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Article

Augmented Reality as an Educational Tool: Transforming Teaching in the Digital Age

by
Miluska Odely Rodriguez-Saavedra
1,*,
Luis Gonzalo Barrera Benavides
1,
Iván Cuentas Galindo
2,
Luis Miguel Campos Ascuña
2,
Antonio Víctor Morales Gonzales
2,
Jiang Wagner Mamani Lopez
2 and
Ruben Washington Arguedas-Catasi
2
1
Facultad de Administración y Negocios, Universidad Tecnológica del Perú, Arequipa 04001, Peru
2
Facultad de Economía, Universidad Nacional de San Agustín de Arequipa, Arequipa 04000, Peru
*
Author to whom correspondence should be addressed.
Information 2025, 16(5), 372; https://doi.org/10.3390/info16050372
Submission received: 13 March 2025 / Revised: 15 April 2025 / Accepted: 24 April 2025 / Published: 30 April 2025
(This article belongs to the Collection Augmented Reality Technologies, Systems and Applications)
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Abstract

:
Augmented reality (AR) is revolutionising education by integrating virtual elements into physical environments, enhancing interactivity and participation in learning processes. This study analyses the impact of AR in higher education, examining its influence on ease of adoption, student interaction, academic motivation and educational sustainability. A quantitative and explanatory design was employed, applying structural equation modelling (SmartPLS) to a sample of 4900 students from public and private universities. The results indicate that AR significantly improves the ease of adoption (β = 0.867), favouring its implementation. In addition, student interaction increases academic motivation (β = 0.597), impacting on perceived academic performance (β = 0.722) and educational sustainability (β = 0.729). These findings highlight the need to design effective learning experiences with AR to maximise their impact. However, challenges such as technological infrastructure, teacher training and equitable access must be addressed to ensure sustainable adoption. This study provides empirical evidence on the potential of AR to enhance motivation, learning and educational transformation. Future research should explore its effectiveness in diverse contexts to optimise pedagogical strategies and institutional policies.

1. Introduction

Augmented reality is having a profound impact on the current educational landscape, introducing a new technological integration into traditional teaching models [1]. Among these new technologies, augmented reality (AR) is emerging as a disruptive tool that allows users to place virtual elements in the real world, thus offering an immersive learning experience and improving knowledge retention and student performance [2]. The global acceptance of AR in education is growing geometrically, with forecasts indicating that it will reach a USD 12.4 billion market by 2025 and an annual growth rate of 31.4%, signifying its entry and future development for higher learning [3].
Despite its growing technology, the incorporation of AR in the field of education still faces several obstacles [4].
One major barrier is the technology gap between advanced economies and developing countries. While 67% of educational institutions in advanced economies have implemented AR programmes in their curricula, this figure drops to 28% in regions with less access to technology, limiting its democratisation and expansion [5,6].
The key factor that determines whether this technology can be effectively applied in education is the training of teachers themselves [7].
Recent surveys have found that only 36% of university professors are confident in their ability to use AR in their teaching methods, which limits its implementation on a wider scale and in more fields [8]. Furthermore, although augmented reality has been shown to be effective in technical areas such as medicine, engineering and architecture [9], its effect on more general factors of educational quality, such as student interest, the sustainability of educational systems and interaction between teachers and students, has not yet been investigated in depth. The lack of evidence is linked to structural constraints in many universities, such as high student/teacher ratios, in some cases exceeding 40 students per teacher, as well as rigid classrooms, limited access to interactive technologies and the use of conventional assessments that hinder more dynamic learning [10,11].
Another key point of controversy in this domain of study is that scholars have reached little agreement regarding its long-term effects on learning processes [12]. While some studies indicate that the fully immersive experience of AR generates higher student engagement and helps students remember complex knowledge, others warn of negative cognitive effects and refer that correct pedagogical forms must be adopted to obtain the best benefits [13].
In this context, the present research not only aims to examine field practices in the application of AR in universities with reference to higher education but also attempts, through empirical methods, to explore important issues such as the ease of technological adoption and interaction platforms, academic motivation, and environmental protection [14]. AR has shown great potential, but there are still gaps in the literature that hinder its integration and make it difficult to use for academic learning [15].
To address this problem, this study poses the following question as its research focus: what effect does augmented reality have on the ease of technological adoption, student interaction, academic motivation and educational sustainability in higher education?
The results of this study aim not only to fill gaps in the existing literature but also to serve as a basis for the development of educational strategies and public policies that promote the effective and equitable integration of AR in academia [16]. In this way, it hopes to contribute to the establishment of a reference framework to guide educational institutions in the implementation of AR-based programmes, ensuring their sustainability and optimisation in the teaching–learning process, with the aim of benefiting students for their academic growth and knowledge [17].
In this framework, augmented reality should not be seen only as a new technological tool but as an ontological resource that significantly transforms the learning experience. Its use in the university context goes beyond the creative presentation of content as it transforms the way students relate to knowledge, promoting processes of meaningful construction, cognitive autonomy and active immersion in learning. From this approach, AR fosters a more active and conscious connection with the objects of study, favouring critical thinking, reflection and a sense of belonging to the educational process. In this way, more than a digital resource, it becomes an agent of educational change in today’s university environment.

2. Related Works

Augmented reality (AR) has been gradually integrated into educational contexts with the goal of enriching the learning experience, increasing motivation and fostering the development of cognitive skills. Different studies have explored its applications, methodologies and effects, consolidating a theoretical and empirical framework that supports its incorporation in technology-enabled educational contexts.
Fidan and Tuncel [18] conducted a quasi-experimental study with 104 students in the field of physics, showing that the inclusion of AR in problem-based learning (PBL) methodologies significantly increases learning performance and improves attitude towards the subject. Kusdiyanti et al. [19] established an educational module with an STEAM approach supported by AR to enhance entrepreneurial skills, showing progress in creativity and critical thinking. Fox [20], from a technical approach, showed the design of visual communication in AR for manual training, highlighting the importance of intuitive interfaces that favour the transfer of practical knowledge.
Wang et al. [21] carried out augmented reality simulations to optimise the teaching of mechanisms, using AutoDesk Inventor (version 2023) together with AR Toolkit (version 5.5), showing progress in spatial understanding and retention of mechanical ideas. Enzai et al. [22] created an interactive AR application for engineering programmes, highlighting its positive effect on student engagement and independence. In contrast, Fearn and Hook [23] adopt a service design thinking approach to identify obstacles and opportunities in the application of AR for educational purposes, offering a methodological framework that can be replicated in different contexts.
In the field of technology education, Rueda et al. [24] analysed the application of AR and virtual reality in the teaching of construction and graphic expression, using immersive environments created with Unity3D. Taggart et al. [25] used mixed methodologies to examine trainee teachers’ perceptions of AR, recognising its potential as an innovative pedagogical resource. Mena et al. [26] conducted a systematic review on the use of AR in teacher education, highlighting its relevance for in-service training. Wang and Tan [27] developed a conceptual model that focuses on the interaction between the user and the augmented reality application, with methodological implications for the creation of digital educational content.
Lampropoulos et al. [28] combined AR, gamification and serious games in computer science programming subjects, using ARCore and web technologies to optimise performance and knowledge retention. Chen [29] proposed an innovative idea linking AR with the Internet of Things (IoT) for music education, providing instant feedback tailored to each user. Anil and Batdi [30] used a multiple quantitative and qualitative methodological approach to analyse the effect of AR in science education, showing gains in academic performance and intrinsic motivation. Badriyah et al. [31] demonstrated, through a quantitative experimental method, that AR in synchronous virtual spaces significantly improves critical thinking. Sabbah et al. [32] examined the impact of AR on motivation and reflective thinking using questionnaires validated through factor analysis. Yüzüak and Yiğit [33] created an AR application in electronics classes to show how MOSFET transistors work, combining AR Toolkit with special technical content. Zhao et al. [34], through a bibliometric analysis in Web of Science, detected the most significant lines of research in AR applied to education during the period from 2018 to 2022. Brown et al. [35] examined its application in technical vocational training fields such as aviation, maintenance and ergonomics, highlighting its influence on practical training.
Widyaningsih et al. [36] created an educational environment with AR that adapts to students with particular needs, demonstrating its effectiveness in increasing the accessibility and understanding of content. Finally, Beheshti et al. [37] examined student collaboration in AR-supported sustainable engineering projects, showing how AR enhances negotiation, technical communication and systems thinking skills.
These studies theoretically and methodologically support the present research, which examines the effect of AR in higher education from a quantitative perspective, analysing its relationship with ease of use, motivation, sustainability and usability in a large representative sample of university students.
Despite the many educational advantages of augmented reality in university contexts, significant limitations were also found. These include the dependence on a robust technological infrastructure, the need for compatible mobile devices and the potential for student distraction if its use is not properly organised. Also, its combination with traditional pedagogical approaches, such as lectures or text-only learning environments, remains a challenge. The effectiveness of AR depends on an instructional design that is attuned to the learning objectives and strong teacher preparation for its proper implementation.

3. Materials and Methods

This study employed an explanatory level of research to analyse the causal relationships between key variables associated with the use of augmented reality (AR) in higher education [38]. A quantitative approach with a non-experimental, cross-sectional design was adopted, allowing for an assessment of students’ experiences at a particular point in time [39]. Unlike experimental studies that evaluate the effectiveness of complex AR systems developed specifically for research, this research is based on an actual classroom implementation using accessible tools. Students actively interacted with three-dimensional models projected via QR codes during academic sessions, providing a practical and meaningful context for data collection. This direct application of AR supports the validity of using a structured questionnaire to assess perceived ease of use, academic motivation, interactivity and educational sustainability. The methodological approach is aligned with current educational research practices and allows for capturing user-centred insights that complement studies focused solely on the technical effectiveness of systems [40].

3.1. Study Population and Sampling

The study population consisted of 4900 students from seven universities, categorised as 43% public and 57% private institutions. The sample was selected using purposive sampling, ensuring that participants had prior experience with emerging technologies such as augmented reality [41]. This approach ensured that respondents had sufficient knowledge to provide informed responses on the adoption of AR in educational settings [42].

3.2. Data Collection Instruments

A structured survey was used for data collection. A 7-point Likert scale was used to measure the respondents’ attitude towards AR HMD, where 1 means “strongly disagree” and 7 means “strongly agree”. Google Forms was used to host and distribute our survey tool [43]. The instrument was designed to measure constructs that are key to technology adoption: the perceived usefulness of an innovation, as well as the user’s experience with it [44]. The questionnaire was administered after students completed the academic sessions using augmented reality tools. This ensured that their responses were based on direct and recent experience with the technology. The activity included real-time interaction with 3D models projected via QR codes during class, which provided a meaningful context for assessing variables such as ease of use, academic motivation, interactivity and sustainability. The timing of data collection was thus aligned with the goal of capturing insights grounded in actual use. Table 1 below shows the latent and observed variables applied in the study.

3.3. Technical and Functional Aspects of the Augmented Reality Application

The implemented solution was created following criteria of functionality and flexibility, prioritising technological accessibility in university contexts without specialised augmented reality infrastructure. A system was chosen that uses QR codes generated from online platforms, which were integrated into printed materials and exhibited presentations. These codes included direct links to 3D models optimised for instant viewing through common browsers.
Students used their mobile devices, either phones or tablets, without requiring the installation of external applications or high-performance equipment. Interoperability between platforms was essential to ensure a consistent experience, facilitating the projection of 3D models directly onto tangible surfaces. Among the elements observed were historical busts, mechanical components and scientific structures. This technical strategy ensured an inclusive, versatile and cost-effective environment, demonstrating that AR can be successfully applied even in institutions without dedicated labs or software.

3.4. Hypothesis Statement

This study is based on the main hypothesis that augmented reality is an educational tool that transforms teaching in the digital age. Based on this premise, the following sub-hypotheses are put forward:
H1: 
The use of augmented reality (RAU) has a positive influence on the ease of adoption (FA) in teaching–learning processes.
H2: 
Educational relevance (ER) has a positive effect on student interaction (SI) in educational environments that integrate augmented reality.
H3: 
Student interaction (SI) has a positive impact on academic motivation (MA) in the context of the use of emerging technologies such as augmented reality.
H4: 
Ease of adoption (FA) has a positive influence on academic motivation (MA) in the use of augmented reality in educational processes.
H5: 
Academic motivation (AM) has a positive effect on perceived academic performance (PAR) and educational sustainability (ES) in educational institutions.
This hypothesis statement guides the structure of the analysis and the empirical validation of the relationships between the variables of the proposed model [38].

3.5. Data Processing and Analysis

Initial data organisation was performed in Microsoft Excel, followed by exploratory analysis in Python (version 3.10). The pandas (version 1.5.3) and numpy (version 1.24.2) libraries were used for data cleaning, while seaborn (version 0.12.2) and matplotlib (version 3.7.1) facilitated data visualisation. The main statistical analysis was carried out with SmartPLS (version 4.0.8.7), a structural equation modelling (SEM) tool. This approach was chosen because of its ability to model latent variables and analyse complex relationships without requiring strict assumptions of normality [45].
To validate the model, the following statistical tests were performed:
Convergent and discriminant validity tests: a factorial loading greater than 0.7 and a mean extracted variance value (AVE) greater than 0.5 were considered valid.
Assessment of model fit: The Standard Ratio Mean Residual (SRMR) was used to verify model fit.
Reliability analysis: Cronbach’s alpha and composite reliability values were calculated to assess internal consistency.
Multicollinearity control: Variance Inflation Factor (VIF) was applied to ensure that multicollinearity did not distort the results.
Bootstrapping: 5000 iterations were performed to estimate the confidence intervals and to reinforce the robustness of the results.
Heteroskedasticity and side effects: Additional tests were carried out to detect possible structural biases.

3.6. Ethical Considerations

This study was conducted following rigorous ethical guidelines, guaranteeing not only the confidentiality of the participants, but also the integrity of the research and respect for the rights of each individual involved [46]. Participation was completely voluntary, ensuring that each person felt comfortable and willing to contribute without any external pressure.
Prior to data collection, ethical approval was obtained from the relevant institutional review board, a crucial step that underscores the study’s commitment to best practices in research. This not only strengthens the validity of the findings but also sets a precedent for future studies in the field [47]. All participants provided informed consent, which is essential to ensure that they fully understand the scope of the study and its impact on their lives [48].

3.7. Data Availability Statement

The data, models and survey instruments used in this research are available upon request. The public dissemination of raw data is subject to certain restrictions due to institutional policies. Even so, aggregated results and processed datasets will be made available for replication purposes upon reasonable request, in accordance with established academic research standards [48]. This methodological approach aims to make research results transparent, reproducible and in line with academic standards of AR educational technology [49].

4. Results

This section presents the findings of the study, organised in sub-sections to address the reliability and validity of the measurement model, the explanatory power of the structural model, the discriminant validity and the coefficients of the relationships between variables. Reliability, convergent and discriminant validity analyses are included, as well as the evaluation of the impact of the exogenous variables on the endogenous variables. The results obtained allow us to validate the robustness of the proposed model and its applicability in augmented reality-based education [50,51].

4.1. Reliability and Validity of the Construct

Table 2 presents the reliability and validity values of the measurement model. All Cronbach’s Alpha and rho_A values exceed the threshold of 0.70, indicating excellent internal consistency of the items in each construct. Likewise, the composite reliability (CR) exceeds the 0.70 threshold for all variables, validating the consistency of the indicators used. In terms of convergent validity, the average variance extracted (AVE) values are above 0.50, confirming that the constructs explain more than 50% of the variance of their indicators. Educational Sustainability (SER) stands out with the highest reliability (0.933) and validity (AVE = 0.882), as well as Ease of Adoption (FA), which also shows strong values (Cronbach’s Alpha = 0.91, AVE = 0.847). These results validate the quality of the measures used in the study.

4.2. Explanatory Power of the Model

Table 3 shows the values of R2, adjusted R2 and effect size (f2) for the endogenous variables of the structural model. The R2 value indicates the proportion of the variance explained by the exogenous variables in each endogenous variable. Ease of Adoption (FA) presents the highest R2 value (0.753), which shows a high explanatory power of Augmented Reality (RAU). Academic Motivation (AM), with an R2 of 0.640, reflects good explanatory power influenced by Ease of Adoption (AF) and Student Interaction (SI). In terms of effect size (f2), the RAU -> FA relationship stands out with a value of 3.041, indicating a significant and high effect. Other relevant relationships include RE -> IE (f2 = 1.176), MA -> SER (f2 = 1.137) and MA -> RAP (f2 = 1.091), all with moderate effects. In contrast, FA -> MA shows a smaller effect (f2 = 0.394), although significant.

4.3. Discriminant Validity

Table 4 presents the discriminant validity using the Fornell–Larcker criterion. It is observed that each construct explains the variance of its own indicators better than that of other constructs, given that the values on the main diagonal (square roots of AVE) are higher than the correlations with other variables. Ease of Adoption (FA), Educational Sustainability (SER) and Academic Motivation (MA) stand out, with values of 0.920, 0.939 and 0.905, respectively, indicating excellent discrimination in relation to other constructs. These results confirm that the measurement model has adequate discriminant validity, supporting the conceptual independence of each construct in the analysis.

4.4. Path Coefficients and Significance

Table 5 presents the path coefficients that assess the relationships between the variables in the structural model. All relationships are significant, with p values ≤ 0.05 and t-statistics greater than 1.96. The strongest relationship is observed between RAU -> FA (β = 0.867, t = 31.327), indicating that the use of augmented reality significantly influences ease of adoption. Similarly, MA -> RAP (β = 0.722, t = 16.351) and MA -> SER (β = 0.729, t = 16.288) show the positive impact of academic motivation on educational achievement and educational sustainability. On the other hand, RE -> IE (β = 0.735, t = 17.405) stands out as a key predictor of student interaction, while FA -> MA (β = 0.243, t = 2.734) has a moderate positive effect. These results reinforce the robustness of the model in explaining the interactions between the variables.

4.5. Confidence Intervals

Table 6 presents the 95% confidence intervals for the structural model relationships. No interval includes the value zero, which confirms the statistical significance of the relationships. The strongest relationships are RAU -> FA (CI: 0.821–0.913) and MA -> SER (CI: 0.645–0.794), evidencing the strong influence of augmented reality use on ease of adoption and academic motivation on educational sustainability. In contrast, the FA -> MA relationship (CI: 0.105–0.395) shows a moderate but significant effect. These results confirm the stability and validity of the proposed structural model.

4.6. Structural Model

Figure 1 shows the structural model with the relationships between the underlying variables and their indicators. The R2 values indicate that the endogenous variables have good explanatory power: MA (R2 = 0.640) is influenced by Ease of Adoption (FA) and Student Interaction (IE), while SER (R2 = 0.532) and RAP (R2 = 0.522) depend mainly on Academic Motivation (MA). The strongest relationship is RAU -> FA (β = 0.867), which underlines the importance of the use of augmented reality in facilitating technology adoption. The relationships MA -> SER (β = 0.729) and MA -> RAP (β = 0.722) also stand out, reinforcing the central role of academic motivation in educational outcomes. This model demonstrates the relevance of augmented reality in educational transformation, promoting adoption, interaction, motivation and, ultimately, sustainability and academic performance.
Figure 2 presents the structural model representing the relationships between the latent variables and their respective observable indicators. The coefficients of determination (R2), located within the circular nodes, indicate the percentage of variance explained for each endogenous construct. The structural paths, represented by directional arrows, contain the standardised coefficients (β), which reflect the magnitude and direction of the effects between variables. It is observed that the use of augmented reality (RAU) has a direct and significant effect on ease of adoption (FA) (β = 0.867); in turn, FA and student interaction (IE) significantly explain academic motivation (MA), the coefficient of determination of which reaches a value of R2 = 0.640. Likewise, MA has a positive effect on perceived academic achievement (PAR) and educational sustainability (ES), both with adequate levels of explained variance. Furthermore, the factor loadings of the observable indicators exceed the threshold of 0.80, which supports the convergent validity of the proposed model. As mentioned above, RAU corresponds to the use of augmented reality; MA, to academic motivation; SER, to educational sustainability; FA, to ease of adoption; IE, to student interaction; and RAP, to perceived academic performance.

4.7. The Practical Application of AR in the University Environment

Augmented reality was integrated as part of the development of regular academic sessions, without modifying the pre-existing curricular structure. This implementation allowed students to interact with three-dimensional models from their own mobile devices, transforming the classroom into a dynamic and participatory environment.
During the experience, there was a high frequency of the use of AR tools (RAU1) by students, who had access to resources that facilitated the understanding of complex concepts. In addition, the content presented was aligned with the course objectives (RE1), which reinforced the perceived usefulness of the technology in their field of study.
Interaction was a key component: activities promoted peer collaboration (IE1) through joint analysis of models and collaborative task execution. In addition, students were more motivated to learn (MA2) during the AR sessions compared to traditional methods. This motivation resulted in increased engagement and participation.
In terms of implementation, the experience was perceived as easy to adopt (FA1), as it did not require advanced technical knowledge or excessive preparation time. This favoured the continuity of use throughout the course. As a result, students reported a better understanding of the concepts (RAP1) and a positive perception of the impact of AR on their academic performance.
Finally, it was observed that the majority of students considered that this technology should be implemented permanently in teaching-learning processes (SER1), thus validating its sustainability as an innovative educational resource in higher education. As part of this educational integration, Figure 3 presents an activity carried out during a university session, in which students used their mobile devices to explore three-dimensional historical busts through the use of augmented reality. This experience promoted the active participation of students and reinforced the link with curricular content, providing important empirical support to the quantitative analysis developed in this research.
Figure 4 shows the application of augmented reality to investigate preferred technological models, such as a robotic arm and a rover. This interaction facilitated situating the learning in practical disciplines, helping to record essential information that reinforced the organised statistical analysis of the research.
The figures presented reflect the structural validation of the proposed model and its practical application in educational contexts, demonstrating the relevance of augmented reality as an innovative resource in university education.

5. Discussion

The overall results confirm that there is a significant effect of augmented reality (AR) on the ease of adoption of any technological innovation (β = 0.867), which emphasises the role of AR in the integration of new tools in the educational context. These results are in agreement with studies by Akinradewo et al. [52] and Nelson et al. [53], as perceived accessibility encourages adoption and improves pedagogical methodologies. However, this contrasts with Nikou et al. [54], who argue that the cognitive overload of AR is accentuated if not adequately controlled and provide evidence that it affects instruction and pedagogical context differently.
In terms of student interaction, instructional relevance had a positive effect (β = 0.735), indicating that pedagogical designs that integrate the practical productivity of AR enhance student engagement and collaboration. This is consistent with findings reported by Nikou et al. [55] and Di Fuccio et al. [56], demonstrating that AR, as an integral part of sound teaching methods, motivates and engages students. In contrast, Del Moral-Perez et al. [57] and Stalheim and Somby [58] warn that over-reliance on AR hinders critical thinking, as long as the interactive material is not designed to foster learner autonomy.
In addition, AR-mediated student interaction positively influences academic motivation (β = 0.597), which aligns with the results presented by Yu-niarti et al. [59] and Gill et al. [60], who state that environments that promote collaboration lead students to develop intrinsic interest. However, they point out that despite the added value of AR for learning, it does not always lead to better academic performance as its success ultimately depends on how it is combined with active methodologies to ensure long-term retention.
The study also found that ease of adoption had a moderate impact on academic motivation (β = 0.243), implying that its impact is conditional on the pedagogical strategies that complement it. Kulkarni and Harne [61] and Chen et al. [62] reaffirm that perceived technological simplicity must be accompanied by sound instructional design to enhance its motivational effect. In contrast, Lu [63] shows that ease of adoption is not a sufficient condition for the effectiveness of AR if teachers are not sufficiently trained to use its potential.
Furthermore, academic motivation directly influenced perceived academic performance (β = 0.722) and educational sustainability (β = 0.729). The findings are in agreement with Chen et al. [64] and Karelkhan and Uderbayeva [65], respectively, who found that AR contributes to increased intrinsic motivation that helps students achieve academic success and ensures their continued interest in new technologies. However, Kim et al. [66] and Muttaqiin et al. [67] emphasise that the long-term viability of AR in education relies not only on motivation but also on long-term investment in infrastructure and teacher training, which could prove to be a barrier to its widespread implementation.
The results of this study show that educational institutions should not consider AR as a separate technological solution, but rather as part of a broader teaching ecosystem, linked to active methodologies and continuous teacher development [68]. They can also provide valuable information to educational decision-makers that can be used to determine how to allocate funds and train teachers to ensure the effective use of AR within the education system [69].
Future research could explore additional variables that may determine the acceptance and impact of AR in different educational contexts [70]. In addition, longitudinal studies could assess the long-term effects of AR integration on student achievement and institutional sustainability. Finally, comparative studies across multiple disciplines and academic levels will help to gain a deeper understanding of the influence of AR on learning experiences in different academic settings.
The results of this research make it possible to establish a clear contrast with educational experiences that do not incorporate immersive technologies such as augmented reality. In traditional approaches, based on the use of texts, presentations or linear audiovisual materials, less active student participation and reduced interaction with the learning content has been observed. In response to this, AR transforms this connection by enabling the learner to relate directly to three-dimensional elements, enriching spatial understanding, motivation and the meaningful internalisation of knowledge. This variation in methodology positions AR as a transformative tool in university education by making the student an active participant in their learning, overcoming the restrictions of traditional expository approaches.
The study was conducted on a unified sample that included students from public and private universities, which facilitated the identification of significant differences in the perception of augmented reality according to the type of institution. Private universities showed higher rates of technology adoption and a favourable assessment towards educational sustainability, which was due to their better access to digital infrastructure, technical assistance and academic innovation programmes. In contrast, students at public universities showed more barriers to adoption, linked to institutional and technological constraints. These variations validated that the institutional context has a direct influence on the educational experience facilitated by AR, which underlines the urgency of applying differentiated technology integration strategies according to the particularities of each type of university.
Unlike studies that collect perceptions without an actual AR application, this research is based on structured responses collected immediately after students experienced an actual augmented reality application in the classroom. This distinction increases the validity of the results, as the students’ responses were based on direct interaction with the AR tools and not on hypothetical or anticipated use. Therefore, this study offers a practical user-centred perspective that complements system-centred experimental designs.
The implementation of augmented reality has specific limitations that need to be considered. During the development of educational sessions, the presence of cognitive overload was evident, generated by the requirement to simultaneously process digital and conceptual information, especially in subjects with a high theoretical load. Likewise, the interactive and immersive nature of AR generated significant distractions in students with low self-regulation, affecting concentration on learning objectives. These factors confirm the need to accompany the use of AR with structured pedagogical planning, aimed at maintaining the academic focus without the technology becoming a disruptive element.

6. Conclusions

The results of this study corroborate that augmented reality, used in higher education, is much more than just a technological tool, it establishes itself as a transformative pedagogical resource that changes the ways in which knowledge is acquired. By integrating AR into regular classes, using accessible mobile devices, the learning environment was adjusted to the student and not the other way around. This change led to more genuine educational experiences, where knowledge was not transmitted but experienced, giving rise to a deeper dimension of learning, related to being and knowing.
From a pedagogical approach, AR facilitated not only the visualisation of 3D objects, but also promoted processes of reflection, analysis and cooperation that increased student participation and engagement. These interactions were not restricted to a functional reaction to a technological stimulus but fostered a relevant connection between the student and the subject of study, favouring an experience where knowledge emerged from the exchange between perception, action and understanding. In this context, AR not only improved academic results but created the conditions for meaningful learning, which changed the role of the student from passive receiver to active participant in his or her own educational process.
Similarly, evidence indicates that the effect of AR on motivation, performance and educational sustainability is closely related to the way in which this technology is integrated into well-designed pedagogical strategies. It is not the tool that produces learning but the way it is incorporated into an intentional, reflective and pedagogically directed process. By avoiding the isolated use of AR and integrating it into defined educational objectives, not only immediate learning but also a critical and lasting appropriation of knowledge was fostered.
This study not only provides important quantitative results but also offers a perspective on educational transformation in the digital age. It shows that it is possible to move towards more active, participatory and personalised models, without requiring complex laboratories or large investments. The secret lies in the educational perspective and in the institutional commitment to transform the classroom into an environment of relevant experiences, where technology is integrated with the educational objective and supports the integral growth of the university student.
From an institutional approach, integrating augmented reality into educational processes involves more than just obtaining technology. It is essential to create teacher training programmes focused on digital skills, develop particular educational materials for AR and form strategic partnerships with technology companies. Similarly, it is suggested to conduct pilots in academic fields or programmes, analysing their effectiveness before wider adoption. These measures would facilitate a progressive and continuous educational transition towards more immersive and relevant learning environments.
For future research, it is recommended to extend the analysis to educational contexts with less technological access to assess the applicability of AR in environments with limited infrastructure. It is also necessary to complement the quantitative analysis with mixed methodologies that include qualitative studies on the perception of teachers and students in the teaching–learning process with AR.

Author Contributions

Conceptualisation, M.O.R.-S. and L.G.B.B.; methodology, M.O.R.-S.; software, I.C.G. and J.W.M.L.; validation, M.O.R.-S., L.G.B.B., and A.V.M.G.; formal analysis, M.O.R.-S.; investigation, M.O.R.-S. and L.G.B.B.; resources, R.W.A.-C.; data curation, L.M.C.A. and J.W.M.L.; writing—original draft preparation, M.O.R.-S.; writing—review and editing, M.O.R.-S., I.C.G., and L.G.B.B.; visualisation, A.V.M.G.; supervision, L.G.B.B.; project administration, M.O.R.-S.; funding acquisition, L.G.B.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Approval by a Research Ethics Board (IRB) was not required for this study, in accordance with Article 14.1, letter (f) of the Regulations of the Research Ethics Committee of the Universidad Tecnológica del Perú (CEI-UTP). The research did not involve risks for the participants, no personally identifiable data were collected and no sensitive topics were addressed. The questionnaire was completely anonymous and participation was completely voluntary.

Informed Consent Statement

No formal informed consent was obtained as the questionnaire was completely anonymous and no personally identifiable data were collected. Participation was voluntary and the confidentiality of the data collected was guaranteed at all times.

Data Availability Statement

All data supporting the findings of this study are included in the article. For additional information or access to specific data not found in the manuscript, please contact the corresponding author or any of the co-authors, who will accommodate requests based on availability and established confidentiality policies.

Conflicts of Interest

The authors declare no conflicts of interest.

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Information 16 00372 g001
Figure 1. A flowchart of the operation of the augmented reality application with mobile devices.
Figure 1. A flowchart of the operation of the augmented reality application with mobile devices.
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Information 16 00372 g002
Figure 2. Structural model with relationships between latent variables and their indicators.
Figure 2. Structural model with relationships between latent variables and their indicators.
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Figure 3. Exploring historical busts in the classroom with AR.
Figure 3. Exploring historical busts in the classroom with AR.
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Figure 4. Interactive models of technology and science with AR.
Figure 4. Interactive models of technology and science with AR.
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Table 1. Exogenous and endogenous variables with their indicators in the model.
Table 1. Exogenous and endogenous variables with their indicators in the model.
Type of VariableLatent VariableObserved VariableQuestion (Likert Scale 1–7)
ExogenousUse of Augmented Reality (RAU)RAU1I frequently use augmented reality tools during my classes.
RAU2I believe that augmented reality facilitates the learning of complex concepts.
RAU3Augmented reality tools are intuitive and easy to use.
ExogenousEducational Relevance (RE)RE1The content presented using augmented reality is aligned with the course objectives.
RE2Augmented reality has practical applications in my field of study.
RE3I believe that augmented reality improves the quality of the classes.
Endogenous MediatorEase of Adoption (FA)FA1I find it easy to learn how to use augmented reality tools.
FA2Implementing augmented reality in the classroom does not require much time.
FA3I have the necessary resources to use augmented reality in my learning.
Endogenous MediatorStudent Interaction (IE)IE1Augmented reality encourages collaboration between my classmates and me.
IE2Interaction with my classmates improves thanks to the use of augmented reality.
IE3Activities based on augmented reality promote greater participation in class.
Endogenous MediatorAcademic Motivation (MA)MA1The use of augmented reality increases my interest in the subjects I study.
MA2I feel more motivated to learn when augmented reality is used in class.
MA3I prefer interactive learning methods, such as augmented reality, over traditional ones.
Endogenous DependentPerceived Academic Performance (RAP)RAP1My understanding of concepts improves when I use augmented reality in my studies.
RAP2My academic performance benefits from using augmented reality tools.
RAP3I solve problems more easily when using augmented reality resources.
Endogenous DependentEducational Sustainability (SER)SER1I believe that augmented reality should be implemented permanently in learning.
SER2The educational benefits of augmented reality justify its continued use in institutions.
SER3The implementation of augmented reality is viable in the long term in the educational context.
Table 2. Reliability and construct validity.
Table 2. Reliability and construct validity.
VariableCronbach’s Alpharho_AComposite ReliabilityAverage Variance Extracted (AVE)
FA0.9100.9140.9430.847
IE0.8800.8830.9260.808
MA0.8880.8900.9310.818
RAP0.9090.9100.9430.847
RAU0.8920.8940.9330.823
RE0.8760.8920.9240.803
SER0.9330.9330.9570.882
Table 3. R-squared, adjusted R-squared and effect size (f2).
Table 3. R-squared, adjusted R-squared and effect size (f2).
VariableR SquaredR Squared-Fittedf2 (Effect Size)
FA0.7530.7503.041
IE0.5410.5361.176
MA0.6400.6320.394
RAP0.5220.5171.091
SER0.5320.5271.137
Table 4. Discriminant validity.
Table 4. Discriminant validity.
VariableFAIEMARAPRAURESER
FA0.920
IE0.7750.899
MA0.7060.7850.905
RAP0.8390.7470.7220.920
RAU0.8670.7360.7130.9130.907
RE0.8320.7350.7370.9080.8910.896
SER0.8480.7480.7290.8990.8970.9620.939
Table 5. Path coefficients.
Table 5. Path coefficients.
RelationshipOriginal SampleSample AverageStandard Deviationt-Statisticp Value
FA -> MA0.2430.2470.0892.7340.003
IE -> MA0.5970.5940.0847.0860.001
MA -> RAP0.7220.7210.04416.3510.001
MA -> SER0.7290.7280.04516.2880.001
RAU -> FA0.8670.8680.02831.3270.001
RE -> IE0.7350.7350.04217.4050.001
Table 6. Confidence intervals.
Table 6. Confidence intervals.
RelationshipOriginal Sample (O)Sample Average (M)5.00%95.00%
FA -> MA0.2430.2470.1050.395
IE -> MA0.5970.5940.4540.722
MA -> RAP0.7220.7210.6410.790
MA -> SER0.7290.7280.6450.794
RAU -> FA0.8670.8680.8210.913
RE -> IE0.7350.7350.6630.803
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Rodriguez-Saavedra, M.O.; Barrera Benavides, L.G.; Cuentas Galindo, I.; Campos Ascuña, L.M.; Morales Gonzales, A.V.; Lopez, J.W.M.; Arguedas-Catasi, R.W. Augmented Reality as an Educational Tool: Transforming Teaching in the Digital Age. Information 2025, 16, 372. https://doi.org/10.3390/info16050372

AMA Style

Rodriguez-Saavedra MO, Barrera Benavides LG, Cuentas Galindo I, Campos Ascuña LM, Morales Gonzales AV, Lopez JWM, Arguedas-Catasi RW. Augmented Reality as an Educational Tool: Transforming Teaching in the Digital Age. Information. 2025; 16(5):372. https://doi.org/10.3390/info16050372

Chicago/Turabian Style

Rodriguez-Saavedra, Miluska Odely, Luis Gonzalo Barrera Benavides, Iván Cuentas Galindo, Luis Miguel Campos Ascuña, Antonio Víctor Morales Gonzales, Jiang Wagner Mamani Lopez, and Ruben Washington Arguedas-Catasi. 2025. "Augmented Reality as an Educational Tool: Transforming Teaching in the Digital Age" Information 16, no. 5: 372. https://doi.org/10.3390/info16050372

APA Style

Rodriguez-Saavedra, M. O., Barrera Benavides, L. G., Cuentas Galindo, I., Campos Ascuña, L. M., Morales Gonzales, A. V., Lopez, J. W. M., & Arguedas-Catasi, R. W. (2025). Augmented Reality as an Educational Tool: Transforming Teaching in the Digital Age. Information, 16(5), 372. https://doi.org/10.3390/info16050372

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