Next Article in Journal
Development and Application of a Fuzzy-Apriori-Based Algorithmic Model for the Pedagogical Evaluation of Student Background Data and Question Generation
Next Article in Special Issue
Graph-Temporal Contrastive Transformer for Financial Fraud Detection Using Transaction Behavior Modeling
Previous Article in Journal
FDC-YOLO: A Blur-Resilient Lightweight Network for Engine Blade Defect Detection
Previous Article in Special Issue
Exploring the Impact of AI Tools on Cognitive Skills: A Comparative Analysis
error_outline You can access the new MDPI.com website here. Explore and share your feedback with us.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Algorithms
Volume 18
Issue 11
10.3390/a18110726
algorithms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Key Aspects to Promote the Safe Use of GenAI Tools by Undergraduate Education and Architecture Students: Similarities and Differences

by
María-Carmen Ricoy
1,*,
Joseba Delgado-Parada
1,
Sálvora Feliz
2 and
Tiberio Feliz-Murias
3
1
Facultade de Educación e Traballo Social, Universidade de Vigo, 32004 Ourense, Spain
2
Escuela Técnica Superior de Arquitectura de Madrid, Universidad Politécnica de Madrid, 28040 Madrid, Spain
3
Facultad de Educación, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Algorithms 2025, 18(11), 726; https://doi.org/10.3390/a18110726
Submission received: 15 October 2025 / Revised: 14 November 2025 / Accepted: 15 November 2025 / Published: 18 November 2025
(This article belongs to the Special Issue Evolution of Algorithms in the Era of Generative AI)
Browse Figures
Versions Notes

Abstract

Generative Artificial Intelligence (GenAI) is transforming higher education, yet concerns remain about its ethical use. The perceptions of students about GenAI may differ depending on the university degree in which they are enrolled. Thus, field-specific training approaches are essential to ensure an effective GenAI adoption. The objective of this research is to analyze the use of GenAI by undergraduate Education and Architecture students, evaluate the potential and associated risks, and identify proposals for its safe use. A qualitative study was conducted with 165 Education and Architecture students, considering similarities and differences in their perceptions through an open-ended questionnaire. GenAI tools, especially ChatGPT, are mostly used on computers. Architecture students use a wide variety of GenAI tools, while those from Education degrees, who started using GenAI later, focus on text generators. The benefits identified by future educators mainly have an impact on the academic level, while future architects value their personal benefit. However, all participants agree on the negative repercussions of GenAI on their personal development. While some Education students encourage promoting the use of these tools, Architecture students call for training initiatives that should be differentiated according to the field of study.

1. Introduction

Digital resources symbolize innovation and can improve the teaching–learning process. However, the introduction of these technologies in educational contexts must be carried out safely and responsibly. The most recent technological revolution affecting education is associated with Artificial Intelligence (AI). Its implementation has generated both great excitement and increasing concern. It simulates human thought processes to tackle tasks in multiple fields of knowledge. The AI study started in the 20th century [1], and its use has become popular through Generative Artificial Intelligence (GenAI) in recent years. GenAI comprises “machine learning algorithms designed to generate new data examples that mimic existing data sets” [2] (p. 2). These systems have offered the possibility of integrating AI into everyday life and work contexts [3] and generated implications in the social, economic, and educational spheres.
GenAI has been described as a disruptive technology [4,5]. It is a very powerful and impactful technology, and its application in the field of education is being heavily analyzed. At the same time, it generates great fascination and interest among users and researchers. Some studies [6,7] have already reflected the impact of AI on the teaching–learning process. GenAI has the potential to provide personalized learning and to optimize teaching and learning methods [8]. Using algorithms and data, it facilitates genuine content, such as text, images, or sound, to improve the design of interactive learning experiences [9]. One example is the chatbot ChatGPT, which has provided support for different learning environments [10]. However, its use has raised concerns regarding academic honesty and plagiarism [11], but also among some professionals, families (especially those with minors), students, and educational authorities. The United Nations Educational, Scientific and Cultural Organization (UNESCO) [12] has taken a position on this issue, recommending caution in the use of these tools.
GenAI could negatively impact personal and social aspects, promoting misinformation or dependence, as well as reproducing stereotypes [13]. As AI becomes integrated into education, ethical concerns arise, demanding academic attention [14]. In addition, data privacy, algorithmic bias, and educational inequality that GenAI tools can generate must be considered, calling for comprehensive regulatory and training frameworks [15]. Students and teachers must acquire skills and develop argumentative, critical, and ethical judgment for the appropriate use of AI. The educational curriculum must change to consolidate educational innovation and improve the teaching–learning process.
Higher education increasingly relies on technology to improve teaching and learning outcomes [16]. In this context, the use of GenAI is controversial and has sparked debate about its application in academic tasks. Their advocates highlight multiple benefits, such as increased productivity [17] and personalized learning [18]. Other authors have expressed concern, for example, about their negative impact on student learning [19], critical thinking [20], or data security [21]. Higher education institutions face the challenge of integrating these tools into the university curriculum, harnessing their potential, and reducing the risks of misuse. In this regard, students must improve their AI literacy by following the guidelines offered by universities and participating in training activities [22] to ensure its responsible and safe use.
Secure and understandable AI systems could give university students greater confidence in using GenAI [23], thereby increasing their academic satisfaction [24]. The concept of secure and explainable AI (XAI) provides a framework for integrating AI systems into the university context in a responsible manner [25] and promoting sustainable educational practices [26]. The optimal use of AI in the implementation process requires analyzing the security implications perceived by its users [27]. In this sense, the perception of undergraduate students regarding these tools can provide valuable information for their optimization and safe use.
While many studies explore GenAI in education, very few compare student perceptions across divergent academic domains such as Education and Architecture. Various studies have identified greater acceptance of AI among students of STEAM (Science, Technology, Engineering, Arts, and Mathematics) degrees compared to students in the field of Social Sciences. For example, a study [28] concluded that in Technology and Engineering degrees, there was significantly greater use of GenAI than in Pedagogy and Law. Similarly, other authors [29] indicated that Education students had less knowledge about GenAI than students in other fields, such as Computer Science and Telecommunications. These dissimilarities could be conditioned by the training and encouragement within the different degree programs.
Students enrolled in university degree programs in Social Sciences, such as Education, have expressed uncertainty and concern about the use of GenAI in their future professional practice [30]. A study [31], based on research conducted with Early Childhood Education university students (a university degree aimed at training teachers focused on children aged 0–6), recommends addressing the advantages and disadvantages of using AI in education. Thus, future teachers will have a balanced view of these tools. Another study concluded that there is a need to develop AI skills from the earliest stages of life [32]. This implies understanding professionals’ perceptions of these tools and providing them with specific training.
Other university degrees, such as Social Education (a university degree aimed at training socio-educational intervention professionals), have paid less attention to the research on the use of GenAI tools. However, they could be useful to promote comprehensive development and collective well-being [33]. ChatGPT was positively valued by Social Education students, as it allowed them to improve cross-cutting skills and enrich educational experiences [34]. In addition, the development of digital skills among future social educators represents a challenge that must be addressed in the university context [35]. Thus, students in this degree have reported low familiarity with technological resources [36].
STEAM students, particularly in the Architecture university degree, demonstrated knowledge and familiarity with the use of AI tools, as well as the need to integrate them into their training [37]. Along these lines, a study showed how they perceived the usefulness of AI image generation models in the preliminary stages of design [38]. In this area, AI may also have potential in project design or report writing [39]. Therefore, it could be useful to integrate it into Architecture curricula [40]. However, some future architects showed concern about the use and knowledge of GenAI tools, demanding the need for qualitative studies to address this topic [41].
Understanding the decisive factors that influence the adoption of educational technology is crucial for its successful implementation [16], as reported in a study based on the Technology Acceptance Model [19]. In particular, perceptions regarding the use of GenAI by undergraduate Education and Architecture students enable addressing different needs in specific academic fields, as reported in another study [42]. As a starting point, it is necessary to know the degree of acceptance of GenAI by students and to analyze whether the number of users has continued to increase over the years. In addition, the benefits and risks attributed to GenAI by students could provide insights about their satisfaction with this technology [43]. These data would provide universities with contextualized guidelines to introduce these tools starting from an informed and responsible perspective. This study aims to analyze the use of GenAI based on the perceptions of undergraduate Education and Architecture students, as well as the potential and risks they attribute to these tools, identifying their proposals for safe use. Specifically, we aim to answer the following research questions (RQs):
  • Do undergraduate Education and Architecture students use GenAI tools? (RQ 1)
  • In what year did Education and Architecture students start using GenAI tools? (RQ 2)
  • What digital devices and GenAI tools do Education and Architecture students use? (RQ 3)
  • What advantages and disadvantages do Education and Architecture university students attribute to the safe use of GenAI tools? (RQ 4)
  • What measures/guidelines do Education and Architecture students suggest to promote the safe use of GenAI tools? (RQ 5)

2. Materials and Methods

A qualitative study was conducted to answer the research questions. This type of research allows for the systematic analysis of textual data to investigate processes, conditions, and outcomes in specific contexts and is particularly useful in educational research [44]. This approach was chosen to capture students’ nuanced perspectives on GenAI use beyond numerical trends. This study can provide a detailed explanation of university students’ perceptions about GenAI.

2.1. Participants, Instrument, and Data Collection

To determine the selected sample, two different fields of knowledge were considered: Social Sciences (Early Childhood Education and Social Education university degrees) and Engineering and Architecture (Architecture university degree). Analyzing similarities and differences among students’ perceptions of the use of GenAI tools may provide valuable insights in the higher education context. The research was approved by the Ethics Committee of the Doctoral Program in Educational and Behavioral Sciences, Faculty of Education and Social Work, Universidade de Vigo, Spain (CE-DCEC-UVIGO2022-03-29-0901).
This study involved 165 undergraduate students (133 women, 29 men, and 3 non-binary individuals) in Education (Early Childhood Education and Social Education) and Architecture university degrees, aged between 18 and 61 (M = 29.4; SD = 11.4) (Table 1). A convenience sample was recruited through direct contact with students at three Spanish universities in degrees taught by the researchers responsible for the study. The study was first conducted with Social Education students, with a response rate of 100%. To make comparisons between the three grades, the researchers tried to obtain similar samples of Early Childhood Education and Architecture students.
An ad hoc questionnaire with open-ended questions was applied to collect data. It had been previously validated by four experts in AI and qualitative methodology. The instrument first presented the general objective of the research and the informed consent form, and participants were asked to participate voluntarily. In addition, it was stated that responses would be treated anonymously and confidentially and that personal data would be protected. The students were then requested to provide some profile data (gender, age, university degree, and year of study) and asked the following open-ended questions, which were associated with the research questions indicated above:
  • If you use GenAI tools, in which year did you first come into contact with them? (RQ 1 and 2)
  • What devices do you use to access GenAI tools? (RQ 3)
  • What GenAI tools do you use/have you used to perform academic tasks? (RQ 3)
  • What do you consider the main advantages of using GenAI tools? (RQ 4)
  • What do you consider the main threats of using GenAI tools? (RQ 4)
  • What measures or rules should be implemented by the university to ensure the safe use of GenAI in an academic context? (RQ 5)
The questionnaire was transferred to Google Forms and presented through a QR code that university students scanned with their smartphones to access and answer the questions. Data collection took place during September 2025.

2.2. Data Analysis

The information collected through the questionnaires was processed by applying content analysis with MAXQDA software (version 24.11.0). To analyze the information, the main categorization levels (Table 2) were defined starting from the research questions.
The coding process for the subcategories began with a review of the literature on the topic. Subsequently, the researchers overseeing the study reviewed the responses to agree on the codes to be used. These experts proposed the configuration of the coding using inductive and deductive procedures to analyze all the collected information. They guided the classification of the main coding in relation to the objective of the study and the research questions [46]. Considering the qualitative approach, the internal consistency or credibility (reliability) of the results is guaranteed by the coding system determined by the experts mentioned above. In turn, the final coding was reviewed and constructed by consensus, reinforcing this validation with the contrast and agreement reached between pairs of researchers.
In the case of RQ 1 and 2, the data were analyzed according to the year of first contact with GenAI that each participant indicated (2022, 2023, 2024, and 2025), following a deductive process. It was interpreted that those who did not answer the first question (“If you use GenAI tools, in which year did you first come into contact with them?”) did not use these tools.
To analyze the information related to RQ 3, an inductive process was carried out to classify digital devices (computer, smartphone, and tablet). In the case of GenAI tools, a mixed (deductive–inductive) procedure was followed. The starting point was an existing classification [45] (text generation, image generation, video generation, and 3D generation), adding the different tools identified by the students (ChatGPT, Gemini, etc.).
The analysis of the advantages and disadvantages (RQ 4) of using GenAI was carried out using a mixed category (deductive–inductive) generation process, based on the classification used in another study [33] (personal, academic, and job-related), adding the category “environmental” in the case of disadvantages.
In RQ 5, the measures proposed by Education and Architecture students for the safe use of GenAI have been classified following an inductive process, based on the raw information analyzed (use, training, resources, and security).
The content analyses reflect the relative and absolute frequency of the codes obtained based on the participants in the case of RQ 1, 2, and 3. In the case of RQ 4 and 5, the codes obtained were considered. In addition, all analyses are carried out for all responses and according to the responses of undergraduate Education (Early Childhood Education and Social Education) and Architecture students. This will enable the discovery of possible similarities and differences among university degrees.

3. Results

The findings obtained to answer the research questions (RQs) are systematized into two subsections: (i) the use (first contact, devices, tools) of GenAI by undergraduate Education and Architecture students (RQ 1, 2, and 3) and (ii) advantages, disadvantages, and measures proposed to promote the safe use of GenAI in the university context (RQ 4 and 5).

3.1. Use, Devices, and Tools of GenAI According to the University Degree (RQ 1, 2, and 3)

The undergraduate students participating in the study reported widespread use of GenAI (146/165; 88.5%). Exceptionally, 19 students (11.5%) stated that they did not use GenAI tools. One of them indicates that “AI appears everywhere without control. I do not use it because I prefer to research in books or scientific articles” (participant no. 95, woman, 50 years old, Early Childhood Education student). Considering the different university degrees, Social Education showed a high use of GenAI (52/55; 94.4%), followed by Architecture (50/55; 90.9%) and Early Childhood Education (44/55; 80%). In contrast, 11 Early Childhood Education students (20%) indicate that they do not use these tools, compared to 5 in the Architecture degree (9.1%) and 3 in Social Education (5.5%).
Students were first exposed to GenAI tools between 2022 and 2025. Analysis based on degree programs shows that Architecture students started using these tools mainly in 2023, while Education students (Early Childhood Education and Social Education) started using them in 2024 (Figure 1).
In terms of the digital devices used by undergraduate students who employ GenAI tools, computers (132/146; 90.4%) and smartphones (97/146; 66.4%) stand out. The number of participants who use tablets is negligible (18/146; 12.3%). The analysis based on the different degrees shows the same trend, confirming that the computer is the preferred device for both Education (Early Childhood Education and Social Education) and Architecture students (Figure 2).
The GenAI tools used by higher education students are mainly focused on text generation. Among them, ChatGPT is the most popular. Other tools for generating texts (e.g., Gemini, Copilot), images (e.g., Leonardo, DALL-E), videos (e.g., Sora AI, Luma Labs), and 3D objects (e.g., Meshy) were identified. Considering the different degrees, ChatGPT is the main tool in all cases. For example, one student states, “ChatGPT helps me organize some tasks into outlines or give me ideas if I find myself stuck” (participant no. 94, woman, 36 years old, Early Childhood Education student). There is a difference in the type of tools used by Education and Architecture students. Early Childhood Education and Social Education students mainly use text generation tools, while Architecture students reflect a wide variety and quantity of tools, especially in relation to the creation of images, videos, and 3D objects (Table 3).

3.2. Advantages, Disadvantages, and Measures for the Safe Use of GenAI (RQ 4 and 5)

Students identify different benefits that can be derived from using GenAI tools when performing academic tasks. The advantages indicated have a notable impact on aspects that improve their personal lives (119/225; 52.9%), such as the speed of these systems, which allows them to save time and obtain multiple ideas. Benefits for academic performance (101/225; 47.9%) were also identified, such as the possibility of consulting specific questions and the variety of information offered by multiple sources. Exceptionally, some students, mainly those in Early Childhood Education, show the positive influence of GenAI in their professional future (5/225; 2.2%). Early Childhood Education and Social Education students highlight the benefits that have an impact on the academic field, while those in Architecture focus on personal aspects (Figure 3).
The following excerpts illustrate some advantages identified by students from different degree programs:
  • “AI helps me better understand certain content or questions that I didn’t understand during the lessons” (participant no. 64, woman, 19 years old, Early Childhood Education student).
  • “Through AI, I can easily access a large amount of different information” (participant no. 165, woman, 36 years old, Social Education student).
  • “GenAI tools save me a lot of time when I have to search for information” (participant no. 9, woman, 20 years old, Architecture student).
Given the drawbacks that may arise from the use of GenAI in the university context, students highlight aspects that affect personal development (136/227; 59.9%), such as critical thinking, reasoning, and creativity, as well as the dependence and addiction that these tools can generate. They also express concern about academic risks (84/227; 37%), questioning the security of the information obtained through these systems and their errors or gaps. Students understand that these facts harm the quality of learning. Exceptionally, Architecture students reflect the negative impact of GenAI on environmental issues (4/227; 1.8%), as well as its unfortunate repercussions on their professional future, expressed by some Early Childhood Education and Architecture students (3/227; 1.3%). The analysis according to the degree programs shows the same trend, highlighting the risks that hinder the development of personal skills (Figure 4).
By way of illustration of the disadvantages identified by students when using GenAI tools, some excerpts are presented:
-
“One threat when using AI tools is the lack of critical thinking and assimilation of concepts” (participant no. 67, man, 46 years old, Early Childhood Education student).
-
“I consider that it is dangerous not to know the criteria these tools use to search for information” (participant no. 123, man, 59 years old, Social Education student).
-
“One disadvantage is the excessive use of energy resources required by these systems and how this contributes negatively to the climate emergency” (participant no. 33, woman, 20 years old, Architecture student).
To safely integrate GenAI into the university context, students indicate measures to control the use of these tools (76/122; 62.3%), specifically to restrict them and establish specific guidelines. For example, one student points out the following: “In academic tasks, an explicit statement should be included specifying the parts of the work in which AI has been used” (participant no. 43, male, 24 years old, architecture student). Some students highlight the desirability of popularizing the use of these tools. In contrast, others mention the possibility of banning the use of GenAI in universities. Below are examples of statements representing both positions:
-
“GenAI is here to stay, and students cannot be excluded from this technological advance. Instead, we must get involved and commit to using it responsibly” (participant no. 123, man, 59 years old, Social Education student).
-
“AI must be banned because universities educate critical individuals capable of producing ideas and innovations. If GenAI does this for us, the creative process will disappear and we will end up being mere executors of an artificial system” (participant no. 61, woman, 50 years old, Early Childhood Education student).
Other measures proposed by students to facilitate the safe use of GenAI in the academic context refer to the need for training (38/122; 31.1%). A few students (2/122; 1.6%) refer to the need to improve technical equipment, as well as the importance of greater security in digital systems (1/122; 0.8%). The measures proposed in all cases by undergraduate Education and Architecture students are essentially aimed at the proper use of GenAI tools. It should be noted that Early Childhood Education students place special emphasis on promoting the normal integration of AI into the university context. Architecture students strongly demand specialized training in line with the characteristics of their degree studies (Table 4).

4. Discussion

The use of GenAI in the university context is a reality that must be addressed in research [47]. Therefore, it is essential to integrate learning about emerging technologies into the digital literacy framework and promote the development of skills for their effective integration into the classroom [48]. Understanding the use and perceptions of students from different degree programs can provide a starting point for introducing measures to ensure the ethical and safe use of these tools.
Undergraduate Education and Architecture students participating in the research regularly use GenAI to complete academic tasks (RQ 1). This has also been observed in other studies conducted on higher education [49,50]. However, some university students are still reluctant to use GenAI tools. According to the Technology Acceptance Model [19], to promote the acceptance of GenAI, it is necessary to improve the usability of these tools, addressing potential perceived risks and ethical concerns [47]. In the case of Social Education students, there is a very high use of these tools, above that of Architecture students. This situation could demonstrate that GenAI is spreading in some Education degrees. However, in others, such as Early Childhood Education, students are still cautious in this regard. Possible explanations for this situation could include low digital literacy or low perceived self-efficacy in using these tools [51].
Students’ first contact with GenAI tools in the university context (RQ 2) took place between 2022, when AI started to be introduced into higher education [52], and 2025. GenAI has recently begun to be used in the university context, and the number of users of these tools continues to grow. In particular, the year 2024 stands out in general terms and in the case of Education students (Early Childhood Education and Social Education). For Architecture students, the main year of initial contact was 2023. Education students began using GenAI later, which could explain why they are less familiar with these tools [29].
Computers are the main devices (RQ 3) used by undergraduate students to employ GenAI. This situation appears justifiable given that they are a recurring technological resource for completing academic tasks at university. Smartphones are also noteworthy, as they are one of the devices used daily by university students. Thus, they are increasingly being utilized for academic and everyday activities [53]. Furthermore, the low usage of tablets seems to indicate that these devices have declined in popularity in recent years [54].
In the case of GenAI tools (RQ 3), text generators are mainly used, especially by Education students (Early Childhood Education and Social Education students). Among these tools, ChatGPT stands out. Its multiple functionalities have led to the widespread use of GenAI in the university context [55]. However, it is important to use ChatGPT safely and responsibly to harness its potential, considering the challenges in terms of academic integrity and ethical issues [10]. In addition to this tool, Architecture students have also highlighted image, video, and 3D generators, in line with the type of tasks proposed in this degree program [56].
Undergraduate students have identified various benefits (RQ 4) that arise from the use of GenAI. They mainly highlight its influence on a personal level, which is the focus of Education students, and on an academic level, especially in the case of Architecture students. From the students’ perspective, GenAI tools allow them to save time by quickly or instantly searching for information and answering questions, over other aspects such as improving academic results. This can promote their autonomy and strengthen personalized learning [57]. In addition, Early Childhood Education students have considered the possible impact of these tools in the professional sphere, which is an essential aspect to weigh in their professional future [58]. In contrast, students have given little importance to aspects commonly associated with GenAI, such as ease of use [59] or contribution to personalized learning [8].
Regarding the drawbacks of using GenAI (RQ 4), all students focused on the personal level, highlighting the reliability of information and potential errors, above other issues that must be addressed, such as data privacy [21]. This has also been shown in another study [60]. Errors could have very negative consequences for the quality of their learning. To overcome these obstacles, one of the emerging approaches that increases confidence in these tools is Explainable AI (XAI), which promotes the use of techniques that produce transparent explanations and reasons for the decisions made by AI systems [61]. It is also worth noting the importance of the environmental impact of GenAI tools in relation to energy consumption [62], as stated by Architecture students.
To promote the safe use of GenAI in the university context, the guidelines recommended by undergraduate students (RQ 5) focus on limiting and regulating the use of these tools. Some authors [22] have highlighted the need for policies, regulations, and guidelines to help ensure the equitable and fair use of generative AI. Students, teachers, and administrators should be involved in these designs [63]. In the case of Architecture students, there is a demand for more training in the effective use of GenAI tools. In addition, it is also important that university teachers are properly updated [64]. These measures could help to integrate the use of GenAI into the university context as a matter of course, as demanded by Early Childhood Education students.

5. Conclusions

The use of GenAI tools among Education and Architecture students in Spanish higher education has been increasing since 2022. Students mainly use text generation tools, such as ChatGPT. For this purpose, they use common digital devices for academic tasks, such as computers, and others for personal use, such as smartphones. They value the speed of response of these systems, which allows them to save time in performing everyday tasks. They also consider the possibility of submitting immediate queries and obtaining a wide variety of information on the subjects. However, GenAI also poses risks that can affect human capacities for critical thinking, reasoning, and creativity, or the reliability of the information obtained, which could harm the quality of learning. To ensure the safe use of GenAI tools in higher education, students propose limiting and regulating them, as well as receiving specialized training.
Given the specific characteristics of the university degrees studied, we identified different uses and perspectives. Social Education students show the predominant use of GenAI. However, Architecture students state the use of a variety of GenAI tools, while Education students mainly use ChatGPT. The benefits identified by Education students essentially have an impact on the academic level, while Architecture students value the impact of GenAI for personal use and the benefits that can be extended to everyday life. Regarding measures to integrate GenAI into the university context, Education students find it necessary to promote and normalize the use of these tools through control measures, while Architecture students demand training actions.

6. Practical Implications

In view of the key findings, measures to regulate the safe use of AI in universities can consider common and different approaches for each field of study. In all cases (Education and Architecture), measures should be proposed to regulate the use of these tools. For example, it would be useful to require students to indicate when and why they have used GenAI, as well as the prompts introduced. In this regard, some universities have already developed materials on introducing GenAI into the classroom [65,66]. Another common aspect among the degrees is the need to raise awareness among university students about the possible environmental impact of these tools on energy resource expenditure, which implies limiting GenAI and using it only when necessary. Furthermore, emphasis should be placed on the introduction of GenAI in students’ future professions (teachers, social educators, and architects), considering its usefulness for carrying out specific tasks (Table 5).
In the case of Education, curricula should consider the use of a wide range of GenAI tools beyond text generators. For example, tasks can be oriented to generate images or videos using AI and analyze them critically. Thus, specialized training should be encouraged, both in the case of students and teachers. Furthermore, to promote GenAI use, Early Childhood Education students could benefit from informative talks illustrating successful university experiences. These training sessions could also address technical security aspects, as requested by a Social Education student. In the case of Architecture students, the training could focus on the possible implications of using these tools at an academic level. This would allow students to see AI as a tool that can enhance their results if used ethically and safely, considering its risks. These training activities could be implemented at the beginning of the academic year and held every three months to ensure that students and teachers remain informed about advances in GenAI tools. However, its development requires investment from universities, mainly in human resources, such as specialized professionals. In addition, where necessary, technical equipment should be improved, in view of the request of some Architecture students.

7. Limitations and Prospects

This study has some limitations that should be considered. First, the unequal gender representation of participants makes it difficult to compare the data in this regard. A potential self-selection bias may exist due to the type of sample selection, which could affect the extent of the transferability of the findings. Considering that the study focuses on Education and Architecture students, the results are transferable under critical judgment to similar university contexts. This study relied on self-reported qualitative data, which may not fully capture behavioral aspects of GenAI use. In addition, the cross-sectional nature of the study offers an initial understanding of the topic but could limit causal inferences.
Future research could explore the influence of other variables on the results, such as personal variables (gender, age, etc.), or compare the results in other degree programs. This study could be replicated in contexts other than the Spanish setting to examine the transferability of the findings. The application of mixed methods using qualitative and quantitative instruments could facilitate the triangulation of results and conclusions to deepen the understanding of general AI literacy in various fields. Other studies could be conducted from a longitudinal perspective to understand the long-term implications of GenAI in university contexts.

Author Contributions

Conceptualization, M.-C.R., J.D.-P. and S.F.; methodology, M.-C.R. and T.F.-M.; software, J.D.-P.; validation, M.-C.R., J.D.-P., S.F. and T.F.-M.; formal analysis, J.D.-P. and M.-C.R.; investigation, M.-C.R., T.F.-M. and S.F.; resources, M.-C.R. and S.F.; data curation, J.D.-P. and S.F.; writing—original draft preparation, J.D.-P. and S.F.; writing—review and editing, M.-C.R. and T.F.-M.; visualization, T.F.-M.; supervision, M.-C.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Dataset available on request from the authors.

Acknowledgments

The authors express their gratitude to the university students for their participation in the study.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial Intelligence
GenAIGenerative Artificial Intelligence
UNESCOUnited Nations Educational, Scientific and Cultural Organization
XAIExplainable Artificial Intelligence

References

  1. Denning, P.J.; Tedre, M. Computational Thinking; Mit Press: Cambridge, MA, USA, 2019. [Google Scholar]
  2. Chan, C.K.Y.; Hu, W. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. Int. J. Educ. Technol. High. Educ. 2023, 20, 43. [Google Scholar] [CrossRef]
  3. Laru, J.; Celik, I.; Jokela, I.; Mäkitalo, K. The antecedents of pre-service teachers’ AI literacy: Perceptions about own AI driven applications, attitude towards AI and knowledge in machine learning. Eur. J. Teach. Educ. 2025, 48, 964–986. [Google Scholar] [CrossRef]
  4. Samuelson, P. Generative AI meets copyright. Science 2023, 381, 158–161. [Google Scholar] [CrossRef]
  5. Trejo-Quinana, J. Más preguntas que respuestas. La inteligencia Artificial y la educación. Perf. Educ. 2023, 11, 43–55. [Google Scholar] [CrossRef]
  6. Amofa, B.; Kamudyariwa, X.B.; Fernandes, F.A.P.; Osobajo, O.A.; Jeremiah, F.; Oke, A. Navigating the Complexity of Generative Artificial Intelligence in Higher Education: A Systematic Literature Review. Educ. Sci. 2025, 15, 826. [Google Scholar] [CrossRef]
  7. Rama, C. Nueva fase educativa digital con inteligencia artificial. Perf. Educ. 2023, 45, 9–23. [Google Scholar] [CrossRef]
  8. Sun, L.; Zhou, L. Generative artificial intelligence attitude analysis of undergraduate students and their precise improvement strategies: A differential analysis of multifactorial influences. Educ. Inf. Technol. 2025, 30, 10591–10626. [Google Scholar] [CrossRef]
  9. Alier, M.; García Peñalvo, F.J.; Camba, J.D. Generative Artificial Intelligence in Education: From Deceptive to Disruptive. Int. J. Interact. Multimed. Artif. Intell. 2024, 8, 5–14. [Google Scholar] [CrossRef]
  10. Munaye, Y.Y.; Admass, W.; Belayneh, Y.; Molla, A.; Asmare, M. ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions. Algorithms 2025, 18, 352. [Google Scholar] [CrossRef]
  11. Cotton, D.R.; Cotton, P.A.; Shipway, J.R. Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innov. Educ. Teach. Int. 2024, 61, 228–239. [Google Scholar] [CrossRef]
  12. UNESCO. Guidance for Generative AI in Education and Research; Unesco Publishing: Paris, France, 2023. [Google Scholar]
  13. Hendriks, F.; David, Y.B.-B.; Banse, L.; Fick, J.; Greussing, E.; Klein-Avraham, I.; Rakedzon, T.; Taddicken, M.; Baram-Tsabari, A. Generative AI in Science Communication: Fostering Scientists’ Good Working Habits for Ethical and Effective Use. Sci. Commun. 2025; early access. [Google Scholar] [CrossRef]
  14. Chau, T.; Yan, Y.; Liu, H. A systematic review of AI ethics in education: Challenges, policy gaps, and future directions. J. Glob. Inf. Manag. 2025, 33, 1–50. [Google Scholar] [CrossRef]
  15. García-López, I.M.; Trujillo-Liñán, L. Ethical and regulatory challenges of Generative AI in education: A systematic review. Front. Educ. 2025, 10, 1565938. [Google Scholar] [CrossRef] [PubMed]
  16. Feng, J.; Yu, B.; Tan, W.H.; Dai, Z.; Li, Z. Key factors influencing educational technology adoption in higher education: A systematic review. PLOS Digit. Health 2025, 4, e0000764. [Google Scholar] [CrossRef]
  17. Holzmann, P.; Gregori, P.; Schwarz, E.J. Students’ little helper: Investigating continuous-use determinants of generative AI and ethical judgment. Educ. Inf. Technol. 2025; early access. [Google Scholar] [CrossRef]
  18. Leite, H. Artificial intelligence in higher education: Research notes from a longitudinal study. Technol. Forecast. Soc. Change 2025, 215, 124115. [Google Scholar] [CrossRef]
  19. Duong, C.D.; Vu, T.N.; Ngo, T.V.N. Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. Int. J. Manag. Educ. 2023, 21, 100883. [Google Scholar] [CrossRef]
  20. Malik, A.; Khan, M.L.; Hussain, K.; Qadir, J.; Tarhini, A. AI in higher education: Unveiling academicians’ perspectives on teaching, research, and ethics in the age of ChatGPT. Interact. Learn. Environ. 2025, 33, 2390–2406. [Google Scholar] [CrossRef]
  21. Xue, Y.; Chinapah, V.; Zhu, C. A Comparative Analysis of AI Privacy Concerns in Higher Education: News Coverage in China and Western Countries. Educ. Sci. 2025, 15, 650. [Google Scholar] [CrossRef]
  22. Chan, C.K.Y.; Tsi, L.H. Will generative AI replace teachers in higher education? A study of teacher and student perceptions. Stud. Educ. Eval. 2024, 83, 101395. [Google Scholar] [CrossRef]
  23. Gupta, M.; Akiri, C.; Aryal, K.; Parker, E.; Praharaj, L. From chatgpt to threatgpt: Impact of generative ai in cybersecurity and privacy. IEEE Access 2023, 11, 80218–80245. [Google Scholar] [CrossRef]
  24. Baig, M.I.; Yadegaridehkordi, E. Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education. Int. J. Educ. Technol. High. Educ. 2025, 22, 5. [Google Scholar] [CrossRef]
  25. Ghosh, S.; Kamal, M.S.; Chowdhury, L.; Neogi, B.; Dey, N.; Sherratt, R.S. Explainable AI to understand study interest of engineering students. Educ. Inf. Technol. 2024, 29, 4657–4672. [Google Scholar] [CrossRef]
  26. Albahli, S. Advancing sustainable educational practices through AI-driven prediction of academic outcomes. Sustainability 2025, 17, 1087. [Google Scholar] [CrossRef]
  27. Tucak-Roguljić, I.; Babić, I.Đ. Trends and Security Challenges of Artificial Intelligence Use in Higher Education: A Systematic Review. In Proceedings of the MIPRO 48th ICT and Electronics Convention, Opatija, Croatia, 2–6 June 2025. [Google Scholar] [CrossRef]
  28. Stöhr, C.; Ou, A.W.; Malmström, H. Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Comput. Educ. Artif. Intell. 2024, 7, 100259. [Google Scholar] [CrossRef]
  29. Fontanillas, T.R.; Carbonell, M.R.; Catasús, M.G.; Quemada, P.B. Challenges of generative Artificial Intelligence in higher education: Promoting its critical use among students. RIED-Rev. Iberoam. Educ. Distancia 2025, 28, 209–231. [Google Scholar] [CrossRef]
  30. Bae, H.; Hur, J.; Park, J.; Choi, G.W.; Moon, J. Pre-Service Teachers’ Dual Perspectives on Generative AI: Benefits, Challenges, and Integration into Their Teaching and Learning. Online Learn. 2024, 28, 131–156. [Google Scholar] [CrossRef]
  31. Lim, E.M. Metaphor analysis on pre-service early childhood teachers’ conception of AI (Artificial Intelligence) education for young children. Think. Skills Creat. 2024, 51, 101455. [Google Scholar] [CrossRef]
  32. Su, J.; Zhong, Y. Artificial Intelligence (AI) in early childhood education: Curriculum design and future directions. Comput. Educ. Artif. Intell. 2022, 3, 100072. [Google Scholar] [CrossRef]
  33. Martínez-Carrera, S.; Sánchez-Martínez, C.; Fernandes, S.; Ricoy, M.C. The Use of AI by Undergraduate Students of Social Education in Spain and Portugal: A Case Study. Educ. Sci. 2025, 15, 390. [Google Scholar] [CrossRef]
  34. Rivera, P.R.; León, A.M. Competencias transversales e inteligencia artificial en educación superior: Percepciones y aplicaciones [Cross-cutting Competencies and Artificial Intelligence in Higher Education: Perceptions and Applications]. REDU 2024, 22, 31–47. [Google Scholar] [CrossRef]
  35. Almerich, G.; Suárez-Rodríguez, J.; Díaz-García, I.; Orellana, N. Structure of 21st century competences in students in the sphere of education. influential personal factors. Educ. XX1 2019, 23, 45–74. [Google Scholar] [CrossRef]
  36. Cabezas-González, M.; Casillas-Martín, S. Social educators: A study of digital competence from a gender differences perspective. Croat. J. Educ. 2018, 20, 11–42. [Google Scholar] [CrossRef]
  37. Alshahrani, A.; Mostafa, A.M. Enhancing the use of artificial intelligence in architectural education–case study Saudi Arabia. Front. Built Environ. 2025, 11, 1610709. [Google Scholar] [CrossRef]
  38. Dortheimer, J.; Schubert, G.; Dalach, A.; Brenner, L.; Martelaro, N. Think AI-side the Box. Exploring the Usability of Text-to-Image Generators for Architecture Students. In Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe, Graz, Austria, 20–22 September 2023. [Google Scholar] [CrossRef]
  39. Memon, S.A.; Shehata, W.; Rowlinson, S.; Sunindijo, R.Y. Generative Artificial Intelligence in Architecture, Engineering, Construction, and Operations: A Systematic Review. Buildings 2025, 15, 2270. [Google Scholar] [CrossRef]
  40. Başarır, L. Modelling AI in architectural education. Gazi U. J. Sci. 2022, 35, 1260–1278. [Google Scholar] [CrossRef]
  41. Cao, Y.; Abdul-Aziz, A.; Mohd-Arshard, W.N. University students’ perspectives on Artificial Intelligence. IJERI-Int. J. Educ. Res. Innov. 2023, 20, 1–21. Available online: https://www.researchgate.net/publication/376561967_University_students'_perspectives_on_Artificial_Intelligence_A_survey_of_attitudes_and_awareness_among_Interior_Architecture_students (accessed on 12 November 2025).
  42. Daher, W.; Hussein, A. Higher Education Students’ Perceptions of GenAI Tools for Learning. Information 2024, 15, 416. [Google Scholar] [CrossRef]
  43. Dubey, P.; Sahu, K.K. Students’ perceived benefits, adoption intention and satisfaction to technology-enhanced learning: Examining the relationships. J. Res. Innov. Teach. Learn. 2021, 14, 310–328. [Google Scholar] [CrossRef]
  44. Gläser-Zikuda, M.; Hagenauer, G.; Stephan, M. The Potential of Qualitative Content Analysis for Empirical Educational Research. Forum Qual. Soc. Res. 2020, 21, 1–20. [Google Scholar] [CrossRef]
  45. García Peñalvo, F.J.; Llorens-Largo, F.; Vidal, J. The new reality of education in the face of advances in generative artificial intelligence. RIED-Rev. Iberoam. Educ. Distancia 2024, 27, 9–39. [Google Scholar] [CrossRef]
  46. Lim, W.M. What is qualitative research? An overview and guidelines. Australas. Mark. J. 2025, 33, 199–229. [Google Scholar] [CrossRef]
  47. Ursavaş, Ö.F.; Yalçın, Y.; İslamoğlu, H.; Bakır-Yalçın, E.; Cukurova, M. Rethinking the importance of social norms in generative AI adoption: Investigating the acceptance and use of generative AI among higher education students. Int. J. Educ. Technol. High. Educ. 2025, 22, 38. [Google Scholar] [CrossRef]
  48. Levy-Nadav, L.; Shamir-Inbal, T.; Blau, I. Digital Competencies for Effective GenAI Use in Secondary Schools: A Longitudinal Exploration of Teachers’ Perspectives and Classroom Practices. J. Comput. Assist. Learn. 2025, 41, e70123. [Google Scholar] [CrossRef]
  49. Almassaad, A.; Alajlan, H.; Alebaikan, R. Student perceptions of generative artificial intelligence: Investigating utilization, benefits, and challenges in higher education. Systems 2024, 12, 385. [Google Scholar] [CrossRef]
  50. Sousa, A.E.; Cardoso, P. Use of generative AI by higher education students. Electronics 2025, 14, 1258. [Google Scholar] [CrossRef]
  51. Lim, E.M. The effects of pre-service early childhood teachers’ digital literacy and self-efficacy on their perception of AI education for young children. Educ. Inf. Technol. 2023, 28, 12969–12995. [Google Scholar] [CrossRef]
  52. Yeung, R.S.K.; Tian, R.; Chiu, D.K.; Choi, S.P.M. University students’ perceptions on how generative artificial intelligence shape learning and research practices: A case study in Hong Kong. J. Acad. Libr. 2025, 51, 103082. [Google Scholar] [CrossRef]
  53. López-Noguero, F.; Romero-Díaz, T.; Gallardo-López, J.A. Smartphone como herramienta de enseñanza-aprendizaje en educación superior en Nicaragua [Smartphone as a Teaching-Learning Tool in Higher Education in Nicaragua]. RIED-Rev. Iberoam. Educ. Distancia 2023, 26, 307–330. [Google Scholar] [CrossRef]
  54. Namuddu, J.; Watts, P.N. Choice of device to view video lectures: An analysis of two independent cohorts of first-year university students. Res. Learn. Technol. 2020, 28, 1–11. [Google Scholar] [CrossRef]
  55. Baig, M.I.; Yadegaridehkordi, E. ChatGPT in the higher education: A systematic literature review and research challenges. Int. J. Educ. Res. 2024, 127, 102411. [Google Scholar] [CrossRef]
  56. Sanna, A.; Lamberti, F.; Paravati, G.; Demartini, C. Automatic assessment of 3D modeling exams. IEEE Trans. Learn. Technol. 2011, 5, 2–10. [Google Scholar] [CrossRef]
  57. Bhullar, P.S.; Joshi, M.; Chugh, R. ChatGPT in higher education-a synthesis of the literature and a future research agenda. Educ. Inf. Technol. 2024, 29, 21501–21522. [Google Scholar] [CrossRef]
  58. Colón-Aguirre, M.; Bright, K. The inevitability of AI: A study of undergraduate students’ perceptions of AI tools in their future careers. Inf. Res. 2025, 30, 443–449. [Google Scholar] [CrossRef]
  59. Malhotra, K.; Wiesenfeld, B.; Major, V.J.; Grover, H.; Aphinyanaphongs, Y.; Testa, P.; Austrian, J.S. Health system-wide access to generative artificial intelligence: The New York University Langone Health experience. J. Am. Med. Inf. Assoc. 2025, 32, 268–274. [Google Scholar] [CrossRef]
  60. Liu, Y.; Park, J.; McMinn, S. Using generative artificial intelligence/ChatGPT for academic communication: Students’ perspectives. Int. J. Appl. Linguist. 2024, 34, 1437–1461. [Google Scholar] [CrossRef]
  61. Khosravi, H.; Shum, S.B.; Chen, G.; Conati, C.; Tsai, Y.S.; Kay, J.; Knight, S.; Martínez-Maldonado, R.; Sadiq, S.; Gašević, D. Explainable artificial intelligence in education. Comput. Educ. Artif. Intell. 2022, 3, 100074. [Google Scholar] [CrossRef]
  62. Licht, K.d.F. Rethinking the Ethics of GenAI in Higher Education: A Critique of Moral Arguments and Policy Implications. J. Appl. Philos. 2025, 42, 1217–1337. [Google Scholar] [CrossRef]
  63. Magrill, J.; Magrill, B. Preparing Educators and Students at Higher Education Institutions for an AI-Driven World. Teach. Learn. Inq. 2024, 12, 1–9. [Google Scholar] [CrossRef]
  64. Al-Ali, S.; Miles, R. Upskilling teachers to use generative artificial intelligence: The TPTP approach for sustainable teacher support and development. Australas. J. Educ. Technol. 2025, 41, 88–106. [Google Scholar] [CrossRef]
  65. Guía de uso de las Herramientas de Inteligencia Artificial Generativa para el Estudiantado [User Guide for Generative Artificial Intelligence Tools for Students]. Available online: https://www.uned.es/universidad/dam/inicio/institucional/areas-direccion/vicerrectorados/innovacion-educativa/doc/Gu%C3%ADa-genIA-Estudiantes.pdf (accessed on 12 November 2025).
  66. Usos de IA Xerativa na Docencia. Documento Marco na Escola de Enxeñaría de Telecomunicación [Uses of Generative AI in Teaching: Framework Document at the School of Telecommunications Engineering]. Available online: https://teleco.uvigo.es/documentos/normativa/escola/usos-de-ia-xerativa-na-docencia-documento-marco-na-eet/ (accessed on 12 November 2025).
Algorithms 18 00726 g001
Figure 1. Frequency and % of students in each degree program who began using GenAI by year. Notes: EC (Early Childhood Education), SE (Social Education), and Arch. (Architecture). The % were calculated based on the total number of participants (N = 55 in each degree program).
Figure 1. Frequency and % of students in each degree program who began using GenAI by year. Notes: EC (Early Childhood Education), SE (Social Education), and Arch. (Architecture). The % were calculated based on the total number of participants (N = 55 in each degree program).
Algorithms 18 00726 g001
Algorithms 18 00726 g002
Figure 2. Frequency (f) and % of students in each degree program who use each device. Notes: EC (Early Childhood Education), SE (Social Education), and Arch. (Architecture). The percentages were calculated based on the number of participants using GenAI (EC = 44, SE = 52, Arch. = 50).
Figure 2. Frequency (f) and % of students in each degree program who use each device. Notes: EC (Early Childhood Education), SE (Social Education), and Arch. (Architecture). The percentages were calculated based on the number of participants using GenAI (EC = 44, SE = 52, Arch. = 50).
Algorithms 18 00726 g002
Algorithms 18 00726 g003
Figure 3. Frequency and % of undergraduate students who stated advantages when using GenAI. Notes: EC (Early Childhood Education), SE (Social Education), and Arch. (Architecture). The percentages were calculated based on the total number of answers in each group for this main category.
Figure 3. Frequency and % of undergraduate students who stated advantages when using GenAI. Notes: EC (Early Childhood Education), SE (Social Education), and Arch. (Architecture). The percentages were calculated based on the total number of answers in each group for this main category.
Algorithms 18 00726 g003
Algorithms 18 00726 g004
Figure 4. Frequency and % of undergraduate students who stated disadvantages when using GenAI. Notes: EC (Early Childhood Education), SE (Social Education), and Arch. (Architecture). The percentages were calculated based on the total number of answers in each group for this main category.
Figure 4. Frequency and % of undergraduate students who stated disadvantages when using GenAI. Notes: EC (Early Childhood Education), SE (Social Education), and Arch. (Architecture). The percentages were calculated based on the total number of answers in each group for this main category.
Algorithms 18 00726 g004
Table 1. Undergraduate Education and Architecture students who participated in the research.
Table 1. Undergraduate Education and Architecture students who participated in the research.
Early Childhood Education (N = 55)Social Education
(N = 55)		Architecture
(N = 55)
52 women49 women32 women
3 men5 men21 men
1 non-binary2 non-binary
Total = 165
Note: Each group is made up of 55 participants.
Table 2. Main categories of analysis.
Table 2. Main categories of analysis.
1st LevelDefinition
YearThe year the students came into contact with GenAI tools (deductive process).
DevicesDigital media used by higher education students to utilize GenAI tools (inductive process).
ToolsGenAI software used by higher education students (deductive [45] and inductive process).
AdvantagesBenefits attributed by higher education students to the use of GenAI tools for academic tasks (deductive [45] and inductive process).
DisadvantagesRisks and concerns identified by higher education students when using GenAI tools to develop academic tasks (deductive [33] and inductive processes).
MeasuresProposals from higher education students for the safe integration of GenAI tools in the university context (inductive process).
Table 3. Frequency (f) and % of GenAI tools used by undergraduate Education and Architecture students.
Table 3. Frequency (f) and % of GenAI tools used by undergraduate Education and Architecture students.
CategoriesTotal
(N = 146)		Early
Childhood
(N = 44)		Social
Education
(N = 52)		Architecture (N = 50)
1st Level2nd Level3rd Levelf%f%f%f%
ToolsText
generation		ChatGPT13290.43579.55198.14692
Gemini1711.6613.647.7714
Copilot128.224.511.9918
Perplexity96.224.559.624
Deepseek32.1--23.812
Nanobanana21.4----24
Image, video, and 3D
generation		Leonardo74.812.3--612
Midjourney64.1--11.9510
DALL-E64.1--23.848
Canva64.136.8--36
Sora AI32.112.312.312
Adobe Firefly21.4----24
Meshy21.4----24
Note: The percentages were calculated based on the total number of answers in each group.
Table 4. Frequency (f) and % of measures proposed by university students according to the respective degree programs.
Table 4. Frequency (f) and % of measures proposed by university students according to the respective degree programs.
CategoriesTotal
(N = 122)		Early
Childhood
(N = 39) 		Social
Education
(N = 37)		Architecture
(N = 46)
1st Level2nd Level3rd Levelf% f%f%f%
MeasuresUseLimit3730.3923.11335.11532.6
Regulate2722.11128.2821.6817.4
Promote97.4717.925.4--
Prohibit86.537.712.748.7
Training-3831.1923.11232.41737
Resources-21.6----24.3
Security-10.8--12.7--
Note. The percentages were calculated based on the total number of answers in each group.
Table 5. Main proposals to introduce GenAI at the university.
Table 5. Main proposals to introduce GenAI at the university.
Degree ProgramsTemporary
Proposal
Early Childhood
Education		Social
Education 		Architecture
Common
  • Declare GenAI use in academic tasks
Beginning of the academic year.
  • Environmental awareness
  • Professional implications
  • Review and analysis of user guides [65,66]
Specific
  • Training on different types of tools (text, image, video, etc.)
  • Foster academic usefulness/risks
Beginning of the academic year and every 3 months.
  • Promote GenAI use
  • Security information
  • Enhance resources
  • Teaching planning
  • Designing strategies
  • Creative design
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ricoy, M.-C.; Delgado-Parada, J.; Feliz, S.; Feliz-Murias, T. Key Aspects to Promote the Safe Use of GenAI Tools by Undergraduate Education and Architecture Students: Similarities and Differences. Algorithms 2025, 18, 726. https://doi.org/10.3390/a18110726

AMA Style

Ricoy M-C, Delgado-Parada J, Feliz S, Feliz-Murias T. Key Aspects to Promote the Safe Use of GenAI Tools by Undergraduate Education and Architecture Students: Similarities and Differences. Algorithms. 2025; 18(11):726. https://doi.org/10.3390/a18110726

Chicago/Turabian Style

Ricoy, María-Carmen, Joseba Delgado-Parada, Sálvora Feliz, and Tiberio Feliz-Murias. 2025. "Key Aspects to Promote the Safe Use of GenAI Tools by Undergraduate Education and Architecture Students: Similarities and Differences" Algorithms 18, no. 11: 726. https://doi.org/10.3390/a18110726

APA Style

Ricoy, M.-C., Delgado-Parada, J., Feliz, S., & Feliz-Murias, T. (2025). Key Aspects to Promote the Safe Use of GenAI Tools by Undergraduate Education and Architecture Students: Similarities and Differences. Algorithms, 18(11), 726. https://doi.org/10.3390/a18110726

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop