Artificial intelligence and academic

fraud in the university context

La inteligencia artificial y el fraude

académico en el contexto universitario

How to cite: Puche, V. D. J. (2025). Artificial intelligence and academic fraud in the university context.

Revista Digital de Investigación y Postgrado, 6(11), 69-83.https://doi.org/10.59654/kg944e15

Revista Digital de Investigación y Postgrado, 6(11), 69-83

Electronic ISSN: 2665-038X

* Latin American Doctorate in Education: Public Policies and Teaching Profession, MSc. in Biology Teaching,

Bachelor's Degree in Education with a Major in Biology. Universidad Pedagógica Experimental Libertador,

Faculty of Letters and Education, School of Education, Caracas - Venezuela.Email: deinnypuche@gmail.com

Received: September / 3 / 2024 Accepted: October / 23 / 2024

Deinny José Puche Villalobo

https://orcid.org/0009-0003-9646-2356

Caracas / Venezuela

https://doi.org/10.59654/kg944e15

Abstract

The study arises from the growing observation of the use of AI in education and the inability of

students to explain their processes, suggesting the misuse of AI in their work. The objective was

to determine the relationship between the use of AI and academic fraud in the university context.

The methodology was positivist, with a quantitative approach and correlational level. A virtual

questionnaire was used, with a reliability of 0.980 and validated by five experts, applied to a

sample of 144 faculty advisors (48 from Venezuela, 48 from Colombia, and 44 from Peru). The

results showed a Pearson correlation of 0.980 between the use of AI and academic fraud, indi-

cating a very strong positive relationship.

Keywords: artificial intelligence, academic fraud, correlation.

Resumen

El estudio surge de la observación creciente del uso de la IA en la educación y la incapacidad

de los estudiantes para explicar sus procesos, sugiriendo un uso indebido de la IA en sus tra-

bajos. El objetivo fue determinar la relación entre el uso de la IA y el fraude académico en el

contexto universitario. La metodología fue positivista, con enfoque cuantitativo y de nivel co-

rrelacional. Se utilizó un cuestionario virtual, con una confiabilidad de 0.980 y validado por

cinco expertos, aplicado a una muestra de 144 docentes tutores (48 de Venezuela, 48 de Co-

lombia y 44 de Perú). Los resultados mostraron una correlación de Pearson de 0.980 entre el

uso de la IA y el fraude académico, indicando una relación positiva muy fuerte

Palabras clave: inteligencia artificial, fraude académico, correlación.

Introduction

Artificial Intelligence (AI) is having a significant impact on education, revolutionizing academic

processes and presenting numerous advantages for both students and teachers. Its impact on

academic processes is becoming increasingly significant, offering numerous advantages and

opportunities for both students and educators.

In this regard, Jofre (2023) highlights that the importance of AI in the educational field is evident

in several aspects, as it allows teaching and learning processes to be adapted to the individual

needs of each student, offering personalized study plans and individualized feedback. Additio-

nally, it can automate administrative and repetitive tasks, freeing up time for teachers to focus

on more important aspects.

According to Granero (2021), AI systems act as intelligent tutors, providing personalized assistance

to students anytime and anywhere. At the same time, they can analyze data to identify patterns

that might indicate learning difficulties, enabling early interventions. AI systems can continuously

assess students' progress and provide detailed information to teachers and parents.

70 Deinny José Puche Villalobo

Revista Digital de Investigación y Postgrado, 5(11),69-83

Electronic ISSN: 2665-038X

Artificial intelligence and academic fraud in the university context

In this same context, Alonso & Quinde (2023) argue that AI can facilitate access to quality edu-

cation for students in remote areas or with limited resources, as well as promote inclusion in

the classroom by providing tools and resources that support students with special educational

needs. It also helps drive educational research and development by providing tools to analyze

large data sets and assess the effectiveness of different teaching strategies.

Considering the aforementioned points, it can be seen that the authors believe AI can foster

creativity and critical thinking in students by providing them with tools to explore ideas and

solve problems creatively. AI-driven education can help students acquire the necessary skills to

thrive in a workplace transformed by AI.

However, the indiscriminate and unconscious use of AI can lead to adverse consequences in

learning levels and intellectual production, as the responsibility of extracting information from

these programs is often delegated without analyzing or questioning its accuracy. This suggests

that while its impact on teaching and learning processes offers numerous benefits, new concerns

arise related to the potential misuse of AI for academic fraud.

In this regard, García et al. (2024) point out that one form of academic fraud involving AI includes

plagiarism, identity theft, the creation of false content, and data manipulation. This is significant,

as it undermines academic integrity, affects educational equity, hampers the assessment of real

learning, and discourages creativity and critical thinking.

According to Mayta et al. (2023), combating academic fraud in the AI era requires promoting

a culture of academic integrity, implementing fraud detection measures, designing more inno-

vative assessments, encouraging responsible use of AI, and fostering collaboration between

educational institutions and technology developers.

Thus, the author of this study considers that AI presents both challenges and opportunities for edu-

cation. It is essential to address the risk of its misuse for academic fraud by promoting academic in-

tegrity, implementing effective detection measures, designing robust assessments, and educating

about the responsible use of AI. AI should not be seen as a threat but as a tool that, when used res-

ponsibly, can contribute to strengthening education and promoting honest and meaningful learning.

After reviewing some postulates and theories on this topic, the researcher believes that unders-

tanding the relationship between AI use and academic fraud is of great importance for main-

taining academic integrity, which is a fundamental pillar of education, particularly at the

university level in postgraduate studies. Understanding how AI can influence academic fraud

helps institutions preserve high ethical and quality standards in learning and research, ensuring

that academic achievements truly reflect students' abilities and efforts.

Additionally, this study aims to identify this relationship, as it allows educational institutions to

develop clear policies and guidelines on AI use. Establishing limits and standards for its utilization

ensures that AI is used ethically and responsibly. In this sense, these policies not only prevent

72 Deinny José Puche Villalobo

fraud but also promote the constructive use of technology in educational processes.

Moreover, understanding the risks associated with AI misuse is important for offering ethical education

and training programs. It is also considered that understanding the relationship between AI and aca-

demic fraud can drive the development and improvement of plagiarism and fraud detection tools.

Furthermore, understanding how AI can affect the quality of education allows institutions to

take proactive measures to ensure that students receive an authentic and valuable education.

Universities have the responsibility to train ethical and competent professionals, and unders-

tanding the challenges posed by AI in terms of academic fraud is essential to fulfilling this social

responsibility. In this sense, a figure is presented that, according to the researcher, gathers the

factors that can influence academic fraud through the use of AI.

Figure 1. Factors that may influence academic fraud through the use of AI.

Fuente: Elaboración propia (2024).

Figure 1 shows that, according to the researcher, the relationship between AI use and academic

fraud may be linked to the accessibility and ease of use of AI. These tools allow students to use

content generation tools, such as chatbots and text generators, without the need for advanced

technical skills. Additionally, academic pressure is another significant factor. Students may feel

intense pressure to achieve high academic performance, which may lead them to resort to AI

to complete tasks more quickly and efficiently, albeit dishonestly. Furthermore, the lack of proper

education on the ethical use of AI and the consequences of academic fraud can cause students

to underestimate the severity of using AI for dishonest purposes.

On the other hand, considering Puche's (2024) argument, the absence of clear policies and ins-

titutional guidelines on the use of AI in education can create an environment where students

do not know what is allowed and what is not, making it easier to commit fraud.

Revista Digital de Investigación y Postgrado, 5(11),69-83

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In another line of thought, Alonso & Quinde (2023) suggest that current plagiarism detection

tools may not be fully equipped to identify AI-generated content, allowing fraud to go unno-

ticed. AI can provide a convenient and quick way to generate academic content, which may be

tempting for students seeking to save time and effort.

Similarly, Alonso (2024) argues that students who use AI to complete tasks may not be engaged

in the learning process, resulting in a disconnect between acquired knowledge and the work

presented. The perception that teachers do not thoroughly review assignments or fail to detect

AI use may encourage fraud, as students feel they will not be caught. Moreover, the absence

of assessment methods that focus on the process rather than just the final product may allow

academic fraud to go unnoticed.

Thus, the study's author infers that by addressing these elements through clear educational po-

licies, ethical training programs, and the development of better detection tools, institutions can

mitigate the risk of academic fraud associated with AI use. It is important for educational ins-

titutions to take a proactive approach to face these challenges and ensure academic integrity

in the era of artificial intelligence. In this sense, the study aimed to determine the relationship

between artificial intelligence and academic fraud in Venezuela, Colombia, and Peru.

Methodology

The study's methodology adheres to the processes of the positivist paradigm, which aims to be

as objective as possible in the pursuit of knowledge, employing orderly and disciplined procedures

that allow the researcher’s ideas about the nature of the phenomena under study to be tested

(Acosta, 2023). Additionally, the quantitative approach was considered, defined by Arias (2019) as

one that is based on the idea that all things or phenomena studied by science are measurable.

The study is descriptive in nature, as Hernández & Mendoza (2018) state that descriptive re-

search aims to describe the characteristics or properties of a phenomenon, situation, or area of

study without manipulating variables or establishing causal relationships. Its focus is to provide

a detailed and accurate representation of what is being studied.

It also presented a correlational level, as Hernández & Mendoza (2018) affirm that this type of

study seeks to assess the relationship between two variables to examine the degree of correla-

tion between them. This approach focuses on discovering how one variable changes as the

other changes, analyzing the direction of movement and the strength of the relationship. It is

important to note that correlation does not imply causality, meaning it does not establish a

cause-and-effect relationship between variables.

According to Arias (2019), in this type of research, statistical tools are used to measure and un-

derstand the degree of correlation between the studied variables. For example, correlation coef-

ficients, such as Spearman’s coefficient, can be employed to analyze the obtained information

and draw conclusions about the relationship between the variables.

Artificial intelligence and academic fraud in the university context

74 Deinny José Puche Villalobo

The sample consisted of 48 teachers from Venezuela, 48 from Colombia, and 44 from Peru, all

at the university level. The inclusion criteria required a teaching experience of over 5 years at

the university level, a master’s or doctoral degree, and involvement in teaching thesis seminars

at the university level, selected from various universities in each country.

The survey technique was applied using a multiple-choice questionnaire (always, sometimes,

never). This questionnaire was validated by 5 experts with doctoral degrees (2 from Venezuela,

2 Colombians, and 1 Peruvian) using Cronbach’s alpha coefficient, which yielded a reliability

score of 0.980. Regarding ethical considerations, transparency was ensured; the study's objec-

tives were made known, the data was safeguarded for academic and scientific use only, and

the identity of the universities and participants was protected. Data were processed using des-

criptive statistics, with results presented in frequency tables. Additionally, inferential statistics

were used to analyze the correlation between the study variables.

Results

Table 1

Elements influencing academic fraud

Note: Author's own work (2024).

Table 1 reveals the elements influencing academic fraud. The first dimension corresponds to "Ne-

gligence in supervision," with the first indicator being the lack of proper instruction and guidance.

It was observed that 66.42% of respondents indicated that this always occurs, 26.42% noted that

it happens sometimes, and 7.14% stated that it never happens. Regarding the lack of student

progress monitoring, 62.85% of participants reported that this lack always occurs, while 28.57%

said it happens sometimes, and 8.57% mentioned that it never occurs. Finally, concerning the

lack of communication with teachers, 80.0% of respondents believe that this lack always exists,

Dimensions Indicators

Answer options

Always Sometimes Never

F % F % F %

Negligence in

supervision

Lack of proper instruction and guidanc. 93 66.42 37 26.42 10 7.14

Lack of student progress monitoring. 88 62.85 40 28.57 12 8.57

Lack of communication with teachers. 112 80.0 23 16.42 5 3.57

Facilitating

behavior

Not challenging or questioning students'

work. 91 65.0 39 27.85 10 7.14

Not penalizing fraud. 124 88.57 16 11.42 0 0

Conflicts of

interest

Close personal relationships with stu-

dents. 99 70.71 20 14.28 21 15.0

Total 140 100 140 100 140 100

Revista Digital de Investigación y Postgrado, 5(11),69-83

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16.42% indicated that it happens sometimes, and 3.57% stated that it never occurs.

Referring to the dimension "Facilitating behavior," which is analyzed through two indicators: not

challenging or questioning students' work and not penalizing fraud. For the first indicator, 65.0%

of respondents said that challenging or questioning students' work is always avoided, 27.85%

indicated that it happens sometimes, and 7.14% mentioned that it never happens. Regarding

not penalizing fraud, 88.57% of participants reported that this behavior always occurs, 11.42%

said it happens sometimes, and no respondents stated that it never happens.

In relation to the dimension "Conflicts of interest," it was observed that, according to the results,

70.71% of respondents indicated that these close relationships between tutors and students al-

ways exist, 14.28% said they occur sometimes, and 15.0% noted that they never occur.

In this context, the researcher considers that the results indicate that negligence in supervision,

facilitating behavior, and conflicts of interest are significant problems in the evaluated academic

environment. Additionally, the lack of proper instruction, insufficient monitoring of student pro-

gress, and poor communication with teachers are commonly reported practices, suggesting

inadequate supervision. Furthermore, the lack of penalties for fraud and the absence of ques-

tioning students' work reflect permissive behavior that can negatively affect academic integrity.

Finally, close personal relationships with students reveal potential conflicts of interest that may

compromise fairness and impartiality in dealing with students.

Table 2

Common Frauds Committed Using AI

Note: Own elaboration (2024).

Artificial intelligence and academic fraud in the university context

Dimensions Indicators

Answer options

Always Sometimes Never

F % F % F %

AI-assisted

plagiarism

Generate complete works using AI tools. 123 85,41 17 11,80 0 0

Paraphrase existing text to avoid plagiarism

detection. 110 46,38 23 15,97 7 4,86

AI-assisted creation

of false content Use AI tools to create interview responses. 40 27,77 50 34,72 50 34,72

AI-assisted creation

of false content Fabricate data or research results. 70 46,61 35 24,30 35 24,30

AI-assisted misap-

propriation of ideas.

Present AI-generated work as one's own. 92 63,88 38 26,38 10 6,94

Fail to properly cite AI sources. 140 100 0 0 0 0

Total 140 100 140 100 140 100

76 Deinny José Puche Villalobo

Table 2 reveals the results for analyzing the most common frauds committed using AI. Concer-

ning the dimension "AI-assisted Plagiarism," specifically regarding the indicator of generating

complete papers using AI, 85.41% of respondents indicated that students always engage in this

practice, while 11.80% believe they do it sometimes. Additionally, regarding the practice of pa-

raphrasing existing text to avoid plagiarism detection, 46.38% of respondents noted that stu-

dents always use AI for this purpose, 15.97% said they do it sometimes, and 4.86% stated that

they never do it.

Regarding the dimension "AI-assisted Deception," 27.77% of participants mentioned

that students always use AI tools to create responses in interviews, while 34.72% do

it sometimes. Additionally, 34.72% believe that students never engage in this prac-

tice.

When analyzing the dimension "AI-assisted Creation of False Content," specifically regarding

fabricating data or research results, it was found that 48.61% of respondents indicated that stu-

dents always engage in this practice, 24.30% said they do it sometimes, and another 24.30%

believe they never do it.

Finally, concerning the dimension "AI-assisted Misappropriation of Ideas," it was observed that

63.88% of respondents said that students always present AI-generated work as their own,

26.38% do it sometimes, and 6.94% never do it.

According to the researcher, the results suggest a significant reliance on AI tools to produce

academic work without authentic personal contribution. It is also observed that, according to

the surveyed teachers, there is a significant prevalence of misuse of AI tools by students for

committing plagiarism and deception.

Table 3

Correlation Coefficient Between Variables

Note: Own elaboration (2024).

Artificial intelligence Academic fraud

Spearman's

Rho

Artificial intelligence

Coeficiente de correlación 1 0,980**

Sig. (bilateral) 0,000

N 140 140

Academic fraud

Coeficiente de correlación 0,980** 1

Sig. (bilateral) 0,000

N 140 140

Revista Digital de Investigación y Postgrado, 5(11),69-83

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Table 3 shows a Pearson correlation between the variables AI and academic fraud, indicating a

Pearson correlation of 0.980, which signifies a very strong positive relationship. This means that

as the use of artificial intelligence in education increases, academic fraud also tends to rise. Ho-

wever, it is important to emphasize that correlation does not imply causation. In other words,

just because two variables are correlated does not mean that one causes the other.

Discussion

Considering the results regarding teachers' perceptions of students’ use of AI when conducting

research, Cáceres & Ulloa (2023) suggest that students often misuse AI, largely due to negli-

gence in supervision, which negatively impacts the quality of education by allowing students to

deviate from learning objectives without timely correction.

In line with this, Granero (2021) argues that when supervisors do not adequately monitor student

performance, students may develop poor study habits, lack direction in their projects, and in

extreme cases, resort to dishonest practices such as plagiarism or the use of AI to create false

content. This lack of oversight fosters an environment where academic standards decline, and

students fail to reach their full potential.

Moreover, Granero (2021) also highlights that inadequate instruction and guidance prevent stu-

dents from clearly understanding academic expectations and how to meet them. According to

García et al. (2024), without proper guidance, students may feel lost and resort to quick fixes,

such as using AI tools to complete assignments. This not only hampers their learning and skill

development but also perpetuates a culture of dependency, rather than encouraging critical

thinking and problem-solving. The absence of clear instruction undermines students' confidence

in their abilities and the educational system as a whole.

Similarly, Crawford (2023) posits that the lack of monitoring of students' progress hinders ti-

mely identification of challenges and areas for improvement, leading to late or nonexistent

interventions. Without continuous monitoring, students' academic and personal struggles

may go unnoticed, increasing the risk of demotivation, underperformance, and even dro-

pout. García et al. (2024) assert that the absence of constructive feedback leaves students

without guidance on how to improve, affecting both their academic and personal develop-

ment. This lack of attention can lead to a general decline in educational quality and student

success.

Belda (2019) adds that the lack of communication with professors creates a gap in the educa-

tional process, where students do not receive the necessary guidance for their academic and

personal development. Without effective communication, teachers cannot identify students' in-

dividual needs or provide adequate support. This can result in an incomplete understanding of

the material, unresolved difficulties, and a lack of direction in learning. The disconnection bet-

ween students and professors can also lead to decreased motivation and engagement with

their studies.

Artificial intelligence and academic fraud in the university context

78 Deinny José Puche Villalobo

In this context, Soria et al. (2022) and Vries (2023) argue that facilitator behaviors, such as not

challenging or questioning students' work, contribute to poor educational quality by failing to

promote critical thinking and self-assessment. Mayta et al. (2023) suggests that when students

are not challenged to justify and reflect on their work, the opportunity to develop analytical

and reasoning skills is lost. This lack of academic rigor allows students to settle for minimal effort,

failing to reach their full potential and perpetuating a culture of mediocrity rather than exce-

llence.

On the other hand, Puche (2024) emphasizes that failing to sanction fraud creates an envi-

ronment where academic dishonesty can proliferate without consequences, undermining

the integrity of the educational system. The lack of clear and consistent sanctions sends a

message that fraud is tolerated, which may encourage more students to engage in dishonest

practices. This not only affects fairness and justice in academia but also devalues degrees

and certifications, harming both honest students and the reputation of the educational ins-

titution.

Continuing the analysis of the study's results, Vander & Cury (2024) argue that conflicts of inte-

rest, such as close personal relationships with students, can compromise impartiality and ob-

jectivity in academic evaluation and supervision. These conflicts may lead to favoritism, where

certain students receive preferential treatment or unjustly positive evaluations, affecting clas-

sroom fairness. Moreover, these relationships can make it difficult to enforce disciplinary sanc-

tions and base academic decisions on merit. The presence of such conflicts erodes trust in the

integrity of the educational process and can create an environment of distrust and resentment

among students.

In the same vein, Zuñiga & Polanco (2023) highlight that AI-assisted plagiarism occurs when

artificial intelligence technology is used to copy and present others' work as one's own. This

manifests in texts or assignments containing entire phrases or paragraphs that match existing

sources without proper citation, which can be easily identified through plagiarism detection

software.

However, Alonso & Quinde (2023) point out that these works often exhibit inconsistent or un-

natural writing styles, as the copied parts do not integrate well with the rest of the original con-

tent. The use of AI tools to paraphrase or reword content without significantly altering its

meaning is another key indicator. These elements reveal the reliance on AI to create academic

or professional work that is not entirely original.

Regarding AI-assisted creation of false content, Franganillo (2022) explains that it involves using

artificial intelligence technologies to generate texts that are not authentic. Jofre (2023) asserts

that this seriously impacts educational quality by flooding the academic environment with inac-

curate or misleading information, making it difficult to distinguish between real and fabricated

facts. This can lead to the spread of erroneous knowledge among students and teachers, com-

promising the integrity of learning and research.

Revista Digital de Investigación y Postgrado, 5(11),69-83

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According to Villalobos (2024), it fosters a culture of distrust in information sources and re-

duces the value of genuine academic work, while also discouraging critical thinking and ri-

gorous fact-checking. These effects erode the credibility and effectiveness of the educational

system in its mission to educate informed individuals capable of positively contributing to so-

ciety.

Contrasting these results with Gallent et al. (2023) theory, which posits that AI-assisted misap-

propriation of ideas occurs when AI tools are used to take others' original ideas and present

them as one’s own, this is evident in project proposals, research, or presentations that reflect

ideas or concepts previously presented by others without proper acknowledgment. The study

data reveal a significant weakness concerning this dimension (AI-assisted misappropriation of

ideas).

In this context, Díaz (2023) argues that works showing advanced or detailed knowledge that

does not align with the author's level of experience are also suspect. Alonso (2024) adds that

using AI to explore research databases and then slightly rephrase the findings without crediting

the original authors is a common practice. This reveals that discrepancies between the author's

knowledge of the subject and the quality of the work presented indicate possible dependence

on AI to misappropriate others' ideas.

Considering the results obtained, it is evident that students are not using AI appropriately. Ins-

tead of employing it as a support tool to enrich and facilitate their academic work, students

are delegating the construction and writing of every element of their research to AI. This is

based on the high level of correlation determined between the analyzed variables, suggesting

an excessive dependence on AI for tasks that should be completed by the students themsel-

ves.

In this regard, the misuse of AI has serious implications for educational quality, as students are

not developing the critical skills necessary for their academic and professional growth. The lack

of personal involvement in the research and writing process can lead to a superficial unders-

tanding of the content and an inability to apply acquired knowledge in real-world contexts.

To address this issue, a meeting was held with faculty members (research supervisors) who par-

ticipated in the survey and shared their observations and concerns. By consensus, some gui-

delines were established to curb the misuse of AI. These guidelines aim to promote the

responsible and ethical use of technology, ensuring that students develop the skills necessary

for their academic success.

In this context, it was considered essential to incorporate mandatory workshops or modules in

postgraduate programs to educate students on the responsible use of artificial intelligence in

research and thesis writing. These programs should address the scope and limitations of AI

tools for writing and content generation, as well as the ethical and academic standards related

to the integrity of intellectual work.

Artificial intelligence and academic fraud in the university context

80 Deinny José Puche Villalobo

Additionally, it is important to inform students about the consequences of plagiarism and

the misuse of AI, guiding them in its proper use. This virtual assistant can aid in searching

and organizing information, analyzing data, generating visualizations, and writing and re-

viewing academic texts. A series of activities were proposed as part of the solution to this

problem.

Table 4

Suggestions for addressing the misuse of AI in committing academic fraud.

Activity Description Benefits for preventing misuse of AI in

theses

Fomentar la

educación

sobre la IA y

la ética aca-

démica

•Incorporate mandatory workshops or modules

into graduate programs.

•Educate students on the responsible use of ar-

tificial intelligence in research and thesis wri-

ting, including: (a) The scope and limitations of

AI tools for writing and content generation. (b)

Ethical and academic standards related to in-

tellectual integrity. (c) The consequences of

plagiarism and misuse of AI in thesis prepara-

tion.

•Promote the use of AI tools for learning and re-

search:

•Guide students in the appropriate use of AI

tools to support their learning and research

process, such as: searching and organizing re-

levant information; data analysis and genera-

ting visualizations; writing and reviewing

academic texts.

•Emphasize the importance of critical thinking

and originality: encourage students to develop

critical thinking and analytical skills to evaluate

information obtained through AI and generate

their own ideas and arguments.

•Define the types of allowed AI tools: Specify

which AI tools may be used by students in the

development of their theses, considering their

impact on the originality and academic value of

the work. Establish limits on AI usage: Deter-

mine the amount of AI-generated content that

can be used in a thesis, ensuring that the pri-

mary work is conducted by the student Require

transparency in AI usage: Require students to

clearly cite any AI tool or resource used in the

preparation of their thesis, including a descrip-

tion of its function and impact on the final con-

tent.

•Helps students understand the capabilities

and limitations of AI in the academic con-

text, promoting responsible and ethical

use.

•Provides students with tools and strategies

to effectively leverage AI in their learning

and research processes without compromi-

sing the originality of their work.

•Encourages the development of critical

thinking and analytical skills, essential for

evaluating information, formulating argu-

ments, and generating original knowledge.

Revista Digital de Investigación y Postgrado, 5(11),69-83

Electronic ISSN: 2665-038X

Note: Own elaboration (2024).

Conclusions

The study results reveal a Pearson correlation of 0.980 between the use of AI and academic fraud.

This value indicates a very strong positive relationship, suggesting that as the use of AI in edu-

cation increases, academic fraud also tends to increase. However, it is important to highlight that

correlation does not imply causation. Although the two variables are strongly related, it cannot

be concluded that AI use directly causes academic fraud. Other factors may be influencing this

relationship.

These findings underscore the need to implement regulations and educational policies that ad-

dress the ethical use of AI. Additionally, educating students about the responsible use of AI tools

and establishing clear guidelines can help mitigate the risk of academic fraud. Promoting the

development of critical thinking and analytical skills in students is crucial for them to use AI ethi-

cally and responsibly. These skills will help them evaluate AI-generated information and develop

their own arguments and conclusions.

In this context, it is also inferred that implementing fraud detection and evaluation strategies,

such as plagiarism detection software and peer reviews, is essential to ensure academic integrity.

These measures can help identify and prevent AI-related academic fraud. Additionally, fostering

a culture of academic integrity is fundamental to reducing the incidence of academic fraud.

It is also important to inform students about expectations, ethical standards, and the consequen-

ces of fraud, along with recognizing and rewarding ethical behavior, to encourage honest and

responsible academic conduct. Therefore, while the study revealed a very strong positive rela-

tionship between AI use and academic fraud, it is crucial to address this issue from multiple

angles, including education, regulation, evaluation, and the promotion of a culture of academic

integrity. Only through a holistic and multifaceted approach can the challenge of academic fraud

in the context of increasing AI use be effectively addressed.

Artificial intelligence and academic fraud in the university context

Establish

clear gui-

delines for

the use of

AI in thesis

develop-

ment

•Define the types of allowed AI tools: Specify which

AI tools may be used by students in the develop-

ment of their theses, considering their impact on

the originality and academic value of the work. Es-

tablish limits on AI usage: Determine the amount of

AI-generated content that can be used in a thesis,

ensuring that the primary work is conducted by the

student Require transparency in AI usage: Require

students to clearly cite any AI tool or resource used

in the preparation of their thesis, including a des-

cription of its function and impact on the final con-

tent.

•Provides students with clear guidance

on what is expected regarding the use

of AI in their theses, preventing confu-

sion and potential violations of acade-

mic standards.

•Ensures that the majority of the thesis

work is carried out by the student, pro-

moting the development of their re-

search and writing skills.

•Encourages transparency and traceabi-

lity in the use of AI, allowing evaluators

to understand the thesis preparation

process and the student's actual contri-

bution.

82 Deinny José Puche Villalobo

References

Alonso, A. J. & Quinde, C. M. (2023). ChatGPT: La creación automática de textos académicos

con Inteligencia artificial y su impacto en la comunicación académica y educativa. Desiderata,

6(22), 136-142. https://gredos.usal.es/handle/10366/152505

Alonso, R. A. (2024). Hacia un marco ético de la inteligencia artificial en la educación. Teoría de

la Educación. Revista Interuniversitaria, 36(2), 79-98. https://revistas.usal.es/tres/index.php/

1130-3743/article/view/31821

Belda, I. (2019). Inteligencia artificial. RBA Libros.

Caceres, K. & Ulloa, S. (2023). Implicancias del uso de la inteligencia artificial al emitir resoluciones

judiciales respecto de los Derechos Fundamentales. [Tesis de grado, Universidad Cesar Vallejo]

https://repositorio.ucv.edu.pe/handle/20.500.12692/144174

Crawford, K. (2023). Atlas de inteligencia artificial: Poder, política y costos planetarios. Fondo de

Cultura Económica Argentina.

Díaz, D. (2023). Inteligencia artificial vs. Turnitin: implicaciones para el plagio académico. Revista

cognosis, 8 (1) 15-26 https://doi.org/10.33936/cognosis.v8i1.5517

Franganillo, J. (2022). Contenido generado por inteligencia artificial: oportunidades y amenazas.

Anuario ThinkEPI, 16. https://doi.org/10.3145/thinkepi.2022.e16a24

Gallent, T. C., Zapata, G. A. & Ortego, H. J. (2023). El impacto de la inteligencia artificial gene-

rativa en educación superior: una mirada desde la ética y la integridad académica. RELIEVE.

Revista Electrónica de Investigación y Evaluación Educativa, 29(2), 1-21. https://www.redalyc.

org/journal/916/91676028011/91676028011.pdf

García, F.., Llorens, L. F. & Vidal, J. (2024). The new reality of education in the face of advances

in generative artificial intelligence. [La nueva realidad de la educación ante los avances de

la inteligencia artificial generativa]. RIED-Revista Iberoamericana de Educación a Distancia,

27(1). https://doi.org/10.5944/ried.27.1.37716

Granero, H. (2021). Inteligencia artificial y derecho, un reto social. elDial. com.

Hurtado, M. (2020). Liderazgo pedagógico e inteligencia artificial Maestría en educación de una

institución de educación superior. Estudio de caso. .[Tesis de maestría, Universidad militar de

Granada] https://repository.unimilitar.edu.co/handle/10654/36061

Jofre, C. (2023). ChatGPT, Inteligencia Artificial y Universidad. Nuevas tensiones, transformacio-

nes y desafíos en la educación superior. Campo Universitario, 4(7). https://www.academia.

Revista Digital de Investigación y Postgrado, 5(11),69-83

Electronic ISSN: 2665-038X

edu/download/104836259/ChatGPT_Inteligencia_artificial_y_Universidad.pdf

Mayta, T. P., Borja, G. R. & Angulo, B. Y. (2023). Compra y venta de autorías en Perú: Fraude

científico, mala conducta científica o práctica cuestionable en investigación. Revista del

Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo, 16(4). http://www.cmhnaaa.

org.pe/ojs/index.php/rcmhnaaa/article/download/2370/878

Puche, D. (2024). Fraude académico en los trabajos de investigación: Desafíos y ética en la in-

tegridad académica. Revista En Prospectiva| Universidad Yacambú, 5(1), 61-89. https://re-

vista.uny.edu.ve/ojs/index.php/en-prospectiva/article/view/371

Soria, E., Rodríguez, P., García, Q., Vaquer, F., Vicent, J. & Vila, J. (2022). Inteligencia artificial. Ra-

Ma Editorial.

VanderLinde, G. & Cury, T. M. (2024). El uso de inteligencia artificial y sus desafíos para la eva-

luación académica: una revisión de la literatura. Cuaderno de Pedagogía Universitaria, 21(41).

Villalobos, L. J. (2024). El Plagio Académico y las Repercusiones Legales en las Tesis de Grado

en México. Revista Docentes 2.0, 17(1), 5-17. https://ojs.docentes20.com/index.php/revista-

docentes20/article/view/436

Vries, W. (2023). Como (no) combatir el fraude académico: Lecciones internacionales. Revista

mexicana de investigación educativa, 28(97), 637-650. https://www.scielo.org.mx/scielo.php?

pid=S1405-66662023000200637&script=sci_arttext

Zuñiga, G. & Polanco, P. (2023). Plagio y ética profesional en el proceso de investigación social.

Revista de la Universidad, 1, 50-59. https://doi.org/10.5377/ru.v1i1.17244

Artificial intelligence and academic fraud in the university context