic framework for understanding how different genres

function within specific social and communicative contexts (Bhatia, 1993; Swales,

1990). Within this scholarly discourse, the distinction between human-written and AI-

generated content has emerged as a topic of increasing significance, particularly

in light of advancements in artificial intelligence and natural language processing

technologies.

Using Genre Analysis to Detect AI-

Generated Academic Texts

11.

Genre analysis has become an important tool in understanding and teaching

discourse, significantly impacting literacy education globally. This approach provides

applied linguists with a socially informed theory of language and a pedagogical

framework grounded in research on texts and contexts (Kessler & Polio, 2023). Recent

studies have focused on understanding the integrity and variation within genres, exploring

their internal structures and social processes (Darvin, 2023). These studies highlight the

importance of contexts, lexico-grammatical features, and rhetorical patterns. In light

of using genre analysis to detect AI-generated academic texts, these insights become

central (Melliti, 2024; Sárdi, 2023). AI-generated texts often mimic the surface features

of human writing but may lack the deeper rhetorical patterns and contextual details

inherent in human-authored genres. Through using genre analysis, educators and

researchers can develop tools to identify these discrepancies, ensuring the integrity of

academic writing. This addresses the challenge of distinguishing AI-generated content

and reinforces the importance of teaching genre-specific literacy skills in classrooms,

thereby enhancing critical literacy and language education.

The present study seeks to contribute to this evolving discourse by undertaking a

comprehensive investigation into the genre characteristics of human and AI-generated

abstracts (Swales, 1990; Bhatia, 1993). The selection of mini-memoir abstracts, authored

by MA2 students at FLSH Kairouan, serves as the primary corpus for analysis. These

abstracts, spanning diverse topics within the domains of linguistics, literature, culture

studies, and discourse analysis, provide rich material for exploring the genre conventions

inherent in human-authored texts.

AI-generated abstracts for the same topics are generated using ChatGPT—a

sophisticated AI tool capable of natural language generation. This digital collaborator,

provided with the capacity to mimic human language patterns, offers a unique lens

through which to examine the genre characteristics of machine-authored texts.

The analytical framework employed in the first phase of this study draws upon Melliti’s

(2016) Research Letter Introduction Model, which provides a systematic methodology

for identifying the generic structure of research abstracts. Through manual analysis,

the present study explores the syntactic, semantic, and rhetorical features of both

human and AI-generated abstracts, clarifying the underlying patterns that distinguish

between the two. The researcher allowed for other keys that emanate directly from the

corpus to be identified. In the second phase, the researcher employed a comparative

approach, focusing on analyzing human written abstracts and AI-generated ones from

three main aspects: Language Complexity, Writing Style, and Discourse Organization.

Literature review

Genre Analysis in Academic Writing

Genre analysis is a significant approach to understanding the structure and

conventions of academic writing. The concept of genre analysis, as introduced by

Swales (1990) provides a framework for examining how different genres function

within specific social and communicative contexts. Swales’ work is foundational in this

field, emphasizing the importance of genre as a social construct and exploring how

academic genres serve communicative purposes.

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12.

Swales (1990) defines genre as “a class of communicative events” that share

common features and fulfill specific functions within a community (p. 58). This definition

highlights the role of genre in shaping academic writing practices. His model of genre

analysis includes the identification of moves and steps that are characteristic of specific

genres. For instance, the Introduction section of academic papers typically involves

moves such as establishing a research territory, identifying a niche, and occupying that

niche (Swales, 1990).

Swales’s (1990) work emphasizes the significance of understanding the rhetorical

structures and social contexts of academic genres. His ideas have paved the way

for incorporating genre analysis into language teaching, providing a framework for

developing genre-specific literacy skills. In the context of detecting AI-generated

academic texts, Swales’s (1990) genre analysis principles become particularly relevant.

AI-generated texts often replicate the structural aspects of academic writing but may

fall short in capturing the specific rhetorical strategies and social contexts that human

writers inherently incorporate. Teachers and researchers can identify these subtle

differences by applying Swales’s (1990) genre analysis, enhancing the ability to detect

AI-generated content and ensuring the authenticity of academic writing. This reinforces

the importance of genre-based pedagogy in language education, fostering critical

literacy and effective communication skills. This approach aligns with the APA’s (2023)

policy, which emphasizes the necessity of transparent and ethical use of AI-generated

content in academic work, reinforcing the importance of genre-based pedagogy

in language education. Through adhering to these guidelines, educators can foster

critical literacy and effective communication skills in their students.

Bhatia (1993) further extends this analysis by examining the rhetorical structures of

academic genres. Bhatia (1993) provides a detailed examination of how professional

genres, including research articles and abstracts, are constructed to meet the needs

of their audiences. He highlights that academic genres are not static but evolve in

response to changes in disciplinary practices and communication technologies.

Bhatia’s (1993) work is instrumental in understanding the dynamics of academic writing

genres and their role in scholarly communication.

Genre analysis has also been applied to the study of academic abstracts. According

to Hyland (2000), academic abstracts often follow a move-based structure that

includes identifying the purpose of the study, describing the methodology, summarizing

the results, and discussing the implications. Hyland’s research emphasizes the formulaic

nature of abstracts, which helps readers quickly grasp the essence of the research.

Moves and Steps in Academic Genres

Understanding the moves and steps in academic genres is important for analyzing

the structure of scholarly texts. Swales (1990) introduces the concept of “moves” as the

communicative actions that authors use to fulfill the purpose of a genre. In research

articles, the Introduction typically includes moves such as establishing a research

territory, presenting a review of previous research, and stating the research gap or

problem (Swales, 1990).

Further research by Bhatia (1993) elaborates on the concept of “steps,” which are

sub-units within moves that contribute to the overall communicative function. In the

case of research abstracts, moves include providing background information, stating

Using Genre Analysis to Detect AI-

Generated Academic Texts

13.

the research purpose, outlining the methodology, presenting the results, and discussing

the conclusions (Bhatia, 1993). This structured approach ensures that abstracts convey

essential information succinctly.

Additionally, studies on academic writing have identified common move structures

in different genres. For example, the “IMRaD” structure (Introduction, Methods, Results,

and Discussion) is widely used in empirical research articles. According to Oshima

and Hogue (2006), each section of the IMRaD structure serves a specific function: the

Introduction provides background and states the research problem, Methods describe

the procedures, Results present the findings, and Discussion interprets the results and

their implications.

The move-based approach to genre analysis allows for a systematic examination

of how academic texts are organized and how they communicate their intended

messages. Researchers can better analyze both human-written and AI-generated texts

by understanding these structures.

The Challenges of AI in Scholarly Publications

Artificial Intelligence (AI) has made significant strides in natural language processing

and text generation. Tools like GPT-3, developed by OpenAI, have demonstrated the

ability to produce coherent and contextually relevant text across various domains

(Brown et al., 2020). However, the integration of AI in scholarly publications presents

several challenges, particularly in terms of maintaining academic integrity and ensuring

the authenticity of scholarly work.

One of the primary challenges is distinguishing between human-written and AI-

generated content. Research highlights that AI-generated texts often exhibit certain

characteristics, such as repetitive phrasing and lack of depth in contextualization

(Logacheva et al., 2024). These features can be attributed to the algorithms used in

training AI models, which may prioritize coherence and clarity over understanding and

originality.

GPT-3 generates text based on patterns learned from vast amounts of training data.

While tools like this can produce text that mimics human writing, they cannot often

engage deeply with subject matter or integrate previous research in a meaningful

way (Javaid et al., 2023). This limitation poses a challenge for AI-generated content in

academic contexts, where thorough contextualization and critical engagement with

existing literature are needed.

Furthermore, the use of AI in academic writing raises concerns about authorship

and originality. The increasing use of AI tools in generating academic content blurs the

lines between human and machine authorship (Draxler et al., 2024). This shift raises

questions about the ethical implications of AI-generated research and the potential

impact on the credibility of scholarly publications.

The challenge of AI in scholarly publications is compounded by the need for effective

identification methods. Research on this emphasizes the importance of developing

sophisticated tools to identify AI-generated content (Elkhatat et al., 2023). These

tools should focus on detecting patterns and features that are indicative of machine

authorship, such as repetitive structures and lack of depth in analysis. Combining text

analysis algorithms with human judgment can enhance the accuracy of AI content

detection (Yang et al., 2024).

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14.

Combining text analysis algorithms with human judgment creates a robust

framework for detecting AI-generated content in academic writing. Text analysis

algorithms, powered by machine learning and natural language processing (NLP), can

efficiently analyze vast amounts of text, identifying patterns and anomalies indicative

of AI-generated content (Basta, 2024). These algorithms can detect inconsistencies in

writing style, unusual syntax, and repetitive language use that may signal automated

text generation. However, algorithms alone may struggle with rhetorical strategies,

cultural context, and the subtlety of genre-specific conventions, which are critical

for producing genuinely coherent and contextually appropriate academic writing

(Sidorkin, 2024). Indeed, the challenge algorithms face in handling rhetorical strategies,

cultural contexts, and genre-specific conventions highlights the importance of human

expertise in academic writing. This also shows the value of integrating genre analysis

and critical literacy into language education, ensuring that academic writing remains

not only technically accurate but also rhetorically and culturally appropriate. Addressing

these complexities, the present paper aims, interalia, to bridge the gap between AI-

generated content and the demands of scholarly communication.

Human judgment plays a central role in complementing these algorithms by bringing

in-depth knowledge of academic conventions, genre-specific expectations, and the

ability to interpret context beyond surface-level text analysis. Experts in academic writing

can recognize the intricacies of rhetorical moves, the purpose behind specific writing

choices, and the appropriateness of content within its academic discipline (Zhang,

2023). This human insight is essential for identifying whether a text merely mimics the

form of academic writing or genuinely engages with the content meaningfully. Through

integrating human expertise, it is possible to enhance the detection process, ensuring

that AI-generated content is not only identified based on stylistic anomalies but also

on deeper levels of content engagement and rhetorical coherence (Garib & Coffelt,

2024).

The employment of text analysis procedures and human judgment provides a

comprehensive approach to maintaining academic integrity (Gupta, 2024). automatic

procedures offer the speed and scalability needed to screen large volumes of text,

providing preliminary assessments that highlight potentially AI-generated content.

These flagged texts can then undergo detailed scrutiny by human experts, who can

make informed decisions based on their understanding of academic genres and

rhetorical practices. This combined approach also supports continuous improvement

in AI detection tools, as human feedback can refine and enhance automatic models.

Ultimately, investing both technological and human resources ensures a more accurate

and reliable detection process, preserving the authenticity and integrity of academic

writing in an era of increasingly sophisticated AI text generation (Dergaa et al., 2023).

The present paper seeks to analyze AI and human writing and provide strategies to deal

with the challenges related to them.

Genre analysis offers valuable insights into the structure and conventions of

academic writing, highlighting the importance of moves and steps in conveying

scholarly messages. However, the integration of AI in academic writing presents

significant challenges, including issues related to authenticity, originality, and effective

detection. Addressing these challenges requires a multifaceted approach, combining

advanced detection tools with a deeper human understanding of genre-specific

structures and patterns.

Using Genre Analysis to Detect AI-

Generated Academic Texts

15.

Procedure, analysis, and discussion

Procedure to collect the data

The researcher selected 10 mini-memoir abstracts spanning the period from

September 2022 to April 2024. These mini-memoir abstracts, written by MA2 students

at FLSH Kairouan, explore a diverse array of subjects, including linguistics, literature,

culture studies, and discourse analysis. The researcher engaged an advanced AI tool,

ChatGPT-3, to generate another set of mini-memoir abstracts for the same thematic

domains, ensuring a comprehensive comparative analysis. To do so, the topics of

the mini-memoirs were inserted in ChatGPT chat bar, and the researcher asked it to

generate an abstract of a mini-memoir.

Subsequently, each abstract—whether human-written or AI-generated—underwent

meticulous textual scrutiny by the researcher. This approach was favored over automated

methods due to the nuanced nature of genre analysis. Unlike software, human analysts

possess the cognitive intelligence necessary to determine the subtle intentions behind

each sentence (referred to by the letter S in the figure below). The researcher tried

to meticulously dissect the abstracts and was able to unveil their underlying generic

structures, drawing upon the methodological framework elucidated by Melliti (2016) in

the creation of a Research Letter Introduction Model.

Figure 1

Create a Research Letter Model (CARL Model).

Note: the capital ‘S’ stands for ‘Sentence’.

The selection of Melliti’s (2016) Research Letter Introduction Model for this study is

not arbitrary; rather, it is strategically aligned with the characteristics of the mini-memoir

genre. The rationale behind this choice lies in the inherent similarities between the

research letter and the mini-memoir, both of which serve as condensed versions of

their respective longer counterparts within academia.

Introduction

Introducing

Phase (IP): 3 S

Contextualizing

Phase (CP): 5 S

Finding Phase

(FP): 4 S

Background Information (BI)* 1 S

Previous Research (PR)** 1 S

Previous Research/Background Information (PR/BI)* 1 S

Results (R)* 2 S

Conclussion (C)* 1 S

Results/Conclussion (R/C)** 1 S

Identification of Gap (IG)* 1 S

Purpose of Study (PS)* 1S

Rationale for Study (RS)** 1 S

Methodology (ME)** 1S

Previous Research/Identification of Gap (PR/IG)** 1 S

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16.

Analysis of the recurrence of keys in the AI generated vs. human-

written abstracts

As shown in Table 1, in the AI-generated set of abstracts, "Purpose of Study" (PS) is the

most dominant text function, accounting for over half of the mentions (50.45%). This

high percentage indicates a strong emphasis on clearly articulating the purpose of the

research. The prominence of PS in these abstracts suggests that the primary objective

is to ensure that readers immediately understand the research's aims. The table below

identifies the recurrence of keys in the AI generated vs. human written abstracts.

The research letter, as a brief form of the traditional research article genre,

encapsulates key elements of a scholarly investigation within a concise framework.

Similarly, the mini-memoir, serving as a condensed version of the MA thesis, distills

the essence of the research endeavor into a shorter format without compromising

its scholarly rigor. Both genres share the common trait of briefness, reflecting a

streamlined approach to presenting academic insights while retaining the essence of

scholarly inquiry. Moreover, the study employed a model tailored to the research letter

genre, which makes it align itself with established academic conventions, ensuring

methodological coherence and comparability with existing scholarly frameworks.

Table 1

Recurrence of keys in the AI generated vs. Human Written abstracts.

Text Function Sample AI generated sentences

“Metaphor has been central in

linguistic and literary studies since

Aristotle’s time.”

Background

Information (BI)

Human

Count

Percentage

Count

Human

Percentage

109.91%11 12.66%

“Previous studies have shown

that women use more mitigated

speech acts than men.”

Research (PR)

160.90%1 20.25%

“There is little research on how

children acquire pragmatic

competence in multilingual

settings.”

Identification of

Gap (IG)

79.91%11 8.86%

“This study aims to explore

how digital media influences

contemporary literature.”

Purpose of Study

(PS)

1150.45%56 13.92%

“A mixed-methods approach was

used, combining content analysis

with questionnaires”

Methodology

(ME)

1619.82%22 20.25%

“Metaphors were more frequent in

emotionally intense texts.”

Results (R)

100.00%0 12.66%

“This study shows how cultural

context shapes narrative structure.”

Conclusion (C)

69.01%10 7.59%

“The findings show that discourse

markers enhance narrative

cohesion, confirming their

important role in improving listener

comprehension.”

Results/

Conclusion (R/C)

10.00%0 1.27%

“The hypothesis is that code-

switching marks social identity in

bilingual communities.”

Hypothesis (H) 20.00%0 2.53%

Using Genre Analysis to Detect AI-

Generated Academic Texts

17.

In contrast, the human-written set of abstracts also frequently includes PS but to a

lesser extent (13.92%). This indicates that while stating the purpose remains central, it is

not as overwhelmingly dominant. The reduced emphasis on PS in the human-written set

could suggest a more balanced approach to abstract writing, where other elements

such as methodology, results, and previous research are given more prominence.

"Methodology" (ME) appears consistently in both sets, with its presence slightly higher

in the human-written set (20.25%) compared to the AI-generated set (19.82%). This

consistency emphasizes the importance of detailing the methodological approach

in both AI-generated and human-written abstracts. The slight increase in the human-

written set might indicate a greater focus on the research process and techniques

used, potentially reflecting a detailed or methodologically rigorous approach.

"Previous Research" (PR) shows a significant difference between the two sets. In the

human-written set, PR is mentioned frequently (20.25%), whereas in the AI-generated set,

it is scarcely mentioned (0.90%). This substantial difference suggests that the human-

written set places a greater emphasis on situating the current study within the context of

existing research. This contextualization is important for establishing the relevance and

originality of the research, and its higher recurrence in the human-written set may reflect

a more thorough integration of literature review elements.

Both sets include mentions of "Background Information" (BI) and "Identification of

Gap" (IG) with relatively similar frequencies. In the AI-generated set, BI and IG both have

a recurrence of 9.91%, while in the human-written set, BI is at 12.66% and IG at 8.86%.

This indicates a consistent need across both sets to provide context and highlight

the research gap. The slight increase in BI in the human-written set might reflect a

more comprehensive introduction to the research topic, while the levels of IG suggest

a shared emphasis on identifying and addressing gaps in existing knowledge. The

higher percentage of IG in AI-generated abstracts could indeed indicate a deliberate

focus on highlighting research gaps, which might be particularly beneficial for novice

writers who often struggle with this aspect of academic writing. This suggests that AI

tools may be excelling in reinforcing the importance of clearly articulating research

gaps, potentially serving as a valuable aid in academic writing. However, it also raises

questions about the balance between AI-generated content and the development of

human writers' skills, especially in areas where novice writers typically face challenges.

The human-written set of abstracts includes more detailed reporting of "Results" (R)

and "Conclusion" (C), with R at 12.66% and C at 7.59%, compared to the AI-generated

set which has no separate mention of results and only 9.01% for conclusions. This

difference suggests that the human-written set provides more comprehensive reporting

on the outcomes of the research. The presence of distinct mentions of results in the

human-written set indicates a clear delineation of findings, which is essential for

understanding the research's impact and contributions.

The human-written set includes mentions of "Hypothesis" (H) and "Results/Conclusion"

(R/C), which are not present in the AI-generated set. This indicates a broader range of

text functions in the human-written set, potentially reflecting a more detailed or varied

abstract structure. When identifying steps within genres, it is essential to consider what

genre analysts refer to as the propensity for innovation. Members of genre communities

often introduce new elements, which may or may not be validated by expert members

(Bhatia, 1993). The inclusion of H suggests that some abstracts explicitly state the

research hypothesis, while R/C indicates a combination of results and conclusions in

some cases. These unique mentions highlight the human-written set's diverse approach

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18.

to structuring abstracts, incorporating elements that provide a more holistic view of the

research.

The recurrence patterns observed in both sets of abstracts reflect predictable

structures typical of academic writing. Genre analysis reveals that despite the differences

in recurrence, both AI-generated and human-written abstracts adhere to established

conventions of presenting research. Both types of abstracts consistently include key text

functions such as the purpose of the study, background information, identification of

research gaps, methodology, and conclusions. This predictability supports the idea that

academic abstracts follow a genre-specific structure that can be identified through the

recurrence of these text functions. The structured nature of these abstracts ensures that

essential information is communicated clearly and efficiently, meeting the expectations

of the academic community.

The higher recurrence of "Purpose of Study" (PS) in the AI-generated set and the

balanced distribution of text functions in the human-written set highlight differences

that can be attributed to the potential variation in abstract conceptualization. AI-

generated abstracts may emphasize clarity and purpose, while human-written ones

might incorporate more contextual and methodological details. This distinction

suggests that AI-generated abstracts might prioritize straightforward communication of

the research aim, whereas human-written abstracts might strive for a more balanced

and comprehensive presentation.

Therefore, the analysis of text function recurrence in AI-generated and human-

written abstracts demonstrates that both types share common structural elements while

also exhibiting distinct features. Identifying these differences using genre analysis is

feasible, as the generic structure of academic abstracts is predictable. The recurrence

patterns provide insights into how each type of abstract prioritizes different aspects of

research presentation, reflecting both shared conventions and unique characteristics

of their respective creation processes. While AI-generated and human-written abstracts

adhere to similar genre conventions, they differ in their emphasis and distribution of text

functions. These differences can be systematically identified and analyzed, contributing

to our understanding of how abstracts are constructed and the potential impact of AI

in academic writing.

The findings have direct implications for detecting AI-generated content in students'

writing. Through understanding the structural differences and the recurrence patterns

of various text functions, teachers and content detection tools can develop more

sophisticated methods to identify AI-generated text. Based on the findings of this study,

key indicators include:

• High Frequency of Purpose Statements: a higher-than-usual recurrence of

"Purpose of Study" statements may suggest AI-generated content, as AI tends to

prioritize clear and explicit objectives.

• Lack of Detailed Results and Conclusions: AI-generated texts might

underrepresent detailed results and conclusions, focusing more on the study's

purpose and methodology.

• Less Contextualization: AI-generated content might lack the thorough

contextualization seen in human-written abstracts, particularly the integration of

previous research.

Therefore, teachers and content detection tools can develop more sophisticated

methods to identify AI-generated text by focusing on certain key indicators. As AI-

Using Genre Analysis to Detect AI-

Generated Academic Texts

19.

generated content becomes more prevalent, distinguishing it from human-written text

requires attention to specific patterns and characteristics typical of AI writing.

One of the stamps of AI-generated content is the high frequency of explicit "Purpose

of Study" statements. AI models often prioritize clarity and explicit objectives, leading

to a greater-than-usual recurrence of these statements within the text. For instance,

phrases like "The purpose of this study is..." or "This research aims to..." might appear

more frequently in AI-generated content compared to human-written text. This is

because AI algorithms are designed to ensure that the objectives of the text are clear

and unambiguous, which can result in repetitive and formulaic expressions of purpose.

The lower frequency of explicit 'Purpose of Study' statements in human-written abstracts

might reflect a preference for subtlety and integration of the study’s objectives into the

narrative flow. This suggests that while AI-generated content emphasizes clarity through

repetition, human-authored texts might achieve communicative goals more implicitly.

For novice writers, this contrast could indeed be instructive. The explicitness seen

in AI-generated texts might serve as a model for ensuring clarity and directness.

However, it also highlights the importance of developing the skill to convey purpose

in a sophisticated and contextually appropriate manner, which is often seen in more

advanced academic writing.

Additionally, as the findings indicate, AI-generated texts might exhibit a noticeable

underrepresentation of detailed results and conclusions. While AI is proficient at

generating content that outlines the study's purpose and methodology, it often

falls short in providing the comprehensive details typically found in the results and

conclusions sections. Human authors tend to elaborate extensively on their findings,

discussing implications, limitations, and future directions. In contrast, AI-generated

content may offer more superficial summaries, lacking the depth and critical analysis

that characterize human scholarly writing.

Another distinguishing feature of AI-generated content identified in this study is its

tendency to lack thorough contextualization, particularly the integration of previous

research. Human-written abstracts and research papers usually provide a rich

background, situating the current study within the broader context of existing literature.

This involves citing relevant studies, discussing their findings, and explaining how the

current research builds upon or diverges from past work. AI-generated texts, however,

may provide more generic or surface-level context, failing to deeply engage with

previous research. This results in a less robust and interconnected discussion of the

topic.

To effectively identify AI-generated content, teachers and detection tools can

develop methods that invest these key indicators. For example:

• Text Analysis Software: tools can be designed to scan for high frequencies

of specific phrases and structures associated with purpose statements. The

software can flag potential AI-generated content by analyzing the text for

repetitive patterns. Existing text analysis software, such as Turnitin, Grammarly,

and Copyscape, are examples of tools that can be adapted to scan for high

frequencies of specific phrases and structures, particularly those associated

with purpose statements. Turnitin, primarily used for plagiarism detection, could

be enhanced to identify repetitive patterns indicative of AI-generated content.

Grammarly, which analyzes text for grammar and style, can also be trained

to flag unusually frequent occurrences of certain phrases. Copyscape, a tool

Diá-logos – Año 16, N° 29, julio-diciembre 2024

20.

for detecting duplicate content, could similarly be adapted to recognize the

repetitive patterns that suggest AI authorship. These tools make use of advanced

algorithms to analyze text, which render them effective in identifying potential

AI-generated content by detecting patterns and irregularities in writing.

• Contextualization Assessment: advanced algorithms can be used to evaluate

the depth of contextualization in the text. These tools can compare the integration

of previous research in the document against a database of human-written texts

to assess whether the content meets the typical standards of scholarly writing. To

assess the depth of contextualization in texts, advanced algorithms such as TF-

IDF, Citation Network Analysis, Latent Dirichlet Allocation (LDA), BERT for Sentence

Embeddings, ROUGE metrics, and Cosine Similarity can be utilized. These tools

compare the integration of previous research in a document against a database

of human-written texts. By analyzing key terms, citation patterns, thematic

structures, sentence contexts, recall of key phrases, and overall textual similarity,

these algorithms help detect AI-generated content by identifying discrepancies

in how well the text incorporates and contextualizes existing research, ensuring it

meets typical scholarly standards.

• Detailed Results and Conclusions Check: detection tools can be programmed

to look for the presence and quality of detailed results and conclusions. The tools

can identify discrepancies that may indicate AI generation by comparing the

level of detail in these sections to known human-authored works.

• Training and Education: training and education play a central role in equipping

teachers with the skills to recognize AI-generated content in student writings

and research papers. Through participating in workshops and training sessions,

teachers and professors can learn to identify subtle differences between AI-

generated and human-written texts. These sessions can focus on key indicators

such as the high frequency of purpose statements, lack of detailed results and

conclusions, and insufficient contextualization of previous research. Teachers can

be taught to use text analysis software and algorithms effectively, understanding

how these tools flag potential AI-generated content. Additionally, they can be

trained to critically evaluate the depth and quality of writing, looking for signs of AI

authorship. With ongoing professional development, teachers can stay updated

on the latest advancements in AI text generation and detection.

Therefore, it is through focusing on these key indicators and developing sophisticated

detection methods that teachers and content detection tools can better identify AI-

generated text. This ensures the integrity and authenticity of academic and professional

writing, maintaining high standards in scholarly communication.

It is essential also for teachers to develop pedagogical strategies aimed at mitigating

the use of AI-generated content in student submissions. Based on the findings of this

study, these strategies include:

• Emphasizing Comprehensive Writing Skills: this involves encouraging students

to incorporate thorough contextualization, detailed methodology, and

comprehensive results and conclusions in their writing. This approach enhances

the depth and quality of their academic work and helps distinguish it from AI-

generated content. Through teaching students to thoroughly contextualize their

Using Genre Analysis to Detect AI-

Generated Academic Texts

21.

research, they learn to integrate relevant literature and build a solid foundation

for their studies. Emphasizing detailed methodologies ensures that their research

processes are transparent, replicable, and well-understood. Encouraging

comprehensive results and conclusions also helps students develop critical

thinking skills, allowing them to analyze and interpret their findings meaningfully.

Javaid et al. (2023) research supports the strategy of encouraging comprehensive

writing skills, particularly in terms of thorough contextualization and detailed

methodology, which can help students create more original and meaningful

academic work that stands apart from AI-generated content.

• Teaching Critical Analysis: teaching critical analysis involves educating students

on the importance of integrating previous research and identifying research

gaps, which are often underrepresented in AI-generated content. Highlighting

the significance of building on existing knowledge, students learn to contextualize

their work within the broader academic domain, demonstrating how their

research contributes to ongoing scholarly conversations. This skill improves the

quality and relevance of their work and enhances their ability to identify and

address gaps in current research. Through targeted instruction and practice,

students become proficient at critical thinking, which allows them to assess and

synthesize information more effectively, produce original insights, and ultimately

create more robust and impactful research papers. Through educating students

on these aspects, they learn to build on existing knowledge and contribute to

scholarly conversations, aligning with Bhatia’s (1993) insights into genre evolution

and audience expectations.

• Implementing Stringent Assessment Criteria: developing assessment criteria

that emphasize the quality and depth of writing can make it more challenging

for AI-generated content to meet academic standards. For instance, criteria

could focus on the depth of literature review, requiring students to critically

engage with a wide range of sources and demonstrate how their work fits

into existing research. Additionally, rubrics might emphasize the necessity for

detailed arguments, where students must provide comprehensive explanations

and robust evidence to support their claims. Assessments could also include a

strong emphasis on originality and critical thinking, requiring students to formulate

unique research questions and hypotheses, and to provide in-depth analysis and

interpretation of their results. Such criteria would demand a level of intellectual

engagement and complexity that AI-generated texts often struggle to achieve,

thereby encouraging more authentic and thoughtful academic writing. As

stated by Oshima and Hogue (2006), focusing on the structural elements allows

assessments to ensure students provide in-depth analysis and robust evidence,

making it harder for AI-generated content to meet these high standards

The analysis of text function recurrence in AI-generated and human-written abstracts

provides valuable insights into their structural and functional differences. These findings

have significant implications for AI content detection in students' writing. Through

identifying specific patterns and developing advanced detection tools, teachers can

better distinguish between AI-generated and human-written content, thereby maintaining

academic integrity and promoting authentic student learning. Understanding these

distinctions also allows for more targeted pedagogical approaches that address the

unique challenges posed by AI in academic writing.

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22.

Analysis of the abstracts at the discourse level

The methodology used to analyze both types of abstracts is a comparative

approach, focusing on three main aspects: Language Complexity, Writing Style, and

Discourse Organization.

As to Language Complexity, it is evaluated by examining the level of detail and

technicality in the language used (Ortega, 2003). This involves analyzing whether the texts

employ specialized terminology, technical jargon, and complex sentence structures.

Each text is assessed to determine if the language is dense and highly technical or if

it is more straightforward and accessible. This aspect helps in understanding how the

complexity of language affects the clarity and depth of the content.

Regarding Writing Style, it is analyzed by looking at sentence length, clarity, and

the presence of jargon (Leki, 1991). The analysis distinguishes between texts with dense,

technical, and academic writing styles and those with clearer, more concise styles. It

is through comparing how formal or informal the writing is, and how the sentences are

structured that the analysis determines how the writing style influences the readability

and effectiveness of the text.

Concerning Discourse Organization, it involves examining how the texts are

structured and how they present their content (Heracleaous, 2006). This includes

evaluating the organization of moves, the coherence of arguments, and the inclusion

of theoretical or empirical components. The analysis identifies whether the text is

more focused on detailed methodologies, theoretical models, and comprehensive

exploration, or if it centers on practical findings and recommendations. This aspect

helps in understanding how the organization of content affects the global flow and

comprehensibility of the text.

In practice, this methodology involves a systematic review of each abstract, using

established criteria for each aspect to ensure consistency. Abstracts are compared

within each set to identify similarities and differences. Findings show how different

abstracts approach language complexity, writing style, and discourse organization. This

approach allows for a structured and detailed comparison, highlighting the varying

ways in which academic texts handle these key elements.

Table 2

Human-written vs AI generated abstracts.

Set Aspect

Text 1: Human written Text 2: AI generated

Uses specific terminology; more detailed

and technical language.

Language

Complexity

Writing Style

Discourse

Organization

Dense with multiple clauses and technical

jargon; longer sentences.

Detailed methodology and outcomes;

specific references to theoretical models

and implications.

More straightforward; general descriptions

of methods and goals.

Clear and concise; simpler sentence

structures.

Focuses on aims, methods, and

implications; less emphasis on theoretical

frameworks.

Detailed definitions and implications; uses

complex sentences.

Language

Complexity

Writing Style

Detailed and academic with

comprehensive definitions and

explanations.

Simpler and more direct; focuses on

practical implications and empirical

research.

More focused on effects and practical

applications; less technical detail.

Using Genre Analysis to Detect AI-

Generated Academic Texts

23.

Discourse

Organization

Structured with definitions, methods,

findings, and implications.

Organized around empirical research

and practical outcomes; less emphasis

on definitions.

Informal and fragmented; inconsistent

grammar and structure.

Language

Complexity

Writing Style

Discourse

Organization

Informal and conversational with

grammatical errors and lack of cohesion.

Disjointed structure with fragmented

sentences; lacks clear focus and

organization.

Formal and structured; consistent

grammar and clear language.

Formal academic style with clear,

organized presentation of findings.

Well-organized with clear sections

on research methods, findings, and

implications.

Complex and theoretical; detailed

discussion of factors.

Language

Complexity

Writing Style

Discourse

Organization

Academic with extensive use of

theoretical frameworks and complex

sentences.

Detailed exploration of theories and

factors; includes various research

methods and implications.

Theoretical but more focused on

practical implications; concise and

direct.

Direct and less theoretical; emphasizes

practical implications and concise

reporting.

Focused on practical findings and

implications; organized around specific

case study and context.

Detailed and technical language;

includes specific definitions and

theoretical explanations.

Language

Complexity

Writing Style

Discourse

Organization

Academic with dense descriptions and

detailed theoretical discussion.

Comprehensive with detailed analysis of

theoretical models and methods.

Clear and focused on practical aspects;

less technical detail.

Concise and practical; emphasizes

application and practical results.

Focused on practical strategies and

results; organized around case study and

implications.

Detailed discussion of the topic; complex

sentence structures.

Language

Complexity

Writing Style

Discourse

Organization

Detailed academic style with extensive

use of theoretical references.

Structured with theoretical background,

methodology, and analysis.

Direct and practical; focuses on

implementation and real-world

application of the topic.

Simplified and practical; focuses on the

gap between expectations and reality.

Organized around practical findings and

recommendations; less emphasis on

theoretical background.

Focused on theories with detailed

references and complex explanations.

Language

Complexity

Writing Style

Discourse

Organization

Academic with detailed discussion of

theories and motivation concepts.

Theoretical framework followed by

detailed analysis of the topic.

Direct and practical; focuses on specific

case study and empirical findings.

Clear and focused on empirical research

and practical implications.

Structured around empirical research and

specific case study findings.

Complex and theoretical; detailed

discussion of the topic.

Language

Complexity

Writing Style

Discourse

Organization

Academic with dense theoretical

discussion and complex sentences.

Detailed analysis with theoretical and

narrative elements.

Theoretical but focused on practical

implications; concise and clear reporting.

Direct and focused on specific case

study and theoretical implications.

Structured around practical analysis

of specific case study and theoretical

implications.

Detailed description of challenges

and hypotheses with varied sentence

complexity.

Language

Complexity

More straightforward; focuses on specific

challenges and recommendations.

9 Writing Style

Discourse

Organization

Detailed and descriptive; includes

complex sentences and academic

references.

Detailed exploration of challenges with

mixed organizational structure.

Clear and concise; practical focus on

challenges and recommendations.

Organized around specific findings and

recommendations with a clear structure.

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24.

Detailed discussion of new technology;

includes specific references and complex

sentences.

Language

Complexity

Writing Style

Discourse

Organization

Academic with extensive discussion of

technology and its impact.

Comprehensive analysis with theoretical

and practical components.

Focused on practical aspects of

new technology with clear, empirical

language.

Direct and practical; focuses on specific

case study and practical implications.

Structured around case study and

empirical findings; less emphasis on

theoretical background.

The comparison between human-written and AI-generated texts reveals several

significant differences in language complexity, writing style, and discourse organization.

These differences reflect the distinct approaches and strengths of human authors versus

AI systems.

Language Complexity

Human-written texts often exhibit a higher level of language complexity. They typically

use specific terminology and detailed technical language, as seen in examples in Table

1 where the abstracts employ specialized jargon and complex sentence structures. This

complexity allows for balanced discussions and in-depth explanations of theories and

methodologies. The use of complex language and terminology can contribute to a

rich and precise presentation of ideas, although it may also lead to less accessibility

for readers who are not familiar with the field or genre. This, in fact, aligns with the

claims of Javaid et al. (2023) who focused on the limitations of AI in engaging deeply

with subject matter, highlighting how AI-generated content often lacks the depth and

specificity found in human-written texts.

In contrast, AI-generated texts tend to be more straightforward and less technical.

They often present general descriptions of methods and goals, using simpler language

and sentence structures. While this approach makes the text more accessible to a

broader audience, it may lack the depth and specificity found in human-written texts. AI

systems prioritize clarity and conciseness, which can result in a more readable but less

detailed exposition of complex subjects, which maps with the findings of Logacheva

et al. (2024) who identified the characteristics of AI-generated texts, such as repetitive

phrasing and lack of depth in contextualization.

For this reason, it could be stated that one of the primary indicators of AI-generated

text is its lack of depth and specialization. AI often avoids complex jargon and highly

specific terminology, opting for more general terms. Thus, when a text lacks detailed

technical language and presents information in a more basic manner in an academic

genre, it may suggest AI authorship.

Writing Style

The writing style in human-written texts, as exhibited in the findings above, is frequently

dense, and characterized by multiple clauses and technical jargon. Sentences are often

longer and more complex, reflecting a deep engagement with theoretical frameworks

and detailed descriptions. This style can be indicative of rigorous academic work,

where the richness of the content is conveyed through elaborate and sophisticated

language. However, this style may also lead to less immediate readability.

Using Genre Analysis to Detect AI-

Generated Academic Texts

25.

As described in the findings in the table above, AI-generated texts generally exhibit

a clearer and more concise writing style. They use simpler sentence structures and

avoid excessive jargon, making the content easier to understand. This style is effective

for conveying information quickly and directly, focusing on practical implications and

results rather than theoretical intricacies. However, the simplicity of the writing style may

sometimes limit the depth and richness of the discussion.

Therefore, AI-generated texts’ more straightforward and concise writing style can be

a significant clue. When a text avoids long, complex sentences and technical jargon in

favor of clear and simple explanations, it might be the product of an AI. However, this

may certainly be explored in future research by examining papers published by expert

and professional researchers. The clarity and directness of AI-generated texts are often

noticeable compared to the more elaborate and dense style of human authorship.

The claim that AI-generated texts exhibit a clearer and more concise writing style,

using simpler sentence structures and avoiding excessive jargon, is supported by studies

such as Javaid et al. (2023) and Logacheva et al. (2024). These studies highlight that

AI models prioritize clarity and straightforward communication, which often results in

less depth and complexity compared to human-written texts. While this simplicity can

enhance readability and practical application, it also limits the depth and richness

of discussion, which is typically characterized by complex sentence structures and

specialized terminology in human written texts. Therefore, the straightforward and less

technical style of AI-generated texts may serve as a significant indicator of their origin,

contrasting with the more elaborate and dense writing of human texts.

Discourse Organization

Human-written abstracts demonstrated detailed and structured discourse

organization. They included specific references to theoretical models, methodologies,

and implications. The organization is typically comprehensive, with a clear delineation

of different moves such as methodology, findings, and theoretical analysis. This structure

supports a thorough exploration of the topic, allowing for an in-depth discussion and a

balanced presentation of research findings.

As to AI-generated texts, they focused on aims, methods, and practical outcomes

with less emphasis on theoretical frameworks. The organization tended to be more

streamlined, centering on empirical research and practical implications. While this

approach facilitates a straightforward presentation of findings and recommendations, it

may lack the detailed theoretical context and comprehensive analysis found in human-

written texts. The AI’s organization is often designed to ensure clarity and coherence,

which can enhance the accessibility of the content.

Consequently, Discourse organization can provide clues to the text’s origin. AI-

generated texts often have a more streamlined structure, focusing on practical

implications rather than detailed theoretical discussions. A lack of comprehensive

theoretical exploration and detailed methodology might indicate AI authorship. If a

text is well-organized but lacks in-depth theoretical context or detailed analysis, it may

be produced by an AI.

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26.

Conclusion

This study provides a detailed analysis of the differences between human-written

and AI-generated abstracts by applying genre analysis techniques. Through examining

the distinctive features and recurrence patterns of key text functions (such as the

purpose of study, methodology, and contextualization) the research identifies clear

differentiators between the two types of content. Human-written abstracts tend to

exhibit a more balanced distribution of elements, with a greater emphasis on detailed

results and conclusions, as well as a deeper integration of previous research. In contrast,

AI-generated abstracts often prioritize explicit purpose statements and demonstrate

less depth in results and contextualization. The study highlights the potential for

developing sophisticated detection methods, such as tailored text analysis software

and contextualization assessments, to identify AI-generated content. Additionally, it

highlights the importance of educating teachers and refining assessment criteria to

maintain academic integrity. Focusing on comprehensive writing skills, critical analysis,

and stringent assessment standards equips the academic community with better

strategies to deal with the challenges posed by AI in scholarly writing.

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