# Relationship Between the Use of ChatGPT and Plagiarism in Higher Education: The Influence of Gender, Age and Previous Academic Results

Lucía Campo, Nahia Delgado, José María Etxabe-Urbieta, Houssam Kanso, Mamadou Lamine Gueye

<figure><img src="image_1.png" /></figure>

## To cite this version:

Lucía Campo, Nahia Delgado, José María Etxabe-Urbieta, Houssam Kanso, Mamadou Lamine Gueye. Relationship Between the Use of ChatGPT and Plagiarism in Higher Education: The Influence of Gender, Age and Previous Academic Results. Innovative Higher Education, 2025, 10.1007/s10755-025-09830-z. hal-05144015

HAL Id: hal-05144015

https://hal.science/hal-05144015v1

Submitted on 4 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
# Relationship Between the Use of ChatGPT and Plagiarism in Higher Education: The Influence of Gender, Age and Previous Academic Results

Lucía Campo¹ ID · Nahia Delgado² ID · José María Etxabe-Urbieta³ ID · Houssam Kanso⁴ ID · Mamadou Lamine Gueye⁴ ID

Accepted: 10 June 2025
© The Author(s) 2025

## Abstract

While artificial intelligence (AI), exemplified by tools like ChatGPT, has undoubtedly ushered in numerous advantages within the realm of education, its implementation also underscores a range of constraints and challenges. The objective of the current study is to examine the association between the frequency of the use of ChatGPT for academic purposes and the extent of plagiarism, while also investigating the influence of personal variables such as gender, age, academic year, and educational background measured by previous results. To achieve this, a sample comprising 503 university students from Spain was recruited, and they were asked to complete a set of questionnaires designed to assess the constructs mentioned. The findings indicated that there is a correlation between the frequency of use of ChatGPT and Plagiarism, but causality was not found. Besides, a moderator effect of age and academic year on the relationship between the use of ChatGPT and Plagiarism was found. These findings suggest the need to address certain factors of plagiarism among higher education students, such as awareness of plagiarism or the management of perceptions about the academic requirements.

**Keywords** Artificial intelligence · Plagiarism · ChatGPT · Age · Gender · Higher education

## Introduction

Artificial Intelligence (following, AI) is a rapidly evolving discipline at the intersection of computer science, mathematics, and cognitive psychology, focused on creating intelligent machines that can mimic human behavior and decision-making processes (Xu et al., 2021).

In the educational field, AI presents numerous opportunities to revolutionize the different teaching and learning processes. Intelligent tutoring systems have

<footnote>Extended author information available on the last page of the article</footnote>
the capability to deliver customized learning experiences designed to match the unique requirements, progress, and learning preferences of each student (Rizvi, 2023). These systems can adapt to students' progress, provide immediate feedback, and offer additional support where necessary, enhancing overall learning outcomes.

Furthermore, AI-powered analytics can analyze vast amounts of educational data to identify trends, assess student performance, and optimize educational strategies. Predictive analytics can anticipate students at risk of falling behind or needing extra assistance, allowing educators to intervene proactively and provide targeted support (Moleka, 2023).

AI also facilitates the development of interactive educational tools and immersive learning experiences. Virtual reality (VR) and augmented reality (AR) applications powered by AI can simulate real-world environments, enabling hands-on learning in subjects like science, history, and engineering (Al-Ansiri et al., 2023; Huang et al., 2019). These immersive experiences enhance student engagement and comprehension, making learning more enjoyable and effective (Huang et al., 2019).

Additionally, AI-driven adaptive assessment systems can tailor assessments to each student's skill level and knowledge, ensuring more accurate evaluation and feedback (Moleka, 2023; Raza, 2023; Rizvi, 2023). This personalized approach to assessment promotes a growth mindset and encourages continuous learning and improvement.

Despite its promising potential, the integration of artificial intelligence in education also comes with challenges and limitations. One significant challenge is the ethical implications surrounding data privacy and security (Akgun & Greenhow, 2022; Camacho et al., 2023; Reiss, 2021). Educational institutions must ensure the responsible collection and use of student data to make sure that the privacy of the students is protected and to prevent misuse (Camacho et al., 2023). Plagiarism is another critical concern that intersects with the use of artificial intelligence in education. While AI can aid in detecting and preventing plagiarism through text similarity analysis and plagiarism detection algorithms, it also poses challenges in terms of ethical use (Akgun & Greenhow, 2022; Cotton et al., 2024). Students may be tempted to use AI-generated content or algorithms to produce academic work without proper attribution, leading to intellectual dishonesty and academic misconduct (Cotton et al., 2024; Delgado et al., 2024). Educators must educate students about the importance of academic integrity and ethical writing practices in the digital age (Delgado et al., 2024; Sullivan et al., 2023). Additionally, AI tools used for plagiarism detection must be regularly updated and refined to keep pace with evolving methods of academic dishonesty (Nguyen et al., 2023; Reiss, 2021). Furthermore, it is essential to strike a balance between using AI to support academic integrity and avoiding overreliance on automated tools, ensuring that critical thinking and originality remain central to the learning process (Cotton et al., 2024; Nguyen et al., 2023).

Lastly, previous research has pointed out fears in identifying the main factors associated with engaging in plagiarism through generative AI platforms like ChatGPT (Dehouche, 2021; Sullivan et al., 2023).

<figure><img src="image_3.png" /></figure>
# ChatGPT and its Influence on Plagiarism

ChatGPT is a large language model (LLM) developed by OpenAI, based on the GPT architecture, which stands for Generative Pre-trained Transformer. ChatGPT has been trained on a massive dataset of text to interact with people in a more natural, conversational human-like way to answer questions and perform a variety of tasks. It uses natural language processing to provide users with written, AI-based answers to questions or prompts (Hsu et al., 2023).

Plagiarism can be defined as presenting someone else's work, ideas or words as if they were one's own, without referring to them appropriately (Wardat et al., 2023). With the use of ChatGPT or any other AI tool, the content generates results from pre-existing data rather than being produced directly by the author. Consequently, without proper citation and attribution generated content to its source could be considered a form of plagiarism (Macdonald et al., 2023; Wardat et al., 2023).

Currently, different studies have shown the difficulties of different software and applications for AI output detection, as well as human revisions, to identify the use of ChatGPT in generated texts (Gao et al. 2023; Khalil & Er, 2023; Schön et al., 2023).

Hence, while AI technology is posited as a tool with clear potential benefits in higher education, recent literature reviews have also detected a potential association between the use of AI technology, such as ChatGPT, and plagiarism. Among the main conclusions, they outline the need for a responsible integration of ChatGPT in academic research and writing to mitigate risks and prevent plagiarism in higher education (Dempere et al., 2023; Jarrah et al., 2023).

# The Influence of Gender

The influence of gender in the context of using ChatGPT for educational purposes and its potential association with unethical conduct, such as plagiarism, is an area worthy of exploration. Gender dynamics can intersect with various aspects of technology usage and academic behavior, shaping individuals'attitudes, behaviors, and experiences (Grande-de-Prado et al., 2020; Unterhalter et al., 2022).

Previous researches suggest that there are differences based on gender when adopting and manifesting the use of technological patterns for educational purposes throughout their schooling. Grande-de-Prado et al. (2020) indicate different levels of ease, trust, and interest between male and female users. However, other recent studies show that there is no clear consensus on the relationship between gender and the level of digital competence in the educational context (Fernández-Morante et al., 2023).

Grande-de-Prado et al. (2020), Jereb et al. (2018) and Zhao (2022) point out the social expectations and stereotypes linked to academic performance and ethical ideas that influence the perception and response related to the attitude of using ChatGPT in an unethical way. In particular, Jereb et al. (2018) have analyzed plagiarism by students of both sexes. Implicit norms associated with gender are linked to competitiveness, success, and intellectual integrity, and indicate their influence on people's decision-making processes when facing academic challenges or pressures (Jereb et al., 2018; Zhao, 2022).
From this research, it is deduced that the educational background and norms linked to different disciplines are linked to gender, such that certain disciplines or academic environments foster a culture that discourages unethical behavior, and influence according to gender representation within the field of knowledge (Fernández-Morante et al., 2023).

Gabriel (2021), Unterhalter et al. (2022) propose that understanding the influence of gender on the relationship between AI usage for educational purposes and unethical conduct like plagiarism requires nuanced exploration. It involves considering not only individual attitudes and behaviors but also broader social and cultural factors that shape gender identities and experiences in educational contexts (Unterhalter et al., 2022). All this research concludes that examining these intersections, provides information on how gender dynamics may impact the ethical implications of integrating AI technologies into education and inform strategies for promoting academic integrity and responsible technology use across diverse student populations to achieve gender equality in the classrooms.

## The Influence of Age

Chan and Lee (2023), Chu et al. (2022); Stypinska (2021) have investigated the influence of age on people's familiarity with technology, on their attitudes towards academic integrity, and on their cognitive development, all of which can influence the way they interact with artificial intelligence tools like ChatGPT in an educational environment. In particular, they show that younger people and those who have grown up in the digital age are more adept at navigating and using technology in their academic activities (Chan & Lee, 2023). They may have a higher level of comfort with AI-driven tools and a greater propensity to incorporate them into their learning processes. Consequently, younger users may be more inclined to turn to ChatGPT for assistance with educational tasks, such as generating written content or solving problems (Akgun & Greenhow, 2022; Vandenberg & Mott, 2023).

However, Holmes et al. (2022) show that the relationship between age and unethical behavior such as plagiarism is complex. While younger individuals may be more familiar with technology, they may also be less experienced with academic integrity standards. As individuals mature and progress through their educational journeys, they may develop a deeper understanding of the importance of originality, attribution, and ethical behavior in academic work (Chan & Hu, 2023).

Furthermore, Holmes et al. (2022) and Nguyen et al. (2023) point out that age-related factors such as cognitive maturity and moral development can influence individuals' decision-making processes regarding the ethical use of AI tools. Older individuals may possess a more developed sense of ethical reasoning and a greater awareness of the consequences of unethical behavior, leading them to exercise more caution and discretion when utilizing ChatGPT for educational purposes (Borenstein & Howard, 2021; Dehouche, 2021; Jereb et al., 2018). Studies made with higher education students show that there is not a clear difference for university users of ChatGPT between using it without attribution constituting plagiarism and using it to generate ideas that are developed in original works (Teel et al., 2023). As a
conclusion, Sullivan et al., (2023) and Teel et al., (2023) point out that some stu-
dents use it to improve their works and others to do the tasks without even making
the effort of changing the responses.

Additionally, generational differences in attitudes towards technology and educa-
tion may shape how individuals of different ages perceive the ethical implications of
using AI tools like ChatGPT (Borenstein & Howard, 2021; Chu et al., 2022). Older
generations may consider technology as a supplementary resource, while younger
generations may see it as an integral part of their academic toolkit (Chu et al., 2022).

Exploring the influence of age on the connection between ChatGPT usage for educational objectives and unethical conduct like plagiarism requires careful consideration of various factors, including technological proficiency, ethical awareness, and attitudes towards education (Stypinska, 2023). By examining these dynamics, researchers can gain insights into how age-related factors shape individuals’interactions with AI technologies in educational settings and inform strategies for promoting academic integrity across diverse age groups.

The Influence of the Educational Background

Chou et al. (2022) and Kaya et al. (2024) defend that the influence of educational background influences the use of ChatGPT to achieve educational objectives and is related to unethical behavior such as plagiarism. These investigations have indicated that educational background encompasses several factors, including academic experiences, learning styles, and familiarity with academic standards, all of which can shape people’s interactions with AI tools like ChatGPT.

Students with diverse educational backgrounds may approach the use of ChatGPT differently based on their prior experiences and training (Kaya et al., 2024). For example, individuals with a strong foundation in research methodologies and academic writing may view ChatGPT as a supplementary tool to aid in their learning process, using it to enhance their understanding of complex concepts or to generate ideas for further exploration (Xu et al., 2021). On the other hand, students with limited exposure to academic conventions or those who struggle with certain subjects may be more inclined to rely on ChatGPT for assistance with assignments or projects (von Garrel & Mayer, 2023).

Moreover, students from different academic disciplines may have varying degrees
of familiarity with technology and AI tools. For instance, researches such as Lam
et al., (2014) and Pumptow and Brahm (2023) have shown that students in STEM
(Science, Technology, Engineering, and Mathematics) fields may be more accus-
tomed to making use of technology in their studies and may be more open to incor-
porating AI tools like ChatGPT into their learning routines. Conversely, students in
humanities or social sciences disciplines may approach technology adoption with
more caution or skepticism, potentially influencing their attitudes towards using
ChatGPT for educational purposes.

Furthermore, educational background can influence individuals' understanding of
academic integrity and ethical conduct in scholarly work. Dehouche (2021) Nguyen
et al. (2023) note that students who have received explicit instruction on plagiarism
and citation practices may be more aware of the ethical implications of using ChatGPT to generate content without proper attribution. On the other hand, students who have not received adequate education or training on academic integrity may be less aware of these issues and may be more susceptible to engaging in unethical behavior unintentionally (Borenstein & Howard, 2021; Cotton et al., 2024; Kumar et al., 2020).

Given this background, the objectives of the present research were:

- O1: To detect and analyze the relationship between the frequency of use of ChatGPT for educational objectives and unethical behaviors such as plagiarism.

- O2: To explore the influence of personal factors such as gender, age, university year and educational background on the frequency of use of ChatGPT for dishonest behavior.

# Methodology

## Sample

Regarding the sample, 503 university students participated in the study, with a mean age of 20.47 years old (SD = 5.03). Specifically, this sample consisted of 155 males and 348 females, with the majority being students in degrees related to the field of Education. Out of which, 326 were students in a degree in Primary Education, 128 students in a degree in Early Childhood Education, and 49 were students from related degrees (Pedagogy, Social Education, Psychology, Social Work, etc.). Concerning the academic year, 171 were enrolled in their first year of university, 136 were in their second year, 167 were in their third year, and 29 students were in their final years. Table 1 outlines a summary of the sample.

According to the previous results of the students in the last academic year, the last year the students had an average grade of 7.77 out of 10 points (SD = 0.72). If we set apart the sample by gender, in the past year the male students obtained an average grade of 7.46 (SD = 0.66) and the female students finished with an average grade of 7.91 (SD = 0.71).

## Instruments

The main instruments were implemented through a questionnaire:

* *Frequency of use of ChatGPT for academic purposes:* One only item, measured in a 5-point Likert scale was designed to collect the data related to the usage of Chat GPT for academic purposes of students. The ad-hoc item created was as follows: “How often do you use ChatGPT or similar artificial intelligences for drafting your essays, reports, or other academic documents?”

* *Plagiarism:* For this variable, the Academic Dishonesty Scale in college students was used in its Spanish version (Domínguez-Lara & Lingan-Huaman, 2022). The scale assesses different factors related to Academic Dishonesty, and in the dimension

<figure><img src="image_4.png" /></figure>
<table><caption>**Table 1** Summary of the sample</caption><thead><tr><th></th><th>f</th><th>%</th></tr></thead><tbody><tr><td><strong>Gender</strong></td><td></td><td></td></tr><tr><td>Male</td><td>155</td><td>30.8%</td></tr><tr><td>Female</td><td>348</td><td>69.2%</td></tr><tr><td><strong>Age</strong></td><td></td><td></td></tr><tr><td>18 to 19 years old</td><td>243</td><td>48.3</td></tr><tr><td>20 to 21 years old</td><td>157</td><td>31.3</td></tr><tr><td>22 to 23 years old</td><td>72</td><td>14.3</td></tr><tr><td>24 or more years old</td><td>31</td><td>6.2</td></tr><tr><td><strong>Academic year</strong></td><td></td><td></td></tr><tr><td>1st year</td><td>171</td><td>34.0%</td></tr><tr><td>2nd year</td><td>136</td><td>27.0%</td></tr><tr><td>3rd year</td><td>167</td><td>33.2%</td></tr><tr><td>4th year</td><td>24</td><td>4.8%</td></tr><tr><td>5th year</td><td>5</td><td>1%</td></tr><tr><td><strong>University degree</strong></td><td></td><td></td></tr><tr><td>Early Childhood Education</td><td>128</td><td>25.4%</td></tr><tr><td>Primary Education</td><td>326</td><td>64.8%</td></tr><tr><td>Others</td><td>49</td><td>9.44%</td></tr><tr><td><strong>TOTAL</strong></td><td><strong>503</strong></td><td><strong>100%</strong></td></tr></tbody></table>

sion “Plagiarism” the students were asked with what frequency they had committed academic plagiarism. Concretely, the dimension of Plagiarism consisted of 4 items measured on a 5 point Likert scale (1-Never to 5-Always), such as “Copying some ideas from other authors without citing the original source”. The goodness of fit of the three-dimensional model assessing Cheating, Plagiarism and Document forgery was excellent (CFI = 0.971; RMSEA = 0.068), as well as the reliability of the plagiarism dimension, measured by Alpha (α = 0.769) and Omega (ω = 0.771).

## Procedure

Data was collected through the digital administration of the questionnaire, specifically using Forms. The questionnaire comprised, firstly, questions regarding personal data such as gender, age, as well as academic data such as academic year, the type of studies the students were pursuing, or their average grades obtained in the previous academic year. Additionally, the questionnaire included the mentioned scales. Overall, the questionnaire was administered to groups of students accessible to the researchers of this study, who answered during working hours. Furthermore, permission for participation from other faculty groups was requested from the dean's office. Those students answered out the academic hours. Before starting the questionnaire administration, all students were informed about the general objective of the research, the expected response time, confidentiality, and anonymization of data. All students participated voluntarily and accepted the conditions and procedures for participation in the study.
## Data Analysis

The software used for the data analyses was SPSS Statistics 27, as well as its Pro-
cess macro for moderation analysis. Firstly, relevant descriptive analyses were con-
ducted to understand descriptive results such as means and standard deviations.
Additionally, Cronbach's alpha was calculated to assess the reliability of the scale.
Next, correlations between different variables were analyzed using Pearson's cor-
relation coefficient. Secondly, moderation analyses were performed. For this analy-
sis, the Plagiarism value was treated as the dependent variable and the frequency of
ChatGPT usage as the independent variable. Personal and academic variables were
analyzed as moderating variables. This moderation analysis was carried out using
10,000 bootstrap samples and employing the pick-a-point method (-1SD, Mean,
+1SD) to choose the various categories of the moderating variable.

## Results

Firstly, Cronbach's alpha coefficient was calculated for the Plagiarism dimension, composed of five items, in order to assess the dimension's reliability. The reliability was optimal (α = 0.875) as it exceeded the α > 0.70 cutoff (Taber, 2018).

Regarding the means and standard deviations of the different dimensions, the level of plagiarism reported by students was quite low, although the standard deviation was large (M = 2.03, SD = 0.911). Additionally, it was notable that students reported a fairly low frequency of using ChatGPT (M = 1.88, SD = 0.932). An analysis of bivariate correlations among variables was added to the study of means. In Table 2, if we examine the significant correlations, it can be observed that Plagiarism correlated positively with the frequency of using ChatGPT (r = 0.196**; *p* = < 0.001). Furthermore, a negative correlation is observed between Plagiarism and the gender variable, which was coded as 1 for male and 2 for female (r = -0.105*; *p* = 0.019). Regarding the frequency of using ChatGPT, it also correlated negatively with the gender variable (r = -0.227**; *p* = < 001). Moreover, it is observed that the frequency of using ChatGPT correlated negatively with the variable of academic outcomes (r = -0.172**; *p* = < 0.001), which was measured by previous results (M = 7.77; SD = 0.722). Additionally, there were other significant correlations among the descriptive variables of the sample that provide more information about its distribution, such as the positive correlation between university year and age (r = 0.246**; *p* = < 0.001), which deviation has not wide variation, neither in age (M = 20.47; SD = 5.03) or in university year (M = 2.12; SD = 0.970), due to the characteristics of the sample. In addition, a negative correlation between age and gender (r = -0.093*; *p* = 0.038) was found, which gives information about the presence of more males in higher academic courses. Regarding the academic results, a negative correlation was founded with age (r = -0.240**; *p* = < 0.001) and with the university year (r = -0.095*; *p* = 0.033).

Afterwards, a moderation analysis was conducted with plagiarism as the dependent variable, the frequency of using ChatGPT as the independent variable, and gender, age, university year, and academic results as moderating variables.
<figure><img src="image_5.png" /><figcaption>**Table 2** Descriptive statistics, correlations and reliability values (N = 503)</figcaption></figure>

Cronbach's alpha values in the main diagonal between brackets

PLG Plagiarism, GPT frequency of use of ChatGPT, GND gender, AGE age, UYE university year, RES academic previous results

From the moderation analysis, despite the existing correlations between the frequency of use of ChatGPT and the level of plagiarism, a low level of causality between the independent and dependent variables was found, resulting in the frequency of use of ChatGPT being a poor predictor of plagiarism by students. As can be observed in Table 3, this is confirmed in all moderation analyses conducted, where none yield a causal relationship between the independent variable (frequency of use of ChatGPT) and the dependent variable (Plagiarism).

Although, regarding the moderation variables, different models were conducted. In model 1 it can be observed that gender, despite the correlations founded, has not a moderation effect between the frequency of use of ChatGPT and the levels of plagiarism. Regarding models 2 and 3, a moderation effect of the moderation variable was founded. In these cases, age and university year were defined as moderation variables. Even if the explained variance in the causality is low in both age (R² = 0.060, 6%) and university year (R² = 0.048, 4.8%), it can be seen that age and university year are predictors of the levels of plagiarism in this case (β = 0.051; *p* = 0.001 and β = -0.202; *p* = 0.032, respectively). In addition, the causality effect of

<footnote>* *p* < .05; ** *p* < .001;</footnote>
the frequency of use of ChatGPT on the plagiarism level appears to be moderated by those two variables, age (β = 0.016; *p* = < 0.001), as can be observed in Fig. 1 and university year (β = 0.099; *p* = 0.036), represented in Fig. 2.

As can be observed, on Figs. 1 and 2, the plagiarism caused by the use of ChatGPT increases as students advance in age and university year.

Discussion

The first aim of this study was to analyze the relationship between the frequency of use of ChatGPT for educational objectives and plagiarism. The results suggest a main correlation between both variables. As shown in Table 2, the results of our correlation analysis show a positive correlation between the plagiarism level and the use of ChatGPT. This indicates the presence of a weak positive relationship between these two parameters. Therefore, this potentially suggests that students may tend to use ChatGPT for plagiarism or that they are influenced by its use. Although, the moderation analyses reflect that no causal effect can directly be assumed between the use of ChatGPT and plagiarism. At the same time, the aim of this study was to explore the influence of personal factors such as gender, age, university year and educational background on the frequency of use of ChatGPT for dishonest behavior. Thus, we decided to conduct a moderation analysis in order to find out if any of the other parameters affect this relationship.

Focusing on the gender parameter, a moderate negative correlation was found with the use of ChatGPT, in addition to a weak negative correlation with plagiarism. These results suggest a relationship between males with the use of ChatGPT and with plagiarism. Thus, studies in the U.K. and the U.S. also found that male students were more likely to commit academic plagiarism than female students (Jiang et al., 2013). These correlations strengthen previous studies highlighting that males are more likely to commit plagiarism compared to female students, although they know it to be unethical (Jereb et al., 2018). Regarding gender, there was no moderation effect found. Thus, we cannot assume from our data that there exists a direct relationship. Though, according to previous research, it could be a variable to be considered, as other studies suggest.

A weak negative relationship was also found between the previous results of students and the use of ChatGPT. Related to the students' background, the results did not show a moderation effect of this variable. Though, the correlation suggests some kind of relationship that should be considered on further research. In this case, these results could be related to the level of motivation of students, which has been previously related to plagiarism of students in higher education (Espiñeira-Bellón et al., 2022; Šprajc et al., 2017).

Moreover, a positive significant correlation was found between the university year and the age, even the weight of this factor is low, which is normal and logical according to the context. However, no significant correlations between age and university year with other variables were found. Furthermore, there exists an observable trend where the use of ChatGPT for plagiarism increases during the academic
years, and as age increases, as shown in Table 3, Figs. 1 and 2. Several factors can contribute to this phenomenon, and are the main findings to discuss in this study.

Firstly, individuals may engage in plagiarism to merge with the group norms, seeking inclusion or acceptance within their academic classmates. Related to this, as academic challenges become more demanding, students might feel pressured to use ChatGPT to fill gaps in their understanding and meet the increasing expectations placed upon them (Fatima et al., 2020).

Additionally, the use of ChatGPT might also lead to better grades and academic performance because they can present assignments that appear more polished and thorough. So, if they aren't caught or face consequences for plagiarism, students may see it as a shortcut to success without any negative repercussions (Malik et al., 2021). This perception of impunity could encourage them to continue plagiarizing in order to achieve their academic goals.

Lastly, it is possible that students, particularly in their initial years of university education, may not possess a comprehensive understanding of the boundaries between legitimate academic conduct and plagiarism, as a result, they may unintentionally turn to ChatGPT without realizing the ethical issues involved. Previous research has noted that currently students may not differentiate between primary or secondary sources, or ignore academic ethics when they start their higher education studies (Abbas et al., 2021; Chang et al., 2015; Olivia-Dumitrina et al., 2019; Šprajc et al., 2017).

As major limitations of the study, firstly, we find some limitations at the level of research design. Some questions into the reasons for using ChatGPT, or the strategies and tendencies while prompting, in addition to its frequency of use, would have shed more light on interpreting the results. Also related to the study design, despite the anonymity of participation, there is a potential limitation associated with the use of self-report instruments that must be taken into account. In future research, alternative measures could be considered to strengthen validity (Pekrun, 2020). As Fritz et al. (2024) suggest, there is a need to improve the measurement of academic dishonesty, and that would contribute to promoting a psychometric basis for the study of academic plagiarism. Secondly, we encounter some methodological limitations, such as the limited variety of the sample, focused on a single university, which hinders the transferability of the findings. Indeed, these are limitations that the authors acknowledge as challenges for future research. Moreover, given the study's outcomes, future research on this topic is recommended to delve into how to integrate the use of ChatGPT in higher education teaching.

The findings of the study show that there is a necessity to rethink how AI is used in university education taking into consideration factors such as age and the academic year. With a future perspective, the proposals of Camacho et al. (2023) and the results obtained are related, especially in the regulations of graduate and master degrees to employ and use responsibly to achieve proper use and full development of student competencies. It is evident the potential of tools like ChatGPT, so to make the most of them a fundamental shift in the approaches of both educators and students is imperative (Akgun & Greenhow, 2022; Cotton et al., 2024).

Also looking to the future, the study results support that teachers must adapt their active methodologies to accommodate the integration of AI technologies to elaborate
<table><caption>Table 3 Outputs of the moderation analysis</caption><thead><tr><th>Moderator</th><th>Path</th><th>β [95% CI]</th><th>SE</th><th>p</th></tr></thead><tbody><tr><td>Gender<br>(GND)<br>(R<sup>2</sup> = .042)</td><td>GPT→PLG</td><td>.127 [-.175; .431]</td><td>.154</td><td>.409</td></tr><tr><td></td><td>GND→PLG</td><td>-.187 [-.588; .214]</td><td>.204</td><td>.360</td></tr><tr><td></td><td>GPT*GND→PLG</td><td>.030 [-.148; .209]</td><td>.091</td><td>.735</td></tr><tr><td>Age (AGE)<br>(R<sup>2</sup> = .060)</td><td>GPT→PLG</td><td>-.381 [-.723; -.039]</td><td>.174</td><td>.289</td></tr><tr><td></td><td>AGE→PLG</td><td>-.051 [-.084; -.019]</td><td>.016</td><td>.001</td></tr><tr><td></td><td>GPT*AGE→PLG</td><td>.028.011; .044]</td><td>.008</td><td>&lt;.001</td></tr><tr><td>University Year<br>(UYE)<br>(R<sup>2</sup> = .048)</td><td>GPT→PLG</td><td>-.018 [-.231; .195]</td><td>.108</td><td>.868</td></tr><tr><td></td><td>UYE→PLG</td><td>-.202 [-.386; -.017]</td><td>.094</td><td>.032</td></tr><tr><td></td><td>GPT*UYE→PLG</td><td>.099 [ .007; .192]</td><td>.047</td><td>.036</td></tr><tr><td>Previous Results<br>(RES)<br>(R<sup>2</sup> = .044)</td><td>GPT→PLG</td><td>-.593 [-1.525; -.338]</td><td>.474</td><td>.211</td></tr><tr><td></td><td>RES→PLG</td><td>-.170 [-.413; .072]</td><td>.123</td><td>.169</td></tr><tr><td></td><td>GPT*RES→PLG</td><td>.102 [-.018; .222]</td><td>.061</td><td>.096</td></tr></tbody></table>

PLG Plagiarism. GPT frequency of use of ChatGPT, GND gender, AGE age, UYE university year, RES academic previous results

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 1 Age moderation effect between frequency of use of ChatGPT and Plagiarism</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 2 University year moderation effect between frequency of use of ChatGPT and Plagiarism</figcaption></figure>

and use knowledge including immediate self-assessment of usage and level of academic dishonesty (Nguyen et al., 2023; Reiss, 2021). Also, other variables related to plagiarism in higher education could be considered in future research, such as students' motivation (Šprajc et al., 2017) or academic integrity (Moya et al., 2024).
One approach towards achieving this integration is the establishment of distinct levels of usage for AI tools. For instance, ChatGPT can serve for various functions ranging from simple tasks such as text translation and grammar correction to more complex roles, such as idea generation and conceptual understanding. By delineating these levels of usage, educators can promote students' engagement with AI tools in different levels and set up limits (Cotton et al., 2024; Nguyen et al., 2023).

Furthermore, as Delgado et al. (2024) and Sullivan et al. (2023) presented in their studies, it is deduced that educators in the future university faculty will be able to employ ChatGPT as a tool to improve their teaching practices. This includes using the tool to generate new and original activities or adapting existing activities to accommodate different learning styles and needs. However, it is crucial to understand that the effective integration of AI tools needs a corresponding evolution in teaching methods. For example, faculty at the University of the Basque Country-Euskal Herriko Unibertsitatea will be able to integrate activities within the IKDi3 model so that the use will provide automatic responses using tools such as ChatGPT, and thus constitute the starting point for developing cognitive and emotional skills such as critical thinking and problem-solving skills (Dehouche, 2021; Sullivan et al., 2023).

Students also need to change their view on these tools (Chou et al., 2022; Kaya et al., 2024). By introducing students to the ethical considerations and responsible usage of AI tools early in their educational journey, educators can gradually introduce a culture of responsible and honest use of AI. This proactive approach not only mitigates the risk of plagiarism but also empowers students to take advantage of the full potential of AI tools as aids in their learning process. It is essential for students to understand the concept of plagiarism and recognize that the use of AI tools does not always correspond to cheating or academic dishonesty. Promoting a conscious and informed approach to AI tool usage is crucial. This requires clarifying the boundaries between legitimate usage and unethical practices, thereby ensuring that students utilize AI tools as aids rather than shortcuts. Related to students' usage of AI at university, further research on how they are using chatbots such as ChatGPT will be crucial for the future design of teaching and learning methods adapted to classrooms in the near future. Recent research suggests that there are different tendencies in students' prompting strategies that may be related to their level of engagement, motivation, and, for instance, their learning (Sawalha et al., 2024). However, more research is needed in this area to help educators develop more effective strategies for promoting learning through AI.

Continuing with a forward-looking perspective, to address issues related to plagiarism, an effective strategy might involve integrating AI resources such as ChatGPT into educational programs within academia. These tools could serve as educational resources, guiding higher education students in their responsible and ethical use. By doing so, students can take advantage of AI's capabilities for research and use it for idea development, while they are respecting the principles of academic honesty and integrity (Teel et al., 2023).

In sum, this paper we conducted a study to analyze the relationship between the use of ChatGPT and plagiarism, and the influence of some personal variables, such as gender, age, university year and previous results. The results of this research highlight a significant influence of the use of ChatGPT on plagiarism as students progress

<figure><img src="image_9.png" /></figure>
in their university years. In addition, a relationship was found between the use of ChatGPT and plagiarism. As well, a relationship of the use of ChatGPT with other personal variables, such as gender and previous results of the students, was found.

Finally, despite the limitations found in this study, we can conclude that there is a need to address the use of AI from a perspective that mitigates its risks throughout university education. Additionally, we can conclude that there are multiple future research avenues related to this topic, which are imperative in the field of higher education teaching. In fact, we can conclude that these future lines of research are already imperative, as stated by Schön et al., (2023, p.1) “the tipping point for this transformation has already been reached. It is in our hands to shape this transformation”.

**Acknowledgements** Acknowledgment to the institution where the research was applied, as well as to the participating students.

**Authors' Contributions** All: theoretical framework, discussion writing. LC, ND and JE-U: instrument design, investigation. LC and ND: methodology, result analysis. MLG and HK: results revision.

**Funding** Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

**Data Availability** The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

**Code Availability** The code used and/or analyzed during the current study are available from the corresponding author on reasonable request.

## Declarations

**Ethical approval** The protocols followed are in accordance with the ethical standards of our institution and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The required procedures established by the institution for the collection of anonymous data through indirect recruitment have been fulfilled: https://www.ehu.eus/es/web/ceid/ceish/comite#Comit%C3%A9:CEISH4

**Ethical Standards** Following the rules of good scientific practice required by your submission guidelines we declare:

The manuscript has not been submitted to more than one journal for simultaneous consideration.

It is a single original study that has not been published in any form or language.

The results are presented clearly, honestly, and without fabrication, falsification or inappropriate data manipulation.

No data, text, or theories by others are presented as if they were the author's own.

**Consent to Participate** All participants consented to their participation in the study through the *Google-Forms* application, prior to the requirement of any data about them.

The information for the participants was the following (translated from Spanish):

Below you will find a series of questions aimed at gaining a better understanding of how artificial intelligence is being used among university students. This information is of great use for future and current professors, as it allows them to have an insight into what we have in class today. We appreciate your sincerity in the responses, which will neither be right nor wrong, and will take you around 20 min. All responses are anonymous and confidential, and will not affect any rights you may already have access to. More information about terms and conditions in https://bit.ly/3utqRMY.

**Conflicts of interest** The authors Campo, Delgado, Etxabe-Urbieta, Kanso and Gueye confirm that the content of this manuscript has not been published, and has not been submitted for publication anywhere, according to the Duplicate publication policy.
**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Abbas, A., Fatima, A., Arrona-Palacios, A., Haruna, H., & Hosseini, S. (2021). Research ethics dilemma in higher education: Impact of internet access, ethical controls, and teaching factors on student plagiarism. *Education and Information Technologies, 26*(6), 6109–6121. https://doi.org/10.1007/s10639-021-10595-z

Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. *AI and Ethics, 2*(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7

Al-Ansi, A. M., Jaboob, M., Garad, A., & Al-Ansi, A. (2023). Analyzing augmented reality (AR) and virtual reality (VR) recent development in education. *Social Sciences & Humanities Open, 8*(1), 132–156. https://doi.org/10.1016/j.ssaho.2023.100532

Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. *AI and Ethics, 1*(1), 61–65. https://doi.org/10.1007/s43681-020-00002-7

Camacho, M. U., Tambasco, P., Martínez, S., & Correa, M. G. (2023). El impacto de la Inteligencia Artificial en la educación. Riesgos y potencialidades de la IA en el aula. *RiiTE Revista Interuniversitaria De Investigación En Tecnología Educativa, 15*, 41–57. https://doi.org/10.6018/riite.584501

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. *International Journal of Educational Technology in Higher Education, 20*(1), 43–61. https://doi.org/10.1186/s41239-023-00411-8

Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? *Smart Learning Environments, 10*(1), 60–83. https://doi.org/10.1186/s40561-023-00269-3

Chang, C., Chen, Y., Huang, Y., & Chou, C. (2015). Why do they become potential cyber-plagiarizers? Exploring the alternative thinking of copy-and-paste youth in Taiwan. *Computers & Education, 87*, 357–367. https://doi.org/10.1016/j.compedu.2015.07.006

Chou, C. M., Shen, T. C., Shen, T. C., & Shen, C. H. (2022). Influencing factors on students’ learning effectiveness of AI-based technology application: Mediation variable of the human-computer interaction experience. *Education and Information Technologies, 27*(6), 8723–8750. https://doi.org/10.1007/s10639-021-10866-9

Chu, C. H., Nyrup, R., Leslie, K., Shi, J., Bianchi, A., Lyn, A., McNicholl, M., Khan, S., Rahimi, S., & Grenier, A. (2022). Digital ageism: Challenges and opportunities in artificial intelligence for older adults. *The Gerontologist, 62*(7), 947–955. https://doi.org/10.1093/geront/gnb167

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. *Innovations in Education and Teaching International, 61*(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148

Dehouche, N. (2021). Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3). *Ethics in Science and Environmental Politics, 21*, 17–23. https://doi.org/10.3354/esep00195

Delgado, N., Carrasco, L. C., de la Maza, M. S., & Etxabe-Urbieta, J. M. (2024). Aplicación de la Inteligencia Artificial (IA) en educación: Los beneficios y limitaciones de la IA percibidos por el profesorado de educación primaria, educación secundaria y educación superior. *Revista Electrónica Interuniversitaria De Formación Del Profesorado, 27*(1), 207–224. https://doi.org/10.6018/reifop.577211

Dempere, J., Modugu, K., Hesham,A., Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. *Frontiers in Education, Sec. Higher Education, 8*. https://doi.org/10.3389/feduc.2023.1206936
Espíñera-Bellón, E. M.; Pérez-Crego, M. C., Muñoz-Cantero, J. M. (2022). Relationship between the lack of motivation and the academic plagiarism commission. *Profesorado: Revista de Curriculum y Formación del Profesorado, 26*(2), 203-225. https://doi.org/10.30827/profesorado.v26i2.21194

Fatima, A., Sunguh, K., Abbas, S., Mannan, A., Hosseini, S. (2020). Impact of pressure, self-efficacy, and self-competency on students' plagiarism in higher education. *Accountability in Research Ethics, Integrity and Policy, 27*(1). https://doi.org/10.1080/08989621.2019.1699070

Fernández-Morante, C., López, B. C., Casal-Otero, L., & León, F. M. (2023). Teachers' digital competence. The case of the university system of Galicia. *Journal of New Approaches in Educational Research, 12*(1), 62-76. https://doi.org/10.7821/naer.2023.1.1139

Fritz, T., González-Cruz, H., Janke, S. & Daumiller, M. (2024). How to best measure academic dishonesty in students: a systematic review of self-report assessment methods and psychometric quality. *European Journal of Psychological Assessment, 40*(6). https://doi.org/10.1027/1015-5759/a000861

Gabriel, D. (2021). Race, ethnicity and gendered educational intersections. *Gender and Education, 33*(7), 791-797. https://doi.org/10.1080/09540253.2021.1967667

Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., Pearson, A. T. (2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. *Npj Digital Medicine, 6*(75). https://doi.org/10.1038/s41746-023-00819-6

Grande-de-Prado, M., Cañón, R., García-Martín, S., & Cantón, I. (2020). Digital competence and gender: Teachers in training. A case study. *Future Internet, 12*(11), 204-219. https://doi.org/10.3390/fi12110204

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. *International Journal of Artificial Intelligence in Education, 32*(3), 504-526. https://doi.org/10.1007/s40593-021-00239-1

Hsu, T. W., Tsai, S. J., Ko, C. H., Thompson, T., Hsu, C. W., Yang, F. C., Tsai, C. K., Tu, Y. K., Yang, S. N., Tseng, P. T., Liang, C. S., & Su, K. P. (2023). Plagiarism, quality, and correctness of ChatGPT-generated vs human-written abstract for research paper. *Preprint with The Lancet*. SSRN. https://doi.org/10.2139/ssrn.4429014

Huang, K.-T., Ball, C., Francis, J., Ratan, R., Boumis, J., & Fordham, J. (2019). Augmented versus virtual reality in education: An exploratory study examining science knowledge retention when using augmented reality/virtual reality mobile applications. *Cyberpsychology, Behavior, and Social Networking, 22*(2), 105-110. https://doi.org/10.1089/cyber.2018.0150

Jarrah, A. M., Wardat, Y., & Fidalgo, P. (2023). Using ChatGPT in academic writing is (not) a form of plagiarism: What does the literature say? *Online Journal of Communication and Media Technologies, 13*(4), e202346. https://doi.org/10.30935/ojcmt/13572

Jereb, E., Urh, M., Jerebic, J., & Šprajc, P. (2018). Gender differences and the awareness of plagiarism in higher education. *Social Psychology of Education, 21*(2), 409-426. https://doi.org/10.1007/s11218-017-9421-y

Jiang, H., Emmerton, L. M., & McKauge, L. (2013). Academic integrity and plagiarism: A review of the influences and risk situations for health students. *Higher Education Research & Development, 32*, 369-380. https://doi.org/10.1080/07294360.2012.687362

Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, ai anxiety, and demographic factors in attitudes toward Artificial Intelligence. *International Journal of Human-Computer Interaction, 40*(2), 497-514. https://doi.org/10.1080/10447318.2022.2151730

Khalil, M., Er, E. (2023). Will ChatGPT get you caught? Rethinking of plagiarism detection. In P. Zaphiris spsampsps A. Ioannou (Eds.), Learning and collaboration technologies. *HCII 2023. Lecture Notes in Computer Science, vol 14040*. Springer, Cham. https://doi.org/10.1007/978-3-031-34411-4_32

Kumar, R., Eaton, S. E., Mindzak, M., Morrison, R. (2020). Academic integrity and Artificial Intelligence: An overview. En S. E. Eaton (Ed.), *Handbook of Academic Integrity* (pp. 1-14). Springer Nature. https://doi.org/10.1007/978-981-287-079-7_153-1

Lam, P., McNaught, C., Lee, J., & Chan, M. (2014). Disciplinary difference in students' use of technology, experience in using eLearning strategies and perceptions towards eLearning. *Computers & Education, 73*, 111-120. https://doi.org/10.1016/j.compedu.2013.12.015

Macdonald, C., Adeloye, D., Sheikh, A., & Rudan, I. (2023). Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis. *Journal of Global Health, 13*. https://doi.org/10.7189/jogh.13.01003

Malik, M. A., Mahroof, A., & Ashraf, M. A. (2021). Online university students' perceptions on the awareness of, reasons for, and solutions to plagiarism in higher education: The development
of the AS&P model to combat plagiarism. *Applied Sciences*, 11(24), 12055. https://doi.org/10.3390/app112412055

Moleka, P. (2023). Exploring the role of Artificial Intelligence in education 6.0: enhancing personalized learning and adaptive pedagogy. *Preprints*, 1, 1–7. https://doi.org/10.20944/preprints202309.0562.v1

Moya, B., Eaton, S., Pethrick, H., Hayden, A., Brennan, R., Wiens, J., McDermott, B. (2024). Academic integrity and artificial intelligence in higher education (HE) contexts: A rapid scoping review. *Canadian Perspectives on Academic Integrity*, 7(3). https://doi.org/10.55016/ojs/cpai.v7i3.78123

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P.T. (2023). Ethical principles for artificial intelligence in education. *Education and Information Technologies*, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w

Olivia-Dumitrina, N., Casanova, M., & Capdevila, Y. (2019). Academic writing and the internet: Cyber-plagiarism amongst university students. *Journal of New Approaches in Educational Research*, 8(2), 112–125.

Pekrun, R. (2020). Self-Report is indispensable to assess students’ learning. *Frontline Learning Research*, 8(3), 185–193. https://doi.org/10.14786/flr.v8i3.637

Pumptow, M., & Brahm, T. (2023). Higher education students differ in their technology use. *Computers and Education Open*, 5, 100–149. https://doi.org/10.1016/j.caeo.2023.100149

Raza, F. (2023). AI in education: Personalized learning and adaptive assessment. *Cosmic*, 2, 280–297. https://doi.org/10.13140/RG.2.2.24796.77446

Reiss, M. J. (2021). The use of AI in education: Practicalities and ethical considerations. *London Review of Education*, 19, 1–14. https://doi.org/10.14324/LRE.19.1.05

Rizvi, M. (2023). Investigating AI-powered tutoring systems that adapt to individual student needs, providing personalized guidance and assessments. *The Eurasia Proceedings of Educational and Social Sciences*, 31, 67–73. https://doi.org/10.55549/epess.1381518

Sawalha, G., Taj, I., & Chaufan, A. (2024). Analyzing student prompts and their effect on ChatGPT's performance. *Cogent Education*, 11(1), 2397200. https://doi.org/10.1080/2331186X.2024.2397200

Schön, E. M., Neumann, M., Hofmann-Stölting, C., Baeza-Yates, R., Rauschenberger, M. (2023). How are AI assistants changing higher education? *Frontiers in Computer Science, Sec. Digital Education*, 5. https://doi.org/10.3389/fcomp.2023.1208550

Šprajc, P., Urh, M., Jerebic, J., Trivan, D., & Jereb, E. (2017). Reasons for plagiarism in higher education. *Organizacija*, 50(1), 33–45. https://doi.org/10.1515/orga-2017-0002

Stypinska, J. (2021). Ageism in AI: New forms of age discrimination in the era of algorithms and artificial intelligence. *Proceedings of the 1st International Conference on AI for People: Towards Sustainable AI, CAIP 2021*. https://doi.org/10.4108/eai.20-11-2021.2314200

Stypinska, J. (2023). AI ageism: A critical roadmap for studying age discrimination and exclusion in digitalized societies. *AI & SOCIETY*, 38(2), 665–677. https://doi.org/10.1007/s00146-022-01553-5

Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. *Journal of Applied Learning and Teaching*, 6(1), 31–40. https://doi.org/10.37074/jalt.2023.6.1.17

Taber, K. S. (2018). The use of Cronbach's alpha when developing and reporting research instruments in science education. *Research in Science Education*, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2

Teel, Z. A., Wang, T., & Lund, B. (2023). ChatGPT conundrums: Probing plagiarism and parroting problems in higher education practices. *College & Research Libraries News*, 84, 205–208. https://doi.org/10.5860/crln.84.6.205

Unterhalter, E., Longlands, H., & Peppin Vaughan, R. (2022). Gender and intersecting inequalities in education: Reflections on a framework for measurement. *Journal of Human Development and Capabilities*, 23(4), 509–538. https://doi.org/10.1080/19452829.2022.2090523

Vandenberg, J., Mott, B. (2023). “AI teaches itself”: exploring young learners’ perspectives on artificial intelligence for instrument development. *Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1*, 485–490. https://doi.org/10.1145/3587102.3588778

Von Garrel, J., & Mayer, J. (2023). Artificial Intelligence in studies—Use of ChatGPT and AI-based tools among students in Germany. *Humanities and Social Sciences Communications*, 10(1), 1–9. https://doi.org/10.1057/s41599-023-02304-7

Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. *Eurasia Journal of Mathematics, Science and Technology Education*, 19(7), em2286. https://doi.org/10.29333/ejmste/13272
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. *The Innovation, 2*(4), 100-179. https://doi.org/10.1016/j.xinn.2021.100179

Zhao, T. (2022). *How gender stereotypes impact students' academic achievement* (pp. 911–918). https://doi.org/10.2991/978-2-494069-45-9_110

Domínguez-Lara, S. & Lingan-Huamán, S.K. (2022). Escala de deshonestidad académica en estudiantes universitarios: estructura interna y asociación con autoeficacia académica. *Anuario de Psicología, 52*(3), 209-219. https://doi.org/10.1344/ANPSIC2022.52/3.2

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Campo, Lucía** PhD in Education (University of Deusto, Bilbao, Spain), Master in Innovation and development of competencies, Graduate in Psychology and Psychopedagogy. Work experience as a member of the research and teaching staff in the University of Deusto, also has worked as an associate lecturer in other universities. She has also participated as a consultant in projects to improve the planning and evaluation of teaching (University of Deusto). For some years she has worked as a therapeutic educator and teacher in a school. Currently assistant lecturer in the Faculty of Education of Bilbao in the University of the Basque Country (UPV/EHU), specifically for the Department of Didactics and School Organisation. She combines the teaching duties with research and publication of papers and communications related to education. Her main themes of research are focused on the definition and analysis of teaching competencies in the EHEA, on the evaluation and reflection of teaching among different topics, such as the development of critical thinking, or the use of technologies.

**Delgado, Nahia** Doctor Cum Laude in Psychodidactics (Psychology of Education and Specific Didactics) from the University of Basque Country. Graduate in Primary Education (UPV/EHU) in the English minor. Official Master Degree in Development and Management of Didactic-Methodological Innovation Projects in Educational Institutions (Universidad de Mondragón). Degree in Graphic Design (Universidad Rey Juan Carlos de Madrid and INESEM). Work experience as a teacher in the Faculty of Philosophy and Anthropology (UPV/EHU). She has participated in the teaching research project "Economy and Taxes" of the Provincial Council of Gipuzkoa in the design of digital teaching materials. She has worked as a teacher for 5 years in several teacher training courses, within the "Prest_Gara" programme of the Basque Government. She has experience as a researcher at the Institute for Democratic Governance (Globernance) and works as a researcher for the "Udal Etorkizuna Eraikiz" project of the Provincial Council of Gipuzkoa. Currently, she is an assistant lecturer in the Faculty of Education, Philosophy and Anthropology of Gipuzkoa at the University of the Basque Country (UPV / EHU).

**Etxabe-Urbieta, Jose María** PhD in Psychodidactics programme (UPV/EHU), Graduate in Chemistry (Macromolecules specialty). Professor of the University School in the Faculty of Education, Philosophy and Anthropology of the UPV/EHU. He teaches subjects in the area of Didactics of Experimental Sciences both in the Degree in Primary Education (Natural Sciences in the Primary Education classroom II) and in the Master's Degree in Teacher Training in Secondary Education (Learning and teaching Physics and Chemistry). He has published articles and communications in conferences on dielectric properties of polymeric materials and education, especially in aspects related to the learning process and teaching of Sciences (mental models, cognitive skills, cognitive-linguistic skills, methodological strategies, dialogical and directive learning in the initial training of teachers and teachers). He has recognized 6 five-year teaching periods and has also obtained positive evaluation in 3 teaching periods in the DOCENTIAZ program (2006-2022), receiving an excellent rating (100 points) in the three calls. Finally, he has been deputy director, academic secretary and vice dean not in the Faculty of Education, Philosophy and Anthropology of the UPV/EHU during the years 1996, 2002-2006, 2008-2019.

**Kanso, Houssam** Houssam Kanso is an associate professor holding a PhD in computer science at the University of Pau and the Adour Countries (UPPA), in France, whose work spans the interdisciplinary
domains of Computer Science. With a profound commitment to the research areas of Green IT and Digital Innovations in Education. Currently, the latter area holds the greatest interest for the researcher. He completed his Ph.D. degree at UPPA in 2022 where he contributed to the energy consumption management in smart environments such as smart homes. Throughout his career, he has participated in different educational innovation projects (in particular, European projects and engineering school creation project). He published many papers in well-known international journals and conferences to share his contributions with the scientific community. Driven by a passion for education and computer science, he continues to push the boundaries of his field, seeking to address pressing challenges and finding new areas for exploration.

**Gueye, Mamadou Lamine** Since 2019, Mamadou Lamine Gueye is a Ph.D. student in Computer Science at the Computer Science Laboratory of the University of Pau and Pays de l'Adour (LIUPPA), France. He teaches courses on Cloud Computing, Enterprise Architecture, Object Oriented Programming, Systems Engineering, Business Intelligence, and Machine Learning in the Bachelor and Master programs in Computer Science. He has contributed to national and international research projects in the field of Education. His research interests focus on Cyber-Physical Systems, Digital & Pedagogical innovations, Education 4.0, and Learning Analytics. He is a reviewer in several international journals in the field of educational and digital innovations. His home page is available at https://orcid.org/0000-0001-6145-5197.

## Authors and Affiliations

Lucía Campo<fnref n="1" />* · Nahia Delgado<fnref n="2" />* · José María Etxabe-Urbieta<fnref n="3" />* ·
Houssam Kanso<fnref n="4" />* · Mamadou Lamine Gueye<fnref n="4" />*

<figure><img src="image_10.png" /></figure>

Lucía Campo

Lucia.campo@ehu.eus

Nahia Delgado

nahia.delgado@ehu.eus

José María Etxabe-Urbieta

josemari.etxabeurbieta@ehu.eus

Houssam Kanso

houssam.kanso@univ-pau.fr

Mamadou Lamine Gueye

mamadou.gueye@univ-pau.fr

<footnote n="1">¹ Department of Didactics and School Management, University of the Basque Country, Leioa, Bizkaia, Spain</footnote>

<footnote n="2">² Department of Didactics and School Management, University of the Basque Country, Donostia-San Sebastián, Gipuzkoa, Spain</footnote>

<footnote n="3">³ Didactics of Mathematics, Experimental Sciences, and Social Sciences, University of the Basque Country, Donostia-San Sebastián, Gipuzkoa, Spain</footnote>

<footnote n="4">⁴ Université de Pau Et Des Pays de L'Adour, UFR Des Sciences Et Techniques de La Côte Basque, Anglet, France</footnote>