Next Article in Journal
Feasibility Evaluation of Secure Offline Large Language Models with Retrieval-Augmented Generation for CPU-Only Inference
Previous Article in Journal
A Novel q-Type Semi-Dependent Neutrosophic Decision-Making Approach and Its Applications in Supplier Selection
Previous Article in Special Issue
Towards Reshaping Children’s Habits: Vitalia’s AR-Gamified Approach
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Information
Volume 16
Issue 9
10.3390/info16090743
information-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

Evaluation of the Use and Acceptance of an AR Mobile App in High School Students Using the TAM Model

by
Antonio Amores-Valencia
1,
Daniel Burgos
2,3,* and
John W. Branch-Bedoya
4
1
Facultad de Ciencias de la Educación, Universidad Internacional de la Empresa (UNIE), C. de Arapiles, 14, Chamberí, 28015 Madrid, Spain
2
Research Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja, 26006 Logroño, Spain
3
MIU City University Miami, 111 NE 1st Street, 6th Floor, Miami, FL 33132, USA
4
Facultad de Minas, Universidad Nacional de Colombia, Medellín 17003, Colombia
*
Author to whom correspondence should be addressed.
Information 2025, 16(9), 743; https://doi.org/10.3390/info16090743
Submission received: 25 July 2025 / Revised: 19 August 2025 / Accepted: 20 August 2025 / Published: 28 August 2025
(This article belongs to the Collection Augmented Reality Technologies, Systems and Applications)
Browse Figures
Versions Notes

Abstract

Augmented Reality (AR) has emerged as a promising educational tool, offering new opportunities to enhance learning through immersive and interactive experiences. This study aimed to evaluate the degree of acceptance of AR by secondary school students using the Technology Acceptance Model (TAM) as the theoretical framework. A quasi-experimental post-test design was implemented with a sample of 321 students (ages 14–17) who engaged with ComputAR, a mobile AR application developed specifically for a didactic unit on computer systems. Data were collected through a validated TAM questionnaire encompassing five dimensions: “perceived usefulness”, “perceived ease of use”, “perceived enjoyment”, “attitude towards using”, and “behavioural intention to use”. The results indicate a high level of acceptance of AR-based educational tools. Significant differences were found in “perceived ease of use” depending on gender, with male students reporting higher ease, while no gender differences emerged in “perceived usefulness” or “behavioural intention”. Additionally, ICT previous experience was shown to positively affect “perceived enjoyment”, ease of use, and usefulness. In conclusion, these findings confirm the relevance of AR for fostering student motivation and engagement.

1. Introduction

The progressive integration of digital technologies in educational settings has significantly transformed the ways in which knowledge is accessed, skills are developed, and learning is managed [1,2,3]. In particular, mobile applications have emerged as key tools in this process due to their accessibility, portability, and ability to deliver interactive content in real time [4]. This context has led to the rise of mobile learning (m-learning), an approach that aligns with the demands of increasingly flexible, personalized, and technology-enhanced education [5,6].
Among the emerging technologies incorporated into the educational field, Augmented Reality (AR) stands out for its potential to enrich learning experiences by overlaying virtual information onto the physical environment. This type of application enables students to actively interact with 3D content, which may enhance their motivation, conceptual understanding, and academic performance [7,8,9]. The widespread availability of mobile devices, such as smartphones and tablets, has facilitated the integration of AR into classrooms, promoting more dynamic and participatory learning environments [10].
Several studies have explored the impact of mobile technologies in educational contexts, including tools such as social networks, digital books, and QR codes [11,12,13,14]. However, the use of AR still represents an emerging field, particularly concerning its acceptance by students. In this regard, it is essential to consider not only the technical features of AR applications but also user perceptions, such as “perceived usefulness”, ease of use, and prior experience with information and communication technologies (ICTs), as these factors directly influence the willingness to adopt and engage with new technologies [15,16].
Based on this approach, a mobile AR application called ComputAR was developed to support the learning of content related to computer hardware components in the subject of information and communication technologies (ICTs) at the secondary education level. The application allows for the visualization of 3D objects using customized graphic markers, fostering active interaction with educational content.
The aim of this study is to analyze the degree of acceptance of this AR-based technological tool. To this end, the Technology Acceptance Model (TAM) is adopted as the theoretical framework to interpret students’ attitudes toward the use of AR. The TAM is a theory developed by [17] to explain how users come to accept and use new technologies. This model has been widely used to assess the acceptance of new technologies in educational contexts, owing to its ability to predict usage intention based on constructs such as “perceived usefulness” and “perceived ease of use”. In addition, the potential influence of gender and ICT previous experience on AR acceptance was also examined.

2. Related Works

The integration of emerging technologies such as Augmented Reality (AR) in educational settings has been shown to foster positive attitudes among students, enhancing their motivation and interest in learning content [18,19]. AR offers a valuable opportunity to enrich the teaching and learning process by enabling more interactive and immersive experiences [20,21]. The continuous development of AR-based educational applications, along with advances in mobile connectivity such as the deployment of 5G networks, has further contributed to the broader adoption and effectiveness of these tools in academic environments [22,23].
However, the implementation of AR in educational contexts still faces several challenges. Among the most pressing are the limited number of studies focused specifically on its pedagogical application [24] and the insufficient training of teachers in information and communication technologies (ICTs), particularly in AR [25]. Additional barriers include the scarcity of innovative learning experiences, the lack of subject-specific resources and AR learning objects, the absence of solid theoretical frameworks for its integration, and the structural difficulties faced by educators due to limited institutional support [26,27].
Several studies have highlighted the potential impact of AR on teaching and learning processes, especially in terms of increasing student engagement and promoting meaningful learning [28,29,30]. Some research has focused on AR-based games, applications, and illustrated books that use animations and videos superimposed on textual content to present new forms of learning and knowledge representation [31,32,33].
Beyond formal education, AR has also proven valuable in professional training environments by enabling the creation of risk-free simulations for practical instruction. This allows learners to engage in realistic scenarios without physical danger, making AR an effective tool for employee training and workplace safety [34,35]. Moreover, with the rise of e-learning, AR has facilitated the delivery of practical content that was previously limited to in-person instruction, expanding the possibilities of remote education [36].
In terms of the acceptance and use of AR learning objects, studies by [37,38,39] report no significant gender differences, indicating that variables such as “perceived usefulness”, ease of use, and enjoyment are similarly evaluated by male and female students. Nevertheless, other researchers have identified gender-based disparities in the perception of AR technologies [40,41], suggesting that the digital divide, although narrowing, still persists [42]. Fortunately, increasing institutional efforts to provide equal access and training across all student groups have helped reduce the impact of gender on digital competence [43].

3. Materials and Methods

3.1. Sample and Context

This research was carried out with a non-probabilistic convenience sample composed of 321 students enrolled in secondary education at Colegio Cerrado de Calderón, located in Málaga (Spain). In this type of sampling, the researcher is responsible for choosing the sample based on this representativeness and ease of access [44], and it is one of the most representative in educational research [45]. All participants were between 14 and 17 years old and were studying the subject of information and communication technologies (ICTs), which included a specific didactic unit on computer systems, hardware architecture, and primary hardware components.
The gender distribution of the sample was 33.33% female and 66.67% male. Only a small portion of students (2.80%) had previously repeated an academic year.
Regarding the academic level, 24.92% of the participants were in the third year of compulsory secondary education, 37.07% in the fourth year, 23.68% in the first year of the baccalaureate, and 14.33% in the second year of the baccalaureate.
It was expected that some students would already possess prior exposure to emerging digital technologies. Specifically, 23.68% of the sample reported previous experience using ICT tools, while the remaining 76.32% indicated no prior experience. Nevertheless, none of the participants had engaged with mobile devices in classroom settings, nor had they interacted with educational applications involving Augmented Reality (AR).
In terms of age, 29.60% of the students were 14 years old, 33.96% were 15, 21.49% were 16, and 14.95% were 17. Given that all participants were minors, the study was reviewed and approved by the corresponding research ethics committee, which granted authorization for data collection in accordance with ethical guidelines.
To protect participant anonymity, data were collected using the Microsoft Forms platform, and Internet Protocol (IP) addresses were removed during the export process. The survey was administered in the school’s computer lab to minimize external interference, with each student completing the questionnaire on an individual computer. Access was restricted to a single response per student via their institutional email accounts, while email identification remained hidden by default to preserve confidentiality.

3.2. Method and Instruments

This study adopted a quantitative methodological approach, in which all aspects of the research design were established prior to data collection, as the independent variable could not be manipulated. Quantitative research offers an empirical–analytical framework that allows for the measurement of central tendency and dispersion, typically through mean scores and standard deviation [46].
To examine students’ acceptance of Augmented Reality (AR), we conducted a quasi-experimental post-test design, which is particularly suitable for assessing the influence of educational technologies in real classroom contexts at the secondary education level. For this purpose, we employed an adapted version of the Technology Acceptance Model (TAM) specifically tailored to the use of AR in education [43].
The instrument consists of five key dimensions: “perceived usefulness” (PU), “perceived ease of use” (PEU), “perceived enjoyment” (PE), “attitude towards using” (ATT), and “behavioural intention to use” (IU) [47,48]. In total, the questionnaire includes fifteen items: four items measure “perceived usefulness”, two items measure “behavioural intention to use”, while each of the remaining dimensions is assessed through three items. Table 1 shows the designated dimensions, identifier, and questions which use a seven-point Likert scale, with response options ranging from one (“extremely unlikely”/”strongly disagree”) to seven (“extremely likely”/”strongly agree”).
To evaluate the internal consistency of the measurement instrument, we calculated Cronbach’s alpha coefficient, a widely accepted statistical indicator for assessing the reliability of multi-item scales. The overall reliability score obtained was Cronbach’s alpha 0.937, which indicates a very high level of internal consistency across the instrument.
This result suggests that the questionnaire is a highly reliable tool for measuring students’ perceptions and attitudes toward the use of Augmented Reality in educational settings. Furthermore, when analyzing the reliability of each individual dimension separately, all yielded alpha values exceeded 0.70, as shown in Table 2. This confirms that each construct within the model demonstrates an acceptable to excellent level of reliability, thereby supporting the robustness of the scale across all subdimensions.
An item–total correlation analysis was also conducted to determine whether the reliability of the instrument could be improved by removing any individual item. The results confirmed that the overall internal consistency of the questionnaire would not increase significantly if any item were eliminated.
Additionally, separate item–total correlation analyses were performed for each of the instrument’s dimensions. These analyses aimed to assess whether the reliability index of any specific dimension could benefit from the removal of an item. However, the results did not indicate any substantial improvement in reliability at the dimensional level. Therefore, all items were retained in the final version of the instrument.
This decision reinforces the validity of the complete questionnaire, as neither the overall reliability coefficient nor those corresponding to the individual dimensions would be meaningfully improved by the exclusion of any item.
In addition to these core dimensions, two predictor variables were incorporated into the analysis: the student’s gender and previous experience with ICT. To explore the relationship between these variables and the TAM dimensions, a correlational analysis was carried out, as illustrated in Figure 1.
The research hypotheses developed from this design are as follows:
•
H1-H2-H3. “ICT previous experience” can positively and significantly affect the “perceived enjoyment”, “perceived ease of use” and “perceived usefulness” of using AR learning objects.
•
H4-H5-H6. “Gender” can positively and significantly affect the “perceived enjoyment”, “perceived ease of use”, and “perceived usefulness” of using AR learning objects.
•
H7-H8-H10. “Perceived ease of use” can positively and significantly affect the “perceived enjoyment,” “perceived usefulness,” and “attitudes towards using” of AR learning objects.
•
H9-H13-H14. “Perceived usefulness” of using AR learning objects can positively and significantly affect the “perceived enjoyment”, “attitudes towards using”, and “behavioural intention to use” AR learning objects.
•
H11-H12. “Perception enjoyment” can positively and significantly affect the “attitudes towards using” and “behavioural intention to use” AR learning objects.
•
H15. “Attitude towards using” can positively and significantly affect the “behavioural intention to use” of AR learning objects.
Data collection took place during the final scheduled session of the teaching unit.

3.3. Materials and Procedure

Regarding the materials employed, a custom mobile application was developed using a combination of software platforms: Unity (version 2019.4.1f1), Vuforia Engine (version 2024.10.1), Android Studio (version 2021.2.1), and Visual Studio 2019. The 3D models integrated into the application were sourced from open-access digital repositories, thereby aligning the project with the principles of Open Educational Resources (OER). Additionally, customized graphic markers were designed to trigger AR content during use (see Figure 2). The resulting application, titled ComputAR, was thoroughly tested on over 15 devices from various manufacturers to ensure cross-platform functionality and operational stability.
ComputAR (version 1) was developed to support teaching practices and enhance student learning outcomes through the integration of Augmented Reality in the classroom. The tool is designed to complement teacher-led instruction and student activities without interrupting classroom flow. This seamless integration facilitates active engagement, promotes meaningful learning experiences, and fosters a dynamic, participatory environment. Through its AR capabilities, the application enables the visualization of otherwise inaccessible or obsolete technological components, enriching the learning process with more engaging and informative interactions. This characteristic is crucial for facilitating meaningful learning, stimulating student engagement, and encouraging peer interaction. In this sense, incorporating AR-based tools such as ComputAR allows educators to deliver lessons that are more engaging, motivating, clear, and dynamic, and to present certain content that would otherwise be difficult or impossible to visualize due to its obsolescence or lack of availability [49].
Upon launching the application, users are presented with an initial screen that includes virtual buttons organized by thematic categories relevant to the educational objectives. Figure 3 illustrates the main menu interface.
The first section, Computer Equipment, provides access to visualizations of historically significant computing machines, each displayed as interactive 3D models (Figure 4).
The second section, Computer Architecture, features an annotated diagram of a desktop computer that highlights both internal and external components. The final section, Hardware, presents a catalogue of individual computer components, each of which can be explored in detail (Figure 5). Selecting any of these items reveals a concise explanatory text and offers the option to view the object in Augmented Reality.
The full ComputAR application package, including all associated graphic markers, is provided in the Supplementary Materials.
The research was carried out in three well-defined stages. The first phase focused on the theoretical contextualization of the study through a comprehensive review of the literature in academic databases. This stage allowed for the identification of educational needs and challenges relevant to the context under analysis. It also served as the foundation for defining the dimensions and indicators to be considered in the development of the research instrument.
The second phase involved the construction of the questionnaire used as the primary data collection tool. After designing the instrument, data were gathered from the selected sample. This process concluded with the organization and statistical analysis of the collected responses to identify patterns and correlations.
The third and final phase centred on the interpretation of the results. The findings were examined considering the research objectives, allowing for the formulation of conclusions that highlight the most significant contributions of the study and their implications for educational practice and the integration of Augmented Reality in the learning process.
Table 3 shows the instructional design for the students. The duration of each session was 60 min.

4. Results

This section presents the analysis of the data collected through the questionnaire administered in the study. The data processing was carried out using Microsoft Excel (2019) and the Statistical Package for the Social Sciences (SPSS), version 26.
Regarding the analysis of the Technology Acceptance Model (TAM), Table 4 presents the mean values and standard deviations obtained both for the overall instrument and for each of its specific dimensions. In all cases, the mean scores significantly exceeded the central point of the scale (3.5), which reflects a strong level of acceptance of Augmented Reality (AR) technology among students following their involvement in the AR-based learning experience. Particularly noteworthy are the high scores recorded in the dimensions “attitude towards using” (6.13) and “behavioural intention to use” (6.11).
To test the hypotheses derived from the TAM framework established for this study, two statistical approaches were applied, depending on the nature of the variables. For binary variables (e.g., yes/no; male/female), Student’s t-test was used to assess hypotheses H1, H2, H3, H4, H5, and H6. For continuous variables, Pearson’s correlation coefficient was employed. In the case of t-tests, Levene’s test was first conducted to assess the assumption of equal variances at a significance level of p ≤ 0.05, which determined the appropriate test parameters to use.
The first set of analyses focused on the variable “ICT previous experience.” Table 5 displays the corresponding mean values and standard deviations across all dimensions, revealing consistently higher scores among participants who reported prior experience with information and communication technologies.
Subsequently, Student’s t-test was applied to examine the influence of prior ICT experience, as summarized in Table 6.
Levene’s test results indicated in all three dimensions that no significant differences in variance were found between the groups with and without ICT previous experience. This indicates that equal variances can be assumed for the t-test. In addition, in all three dimensions which tested “perceived enjoyment,” “perceived ease of use,” and “perceived usefulness”, the obtained t-values supported the rejection of the null hypotheses in favour of the alternative hypotheses (H1-H2-H3), with a significance threshold of p ≤ 0.05. Therefore, it can be concluded that students with ICT previous experience perceived greater enjoyment, ease of use, and usefulness when engaging with the AR-based learning tools.
In addition to statistical significance values, effect sizes were calculated using Cohen’s d to assess the magnitude of the observed differences. The results revealed a moderate effect for the dimension “perceived ease of use” (d = 0.55), a small-to-moderate effect for “perceived enjoyment” (d = 0.40), and a small effect for “perceived usefulness” (d = 0.33). These findings suggest that, although the differences between groups are statistically significant, their practical impact varies across dimensions, with the most substantial effect observed in students’ perception of ease of use. This highlights the relevance of ICT previous experience in shaping user comfort and interaction with AR-based educational tools.
In terms of gender differences, descriptive statistics including mean scores and standard deviations are presented in Table 7.
The observed values were closely aligned between groups, indicating minimal variation. To determine whether these differences were statistically significant, Student’s t-test was conducted, with the results displayed in Table 8.
In no case is the assumption of equality of variances violated (Levene’s p > 0.05), so the results with equal variances are valid. “Perceived ease of use” shows a significant difference between genders, with men reporting greater perceived ease (p < 0.001). However, “behavioural intention to use” and “perceived usefulness” do not show significant differences between men and women (p > 0.05). Gender does not significantly influence “behavioural intention to use” or the “perceived usefulness” of technology, but it does appear to influence “perceived ease of use”, with men reporting greater comfort with technology.
In addition to statistical significance testing, effect sizes were calculated using Cohen’s d to assess the practical relevance of gender-based differences. The results revealed trivial effect sizes for “perceived enjoyment” (d = 0.03) and “perceived usefulness” (d = 0.07), and a very small effect for “perceived ease of use” (d = 0.11). These findings suggest that, although a statistically significant difference was observed in ease of use, the actual magnitude of the gender effect is minimal, indicating limited practical implications in educational contexts.
Below are the results associated with the remaining TAM hypotheses, analyzed using Pearson’s correlation coefficient as previously described. First, Table 9 shows the correlational analysis of the dimension “perceived ease of use” with “perceived enjoyment”, “perceived usefulness”, and “attitude towards using”.
Based on the results obtained, we can affirm with a significance level of 0.05 that “perceived ease of use” positively and significantly influences “perceived enjoyment”, “perceived usefulness”, and “attitude towards using”.
Second, Table 10 shows the correlational analysis of the dimension “perceived usefulness” with “perceived enjoyment”, “attitude towards using”, and “behavioural intention to use”.
The results obtained from the application of Pearson’s correlation to the dimensions referenced in the hypotheses indicate that, with an alpha risk of 0.05, “perceived usefulness” has a positive and statistically significant influence on “perceived enjoyment”, “attitude towards using”, and “behavioural intention to use”.
Third, Table 11 shows the correlational analysis of the dimension “perceived enjoyment” with “attitude towards using” and “behavioural intention to use”.
The findings allow for the rejection of the null hypotheses that posited no relationship between “perceived enjoyment” and both attitude and “behavioural intention to use”. With an alpha risk of 0.05, it can be confirmed that “perceived enjoyment” exerts a positive and statistically significant influence on “attitude towards using” and “behavioural intention to use”.
Finally, Table 12 shows the correlational analysis of the dimension “attitude towards using” with “behavioural intention to use”.
Finally, the correlation between “attitude towards using” and “behavioural intention to use” was examined in response to H15, which proposed that “attitude towards using” Augmented Reality learning objects may positively and significantly influence the behavioural intention to use them. The analysis confirms that, with an alpha risk of 0.05, “attitude towards using” exerts a positive and statistically significant effect on “behavioural intention to use”.
In conclusion, the correlation coefficients revealed the following significant positive relationships:
•
“Perceived enjoyment” following the AR object creation experience was positively related to “perceived ease of use”, “perceived usefulness”, “attitude towards using”, and “behavioural intention to use” the technology.
•
“Perceived ease of use” was positively associated with both “perceived usefulness” and “attitude towards using”.
•
“Perceived usefulness” was positively correlated with “attitude towards using” and “behavioural intention to use”.
•
“Attitude towards using” had a significant and positive effect on “behavioural intention to use” the technology.
It is also worth noting that the relationships between variables were generally strong and positive. This suggests that higher scores in one construct tended to correspond with higher scores in the related constructs, reinforcing the internal consistency of the TAM framework within this study’s context.

5. Discussion

This section presents the analysis of the results aimed at assessing and validating the hypotheses formulated, as well as the objectives established in the study.

5.1. To Assess the Extent to Which Students Accept Augmented Reality as a Technological Tool for Developing Educational Resources

To address the objective of assessing students’ acceptance of Augmented Reality (AR) as a technology for creating educational resources, this study employed an adapted version of the Technology Acceptance Model (TAM) [17]. The instrument used comprised 15 items distributed across five dimensions. Beyond analyzing responses at the item level, we explored interrelations among the dimensions, providing a more comprehensive understanding of acceptance levels. Additionally, the model enabled us to identify predictive variables, specifically students’ gender and ICT previous experience, and to assess their influence on acceptance.
Based on the relationships between the TAM dimensions and the study variables, a total of 15 research hypotheses were formulated. While the associations between gender, ICT previous experience, and AR acceptance are examined later, the discussion here focuses on the remaining hypotheses.
Hypotheses 7, 8, and 10 examined whether “perceived ease of use” could significantly and positively influence “perceived enjoyment”, “perceived usefulness”, and “attitudes towards using” AR learning objects. The data support these hypotheses, suggesting that greater ease of use is associated with higher enjoyment, “perceived usefulness”, and more favourable “attitudes towards using”. This finding highlights the importance of effective instructional design in enhancing usability and, by extension, impacting other key variables [50].
Hypotheses 11 and 12 explored the relationship between “perceived enjoyment” and both usage attitudes and “behavioural intention to use”. Results confirm that enjoyment contributes positively to both attitude and “behavioural intention to use” AR, implying that students are more likely to adopt this technology in future learning contexts.
In the case of Hypotheses 9, 13, and 14, the study proposed that “perceived usefulness” would exert a positive and significant effect on “perceived enjoyment”, “attitudes towards using”, and “behavioural intention to use” AR learning objects. The findings confirm these assumptions, demonstrating that higher “perceived usefulness” strengthens these dimensions. These outcomes are consistent with previous research [51,52], although they contrast with findings reported by [53], who found no significant influence of “perceived usefulness” on related variables.
Regarding Hypothesis 15, which posited a positive and significant relationship between “attitude towards using” and behavioural intention to use AR learning objects, the data again support this claim. The findings suggest that as students’ attitudes toward the technology improve, their behavioural intention to use it increases accordingly.
In line with these results, several authors have emphasized that learners perceive AR as having a beneficial impact on their learning processes and academic performance [54,55,56]. Specific aspects noted include AR’s engaging and enjoyable nature when interacting with learning objects, echoing previous studies that identify enjoyment as a key factor in technology motivation [57,58].
Moreover, the requirement of mobile devices, a familiar and frequently used tool among students, appears to enhance motivation and facilitate acceptance of AR, as noted by [59].
Finally, ease of use was strongly reinforced following the training activity. Participants reported that the application used, ComputAR, was intuitive and accessible. This perceived usability can be attributed, in part, to the quality of the support materials provided, such as tutorials and instructor explanations before the practical phase, as highlighted in prior works [60,61].

5.2. To Determine Whether Gender and ICT Previous Experience Influence the Level of Acceptance of Augmented Reality Technology

In this study, gender and ICT previous experience were considered as predictive variables within the TAM framework. Of the fifteen hypotheses formulated, six specifically addressed the potential influence of these variables on key dimensions such as “perceived enjoyment”, “perceived usefulness”, and “perceived ease of use”.
Regarding ICT previous experience, the findings indicate that students with such experience reported significantly higher levels of perceived enjoyment, “perceived usefulness”, and “perceived ease of use” when interacting with Augmented Reality (AR) learning tools. Levene’s test confirmed equal variances in all three dimensions, and the t-test results led to rejection of the null hypotheses in favour of the alternatives (H1–H3), with significance levels below 0.05. These results suggest that ICT previous experience positively influences how students engage with and perceive AR-based educational technologies.
On the other hand, gender showed a significant effect only on “perceived ease of use”, with male students reporting greater comfort and ease with the AR technology (p < 0.001). No significant gender differences were found for “perceived usefulness” or “behavioural intention to use” (p > 0.05). Thus, while gender does not significantly influence the “perceived usefulness” or “behavioural intention to use” AR, it appears to impact “perceived ease of use”, a finding consistent with previous studies [62,63].
Earlier research by [37,64] reported significant gender differences in technology acceptance and use, but these differences seem to have diminished as ICTs have become more integrated into everyday life. This aligns with the view that such disparities are decreasing over time due to broader technological familiarity.
Moreover, the lack of significant differences related to gender in “perceived usefulness” and behavioural intention concurs with [65] and colleagues, who reported similar findings across various technologies, not limited to Augmented Reality.
In conclusion, the results highlight that ICT previous experience plays a meaningful role in enhancing “perceived enjoyment”, usefulness, and ease of use of AR learning tools, whereas gender differences appear mainly in “perceived ease of use”. This insight emphasizes the importance of considering user backgrounds when implementing innovative educational technologies and suggests that training or support could further mitigate ease-of-use concerns among different demographic groups.

6. Conclusions

Based on the data obtained in this study and the analysis conducted, several relevant conclusions can be drawn.
Firstly, the results derived from the application of the Technology Acceptance Model (TAM) confirm that Augmented Reality (AR), when used as a tool for creating educational resources, is highly accepted by students. The participants reported positive perceptions in terms of enjoyment, usefulness, and ease of use, all of which contribute to a favourable attitude toward the adoption of this technology in academic contexts.
Regarding gender differences, the findings indicate that male students reported significantly higher levels of “perceived ease of use” compared to female students. However, no statistically significant differences were found between genders in terms of “perceived usefulness” or “behavioural intention to use” the technology. These results align partially with previous studies suggesting a diminishing gap in gender-related perceptions of technology [28,37,43], though some divergence remains, specifically concerning “perceived ease of use”.
ICT previous experience significantly influenced all three dimensions: “perceived enjoyment”, “perceived ease of use”, and “perceived usefulness”. Students with prior exposure to ICT tools reported higher scores in these areas, indicating that familiarity with digital environments enhances both emotional and functional perceptions of AR-based educational technologies [4,8,19,22].
Overall, the TAM has once again proven to be a reliable and effective model for predicting technology acceptance, consistent with the prior literature [30,32,47,48,60].
In conclusion, Augmented Reality is a highly accepted technology among students for the creation of learning objects. The applications used in this study were perceived as easy to use, engaging, and motivating, which contributes to greater learning satisfaction. Providing adequate training on how to use AR tools is crucial, as “perceived ease of use” significantly influences students’ willingness to adopt them.
Beyond theoretical validation, these findings also carry practical implications for different stakeholders in education. For teachers, the positive impact of AR on students’ motivation and ease of use highlights its potential as an instructional strategy to foster active learning and engagement. For instructional designers, the results emphasize the importance of developing AR-based activities that are intuitive and enjoyable, thereby lowering barriers to adoption and maximizing pedagogical value. Finally, for educational policymakers, the evidence suggests the need to support teacher training and provide adequate technological resources to ensure the sustainable integration of AR into educational settings.
With regard to future research, several lines are proposed to deepen the understanding and implications of AR in education. These include replicating this study in different regions, academic disciplines, and at various educational levels to test its generalizability. The development of new AR authoring tools may also support broader implementation in educational contexts. Additionally, future studies could explore other predictive variables and factors not considered in this research, such as academic performance or initial motivation, allowing for a more comprehensive understanding of technology acceptance. For this, it is necessary to incorporate mixed methods (quantitative and qualitative) in future studies to better understand the reasons behind technology acceptance. Further investigation is also needed into the specific design features of AR objects that contribute to learning effectiveness. Finally, similar research could be conducted in virtual or blended learning environments, and future studies might consider incorporating performance-based assessments to evaluate the direct impact of AR on academic achievement.

Supplementary Materials

The following supporting information can be downloaded at: https://bit.ly/3Z7FIqU (accessed on 19 August 2025).

Author Contributions

Conceptualization, A.A.-V., D.B. and J.W.B.-B.; methodology, A.A.-V., D.B. and J.W.B.-B.; software, A.A.-V.; validation, A.A.-V., D.B. and J.W.B.-B.; formal analysis, A.A.-V.; investigation, A.A.-V.; resources, A.A.-V.; data curation, A.A.-V.; writing—original draft preparation, A.A.-V.; writing—review and editing, A.A.-V., D.B. and J.W.B.-B.; visualization, A.A.-V., D.B. and J.W.B.-B.; supervision, A.A.-V., D.B. and J.W.B.-B. All authors have read and agreed to the published version of the manuscript.

Funding

This research has been partially funded by the Research Institute for Innovation and Technology in Education (UNIR iTED) at the Universidad Internacional de La Rioja (UNIR) (ref: 018549).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of Universidad Internacional de La Rioja (UNIR) (protocol code PI:065/2022 and date 29 August 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets generated for this study can be found in the manuscript and the Supplementary Materials and are free to use.

Acknowledgments

The authors would like to thank the Research Institute for Innovation and Technology in Education (UNIR iTED), the Universidad Internacional de La Rioja, Logroño, Spain, and the Facultad de Minas at the Universidad Nacional de Colombia (UNAL), Medellín, Colombia.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Colás-Bravo, M.P.; De-Pablos-Pons, J.; Ballesta-Pagán, J. Incidencia de las TIC en la enseñanza en el sistema educativo español: Una revisión de la investigación. Rev. Educ. Distancia 2018, 56, 1–23. [Google Scholar] [CrossRef]
  2. Area-Moreira, M.; Cepeda-Romero, O.; Feliciano-García, L. Perspectivas de los alumnos de Educación Primaria y Secundaria sobre el uso escolar de las TIC. Rev. Educ. Siglo XXI 2018, 36, 229–253. [Google Scholar] [CrossRef]
  3. Cabero-Almenara, J. Reflexiones educativas sobre las tecnologías de la información y la comunicación (TIC). Rev. Tecnol. Cienc. Educ. 2015, 1, 19–27. [Google Scholar] [CrossRef]
  4. León-Garrido, A.; Gutiérrez-Castillo, J.J.; Barroso-Osuna, J.M.; Cabero-Almenara, J. Evaluation of the use and acceptance of mobile apps in higher education using the TAM model. RIED 2025, 28, 107–126. [Google Scholar] [CrossRef]
  5. Mojarro-Aliaño, A. Mobile Learning en la Educación Superior: Una Alternativa Educativa en Entornos Interactivos de Aprendizaje. Doctoral’s Thesis, Huelva, Spain, 2019. Available online: https://acortar.link/pesJVk (accessed on 19 July 2025).
  6. Aguilar-Acevedo, F.; Flores-Cruz, J.A.; Hernández-Aguilar, C.A.; Pacheco-Bautista, D. Diseño e implementación de un simulador basado en realidad aumentada móvil para la enseñanza de la física en la educación superior. Rev. Elec. Tecnol. Educ. 2022, 80, 66–83. [Google Scholar] [CrossRef]
  7. León-Alvarado, M.E.; Alcivar-Gallegos, E. Efectos de la aplicación de proyectos de objetos virtuales de aprendizaje (ovas) en la motivación y el rendimiento académico de los estudiantes. Rev. Cient. Multidiscip. 2023, 7, 5954–5971. [Google Scholar] [CrossRef]
  8. Amores-Valencia, A.; Burgos, D.; Branch-Bedoya, J. Influence of motivation and academic performance in the use of Augmented Reality in education. A systematic review. Front. Psychol. 2022, 13, 1011409. [Google Scholar] [CrossRef] [PubMed]
  9. Kirikkaya, E.B.; Başgül, M.S. The effect of the use of augmented reality applications on the academic success and motivation of 7th grade students. J. Balt. Sci. Educ. 2019, 18, 362–378. [Google Scholar] [CrossRef]
  10. Cabero-Almenara, J.; Barroso-Osuna, J. The educational possibilities of augmented reality. J. New Approaches Educ. Res. 2016, 5, 44–50. [Google Scholar] [CrossRef]
  11. Ripoll-Gómez, S.; Mayoral-García, O.; Azkárraga, J.M. Proyecto Quick Natura. Tecnologías móviles aplicadas a rutas botánicas urbanas. Model. Sci. Educ. Learn. 2017, 10, 185–192. [Google Scholar] [CrossRef]
  12. Tuzel, S.; Hobbs, R. El uso de las redes sociales y la cultura popular para una mejor comprensión intercultural. Comunicar 2017, 51, 63–72. [Google Scholar] [CrossRef]
  13. Moreno-Martínez, N.M.; Onieva-López, J.L. Herramientas y propuestas de innovación basadas en la tecnología de realidad aumentada aplicadas a la literatura infantil y juvenil. Tejuelo 2017, 25, 217–244. [Google Scholar] [CrossRef]
  14. Sanz, J.J. Percepciones y valoraciones de la utilización de los libros digitales en Educación Superior. Rev. Cient. Iberoam. Tecnol. Educ. 2017, 6, 39–50. Available online: https://acortar.link/FMSDl6 (accessed on 23 August 2025).
  15. Baabdullah, A.M.; Alsulaimani, A.A.; Allamnakhrah, A.; Alalwan, A.A.; Dwivedi, Y.K.; Rana, N.P. Usage of augmented reality (AR) and development of e-learning outcomes: An empirical evaluation of students’ e-learning experience. Comput. Educ. 2022, 177, 104383. [Google Scholar] [CrossRef]
  16. Cabero-Almenara, J.; Barroso-Osuna, J.; Llorente-Cejudo, C.; Fernández-Martínez, M. Educational uses of augmented reality (AR): Experiences in educational science. Sustainability 2019, 11, 4990. [Google Scholar] [CrossRef]
  17. David, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  18. Fonseca-Escudero, D.; Redondo-Domínguez, E.; Valls, F. Motivación y mejora académica utilizando realidad aumentada para el estudio de modelos tridimensionales arquitectónicos. Educ. Knowl. Soc. 2016, 17, 45–64. [Google Scholar] [CrossRef]
  19. Schaffernak, H.; Moesl, B.; Vorraber, W.; Koglbauer, I.V. Potential Augmented Reality Application Areas for Pilot Education: An Exploratory Study. Educ. Sci. 2020, 10, 86. [Google Scholar] [CrossRef]
  20. Seifert, T.; Hervás-Gómez, C.; Toledo-Morales, P. Diseño y validación del cuestionario sobre percepciones y actitudes hacia el aprendizaje por dispositivos móviles. Pixel-Bit 2019, 54, 45–64. [Google Scholar] [CrossRef]
  21. Huang, T.L.; Liao, S.L. Creating e-shopping multisensory flow experience through augmented-reality interactive technology. Internet Res. 2017, 27, 449–475. [Google Scholar] [CrossRef]
  22. Buchner, J.; Buntins, K.; Kerres, M. The impact of augmented reality on cognitive load and performance: A systematic review. J. Comput. Assist. Learn. 2022, 38, 285–303. [Google Scholar] [CrossRef]
  23. Juan, M.C.; Charco, J.L.; García-García, I.; Mollá, R. An augmented reality app to learn to interpret the nutritional information on labels of real packaged foods. Front. Comput. Sci. 2019, 1, 1–16. [Google Scholar] [CrossRef]
  24. Lagunes-Domínguez, A.; Torres-Gastelú, C.; Angulo-Armenta, J.; Martínez-Olea, M. Prospectiva hacia el Aprendizaje Móvil en Estudiantes Universitarios. Form. Univ. 2017, 10, 101–108. [Google Scholar] [CrossRef]
  25. McLean, G.; Wilson, A. Shopping in the digital world: Examining customer engagement through augmented reality mobile applications. Comput. Hum. Behav. 2019, 101, 210–224. [Google Scholar] [CrossRef]
  26. Gómez-García, G.; Rodríguez-Jiménez, C.; Marín-Marín, J.A. La trascendencia de la Realidad Aumentada en la motivación estudiantil. Una revisión sistemática y meta-análisis. Alteridad 2020, 15, 36–46. [Google Scholar] [CrossRef]
  27. Rezaee, S.; Sadeghi, A.; Shakeri, M.; Choi, S.M. Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts. Appl. Sci. 2021, 11, 6047. [Google Scholar] [CrossRef]
  28. Elfeky, A.I.M.; Elbyaly, M.Y.H. The effectiveness of virtual classrooms in developing academic motivation across gender groups. Ann. For. Res. 2023, 66, 2005–2020. [Google Scholar]
  29. Buchner, J. Generative learning strategies do not diminish primary students’ attitudes towards augmented reality. Educ. Inf. Technol. 2021, 26, 701–717. [Google Scholar] [CrossRef]
  30. Koç, Ö.; Altun, E.; Yüksel, H.G. Writing an expository text using augmented reality: Students’ performance and perceptions. Educ. Inf. Technol. 2021, 1, 845–866. [Google Scholar] [CrossRef]
  31. Hanafi, H.F.; Said, C.S.; Wahab, M.H.; Samsuddin, K. Improving students’ motivation in learning ICT course with the use of a mobile augmented reality learning environment. IOP Conf. Ser. Mater. Sci. Eng. 2017, 226, 012114. [Google Scholar] [CrossRef]
  32. Lin, H.C.K.; Lin, Y.H.; Wang, T.H.; Su, L.K.; Huang, Y.M. Effects of incorporating Augmented Reality into a Board Game for High School Students’ Learning Motivation and Acceptance in Health Education. Sustainability 2021, 13, 3333. [Google Scholar] [CrossRef]
  33. Bursztyn, N.; Walker, A.; Shelton, B.; Pederson, J. Assessment of student learning using augmented reality Grand Canyon field trips for mobile smart devices. Geosphere 2017, 13, 260–268. [Google Scholar] [CrossRef]
  34. Akçayır, M.; Akçayır, G. Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educ. Res. Rev. 2017, 20, 1–11. [Google Scholar] [CrossRef]
  35. López-Belmonte, J.; Pozo-Sánchez, S.; López-Belmonte, G. The effectiveness of augmented reality in infant education: A BLS and CPR learning study in 5-year-old students. Pixel-Bit 2019, 55, 157–178. [Google Scholar] [CrossRef]
  36. Cheng, Y.; Bololia, L. The Effects of Augmented Reality on Social Skills in Children with an Autism Diagnosis: A Preliminary Systematic Review. J. Autism Dev. Dis. 2023, 53, 1317–1331. [Google Scholar] [CrossRef]
  37. Fernández-Robles, B.; Martínez-Pérez, S. Experiencia formativa sobre el uso de realidad aumentada con estudiantes del grado de Pedagogía. Rev. Tecnol. Cienc. Educ. 2023, 24, 119–140. [Google Scholar] [CrossRef]
  38. Hadi, S.H.; Permanasari, A.E.; Hartanto, R.; Sakkinah, I.S.; Sholihin, M.; Sari, R.C.; Haniffa, R. Developing augmented reality-based learning media and users’ intention to use it for teaching accounting ethics. Educ. Inf. Technol. 2022, 27, 643–670. [Google Scholar] [CrossRef]
  39. Sat, M.; Ilhan, F.; Yukselturk, E. Comparison and evaluation of augmented reality technologies for designing interactive materials. Educ. Inf. Technol. 2023, 28, 11545–11567. [Google Scholar] [CrossRef]
  40. Del-Rio-Guerra, M.S.; Martin-Gutiérrez, J.; Lopez-Chao, V.A.; Flores-Parra, R.; Ramírez-Sosa, M.A. AR graphic representation of musical notes for self-learning on guitar. Appl. Sci. 2019, 9, 4527. [Google Scholar] [CrossRef]
  41. Tarng, W.; Lin, Y.J.; Ou, K.L. A virtual experiment for learning the principle of daniell cell based on augmented reality. Appl. Sci. 2021, 11, 762. [Google Scholar] [CrossRef]
  42. Gómez-Tone, H.C.; Martin-Gutiérrez, J.; Valencia-Anci, L.; Mora-Luis, C.E. International comparative pilot study of spatial skill development in engineering students through autonomous augmented reality-based training. Symmetry 2020, 12, 1401. [Google Scholar] [CrossRef]
  43. Gallego-Pérez, O.M. Estudio y Análisis Sobre Las Posibilidades Educativas de la Realidad Aumentada Como Herramienta de Producción de Experiencias Formativas Por Parte del Alumnado Universitario. Ph.D. Thesis, Universidad de Córdoba, Córdoba, Spain, 2018. Available online: https://acortar.link/MpdWhO (accessed on 19 July 2025).
  44. Bisquerra-Alzina, R. Metodología de la Investigación Educativa; La Muralla: Madrid, Spain, 2004; pp. 167–192. [Google Scholar]
  45. McMillan, J.H.; Schumacher, S. Investigación Educativa; Pearson Adison Wesley: Madrid, Spain, 2005; pp. 127–357. Available online: https://acortar.link/Oaunt (accessed on 15 August 2025).
  46. Del Canto, E.; Silva, A.S. Metodología cuantitativa: Abordaje desde la complementariedad en ciencias sociales. Rev. Cienc. Soc. 2013, 141, 25–34. [Google Scholar] [CrossRef]
  47. Urquidi Martin, A.C.; Calabor Prieto, M.S.; Tamarit Aznar, C. Entornos virtuales de aprendizaje: Modelo ampliado de aceptación de la tecnología. Rev. Elect. Investig. Educ. 2019, 21, e22. [Google Scholar] [CrossRef]
  48. Ursavaş, Ö.F. Conducting Technology Acceptance Research in Education: Theory, Models, Implementation, and Analysis; Springer: New York, NY, USA, 2022. [Google Scholar] [CrossRef]
  49. Millapi, A.; Tardon, M. Uso de Realidad Aumentada en el Ámbito Educativo Para el Desarrollo de Aplicaciones que Enseñen y Entretengan (Edutainment). Grado’s Thesis, Universidad Nacional de la Patagonia, Comodoro Rivadavia, Argentina, 2015. Available online: https://acortar.link/y3Qfi7 (accessed on 20 July 2024).
  50. Drljević, N.; Botički, I.; Wong, L.H. Observing student engagement during augmented reality learning in early primary school. J. Comput. Educ. 2024, 11, 181–213. [Google Scholar] [CrossRef]
  51. Chen, C.-C.; Kang, X.; Li, X.-Z.; Kang, J. Design and Evaluation for Improving Lantern Culture Learning Experience with Augmented Reality. Int. J. Hum. Comput. Interact. 2024, 40, 1465–1478. [Google Scholar] [CrossRef]
  52. Prasittichok, P.; Jitprapaikulsarn, S.; Sriprasertpap, K.; Hwang, G.-J. Technological solutions to fostering students’ moral courage: An augmented reality-based contextual gaming approach. Cogent Educ. 2024, 11, 2378276. [Google Scholar] [CrossRef]
  53. Nelson, S.; Darni, R.; Haris, F.; Ilham, I.; Ndayisenga, J.; Septri, S.; Sari, D.N.; Festiawan, R.; Ockta, Y. The effectiveness of learning media based on digital augmented reality (AR) technology on the learning outcomes of martial arts. Retos 2025, 63, 878–885. [Google Scholar] [CrossRef]
  54. Gill, A.S.; Irwin, D.; Long, P.; Sun, L.; Towey, D.; Yu, W.; Zhang, Y.; Zheng, Y. Enhancing learning in design for manufacturing and assembly: The effects of augmented reality and game-based learning on student’s intrinsic motivation. Interact. Technol. Smart Educ. 2025, 22, 61–80. [Google Scholar] [CrossRef]
  55. Omarov, N.; Omarov, B.; Azhibekova, Z.; Omarov, B. Applying an augmented reality game-based learning environment in physical education classes to enhance sports motivation. Retos 2024, 60, 269–278. [Google Scholar] [CrossRef]
  56. Kim, J.; Wang, K.; Miller, J.; Dorneich, M.C.; Winer, E.; Brown, L.; Caldwell, B. Creating augmented reality-based experiences for aviation weather training: Challenges, opportunities, and design implications for 3D authoring. Ergonomics 2025, 68, 374–390. [Google Scholar] [CrossRef]
  57. Lu, D. The Impact of Augmented Reality on Motor Skills in Piano Education: Improving the Left-Hand Technique with AR Piano. Int. J. Hum. Comput. Interact. 2024, 41, 8398–8408. [Google Scholar] [CrossRef]
  58. Kulkarni, R.V.; Harne, R. Adoption and usage of augmented reality-based virtual laboratories tool for engineering studies. J. Inf. Technol. Educ. Innov. Pract. 2024, 23, 101. [Google Scholar] [CrossRef]
  59. Del Moral-Pérez, M.E.; López-Bouzas, N.; Castañeda-Fernández, J. Transmedia skill derived from the process of converting films into educational games with augmented reality and artificial intelligence. J. New Approaches Educ. Res. 2024, 13, 15. [Google Scholar] [CrossRef]
  60. Nikou, S.A.; Perifanou, M.; Economides, A.A. Development and validation of the teachers’ augmented reality competences (TARC) scale. J. Comput. Educ. 2024, 11, 1041–1060. [Google Scholar] [CrossRef]
  61. Akinradewo, O.; Hafez, M.; Aigbavboa, C.; Ebekozien, A.; Adekunle, P.; Otasowie, O. Innovating Built Environment Education to Achieve SDG 4: Key Drivers for Integrating Augmented Reality Technologies. Sustainability 2024, 16, 8315. [Google Scholar] [CrossRef]
  62. Ventoulis, E.; Xinogalos, S. AR The Gods of Olympus: Design and Pilot Evaluation of an Augmented Reality Educational Game for Greek Mythology. Multimodal Technol. Interact. 2023, 7, 2. [Google Scholar] [CrossRef]
  63. Lampropoulos, G.; Keramopoulos, E.; Diamantaras, K.; Evangelidis, G. Augmented Reality and Gamification in Education: A Systematic Literature Review of Research, Applications, and Empirical Studies. Appl. Sci. 2022, 12, 6809. [Google Scholar] [CrossRef]
  64. Tuta, J.; Luić, L. D-Learning: An Experimental Approach to Determining Student Learning Outcomes Using Augmented Reality (AR) Technology. Educ. Sci. 2024, 14, 502. [Google Scholar] [CrossRef]
  65. Rizki, I.A.; Suprapto, N.; Saphira, H.V.; Alfarizy, Y.; Ramadani, R.; Saputri, A.D.; Suryani, D. Cooperative model, digital game, and augmented reality-based learning to enhance students’ critical thinking skills and learning motivation. J. Pedagog. Res. 2024, 8, 339–355. [Google Scholar] [CrossRef]
Information 16 00743 g001
Figure 1. TAM configured for the study.
Figure 1. TAM configured for the study.
Information 16 00743 g001
Information 16 00743 g002
Figure 2. Graphic marker.
Figure 2. Graphic marker.
Information 16 00743 g002
Information 16 00743 g003
Figure 3. ComputAR home screen.
Figure 3. ComputAR home screen.
Information 16 00743 g003
Information 16 00743 g004
Figure 4. Atari 800 computer (ComputAR).
Figure 4. Atari 800 computer (ComputAR).
Information 16 00743 g004
Information 16 00743 g005
Figure 5. Hardware section screen (ComputAR).
Figure 5. Hardware section screen (ComputAR).
Information 16 00743 g005
Table 1. Data collection tool.
Table 1. Data collection tool.
FactorsIdentifierQuestions (Likert Scale 1–7)
Perceived usefulnessPU1The use of this AR system will improve my learning and performance in this subject
PU2Using the AR system during class would make it easier for me to understand certain concepts
PU3I think the AR system is useful when learning
PU4Using AR would improve my performance
Perceived ease of usePEU1I think the AR system is easy to use
PEU2Learning to use the AR system is not a problem for me
PEU3Learning to use the AR system is clear and understandable
 Perceived enjoymentPE1Using the AR system is fun
PE3I enjoyed using the AR system
PE2I think the AR system allows me to learn while playing
Attitude towards usingATT1Using an AR system makes learning more interesting
ATT3I have not been bored using the AR system
ATT2I think using an AR system in the classroom is a good idea
Behavioural intention
to use		BI1I would like to use it in the future of the AR system if I had the opportunity
BI2I would like to use the AR system to learn other topics
Table 2. Cronbach’s alpha for each dimension.
Table 2. Cronbach’s alpha for each dimension.
DimensionCronbach’s Alpha
Perceived usefulness0.924
Perceived easy of use0.914
Perceived enjoyment0.854
Attitude towards using0.706
Behavioural intention to use0.821
Table 3. Sequencing of the experience.
Table 3. Sequencing of the experience.
SessionsProcess Followed in the Development of the Experience
Session 1Installation and initial configuration of the ComputAR mobile application
Introductory brainstorming activity to explore students’ prior knowledge about computers
Session 2Collaborative research using OneNote to gather information on the origin, historical development, and key features of computing machines
Sessions 3–4Creation of a digital timeline integrating 3D representations of historically significant machines and computers using the Canva platform
Session 5Exploration and explanation of the internal and external components of a desktop computer through the ComputAR Augmented Reality application
Sessions 6–7Development of interactive infographics using Genially to illustrate and describe the main hardware components of a computer system
Sessions 8–9Design and creation of multimedia presentations with technical specifications, pricing information, and product visuals using PowerPoint
Session 10Delivery of 3–5 min oral presentations simulating a business scenario in which students present and justify their hardware configurations
Session 11Complete the Technology Acceptance Model survey (TAM)
Table 4. TAM instrument: means and standard deviations achieved.
Table 4. TAM instrument: means and standard deviations achieved.
DimensionMeanTypical Deviation
TAM5.910.75
Perceived usefulness5.820.89
Perceived ease of use5.591.11
Perceived enjoyment6.050.88
Attitude towards using6.130.70
Behavioural intention to use6.110.80
Table 5. Basic statistics according to the ICT previous experience of the students.
Table 5. Basic statistics according to the ICT previous experience of the students.
DimensionsICT Previous ExperienceNMeanTypical Deviation
Behavioural intention
to use (BI)		Si766.17860.87574
No2455.83330.85431
Perceived ease of use (PEU)Si765.83330.98108
No2455.20991.20487
Perceived usefulnessSi765.93450.94592
No2455.64810.78084
Table 6. Student’s t-test for dimensions “perceived enjoyment”, “perceived ease of use”, and “perceived usefulness” according to ICT previous experience.
Table 6. Student’s t-test for dimensions “perceived enjoyment”, “perceived ease of use”, and “perceived usefulness” according to ICT previous experience.
Dimensions Levene’s TestStudent’s t-Test
Fp-ValueTdfp-Value
Behavioural intention
to use (BI)		Equal variances1.050.353.063190.0024
No equal variances  3.02122.530.0031
Perceived ease of use (PEU)Equal variances1.510.354.113210.0000
No equal variances  4.57151.410.0000
Perceived usefulness (PU)Equal variances1.470.352.653210.0084
No equal variances  2.40108.570.0182
Note: degree of freedom (df).
Table 7. Basic statistics according to the gender of the students.
Table 7. Basic statistics according to the gender of the students.
DimensionsGenderNMeanTypical Deviation
Behavioural intention
to use (BI)		Men2146.06060.86923
Women1075.93330.99363
Perceived ease of use (PEU)Men2145.64231.09990
Women1075.15561.16064
Perceived usefulnessMen2145.83130.88214
Women1075.75001.00889
Table 8. Student’s t-test for dimensions “perceived enjoyment”, “perceived ease of use”, and “perceived usefulness” according to “gender”.
Table 8. Student’s t-test for dimensions “perceived enjoyment”, “perceived ease of use”, and “perceived usefulness” according to “gender”.
Dimensions Levene’s TestStudent’s t-Test
Fp-ValueTdfp-Value
Behavioural intention
to use (BI)		Equal variances1.310.351.183190.240
No equal variances  1.131890.261
Perceived ease of use (PEU)Equal variances1.110.353.673210.000
No equal variances  3.602020.000
Perceived usefulness (PU)Equal variances1.310.350.743210.459
No equal variances  0.711890.479
Note: degree of freedom (df).
Table 9. Pearson’s correlation between “perceived ease of use” and the dimensions “perceived enjoyment”, “perceived usefulness”, and “attitude towards using”.
Table 9. Pearson’s correlation between “perceived ease of use” and the dimensions “perceived enjoyment”, “perceived usefulness”, and “attitude towards using”.
Dimension Perceived EnjoymentPerceived UsefulnessAttitude Towards Using
Perceived ease of use (PEU)Pearson correlation0.5400.5830.530
p-value0.0000.0000.000
Table 10. Pearson’s correlation between “perceived usefulness” and the dimensions “perceived enjoyment”, “attitude towards using”, and “behavioural intention to use”.
Table 10. Pearson’s correlation between “perceived usefulness” and the dimensions “perceived enjoyment”, “attitude towards using”, and “behavioural intention to use”.
Dimension Perceived EnjoymentAttitude Towards UsingBehavioural Intention to Use
Perceived usefulnessPearson correlation0.7010.5870.620
p-value0.0000.0000.000
Table 11. Pearson’s correlation between “perceived enjoyment” and the dimensions “attitude towards using” and “behavioural intention to use”.
Table 11. Pearson’s correlation between “perceived enjoyment” and the dimensions “attitude towards using” and “behavioural intention to use”.
Dimension Attitude Towards UsingBehavioural Intention to Use
Perceived enjoymentPearson correlation0.5830.709
p-value0.0000.000
Table 12. Pearson’s correlation between “attitude towards using” and “behavioural intention to use”.
Table 12. Pearson’s correlation between “attitude towards using” and “behavioural intention to use”.
Dimension Behavioural Intention to Use
Attitude towards usingPearson correlation0.687
p-value0.000
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

Amores-Valencia, A.; Burgos, D.; Branch-Bedoya, J.W. Evaluation of the Use and Acceptance of an AR Mobile App in High School Students Using the TAM Model. Information 2025, 16, 743. https://doi.org/10.3390/info16090743

AMA Style

Amores-Valencia A, Burgos D, Branch-Bedoya JW. Evaluation of the Use and Acceptance of an AR Mobile App in High School Students Using the TAM Model. Information. 2025; 16(9):743. https://doi.org/10.3390/info16090743

Chicago/Turabian Style

Amores-Valencia, Antonio, Daniel Burgos, and John W. Branch-Bedoya. 2025. "Evaluation of the Use and Acceptance of an AR Mobile App in High School Students Using the TAM Model" Information 16, no. 9: 743. https://doi.org/10.3390/info16090743

APA Style

Amores-Valencia, A., Burgos, D., & Branch-Bedoya, J. W. (2025). Evaluation of the Use and Acceptance of an AR Mobile App in High School Students Using the TAM Model. Information, 16(9), 743. https://doi.org/10.3390/info16090743

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