Introduction

Teachers’ actual and perceived digital competences are very important determinants for the successful integration of digital technologies in teaching and learning (Knezek et al., 2019; Petko et al., 2018; Rubach & Lazarides, 2021). Digital competences are defined as “the confident, critical and responsible use of, and engagement with, digital technologies for learning, at work, and for participation in society” (Directorate-General for Education, 2019, p. 12) and in the context of education they can be understood as the set of abilities to use digital technologies to optimize teaching and learning effectively. International frameworks such as the UNESCO ICT Competency Framework for Teachers (UNESCO, 2018), and the European Framework for the Digital Competence of Educators (DigCompEdu: European Commission, 2017) have highlighted the importance of teachers’ digital competences and research studies examined different areas of teachers’ digital competences (e.g., Perifanou & Economides, 2019; Rubach & Lazarides, 2021; Tzafilkou et al., 2022) showing that they constitute an important factor for the successful integration of digital technologies in educational practice (Mishra & Koehler, 2006; Tondeur et al., 2017). However, despite the large number of studies on teachers’ digital skills and competences, to the best of our knowledge, no study exists that specifically addresses teachers’ competences in using augment reality (AR) in their teaching.

Augmented reality (AR) is a technology which, by incorporating computer-generated virtual information in real environments, can extend/augment users’ reality. AR has many benefits in education: it can provide enhanced interaction opportunities, can enable visualization of invisible elements and abstract concepts, can increase student interest, motivation and engagement (Akçayır & Akçayır, 2017) and enhance leaning achievement favouring long-term knowledge retention (Radu, 2014). However, despite the growing trend of introducing AR learning experiences in education and AR’s positive educational outcomes it is still difficult to be integrated in classroom practices and a main reason for this is the lack of teachers’ AR competences (Tinti-Kane & Vahey, 2018). Usually, teachers lack skills for developing AR applications (Bacca et al., 2014; Belda-Medina & Calvo-Ferrer, 2022; Manuri & Sanna, 2016; Palamar et al., 2021; Tzima et al., 2019; Saforrudin et al., 2011). More specifically, Saforrudin et al. (2011) as well as Bacca et al. (2014) found that most teachers cannot create AR applications. Also, recently, Tzima et al. (2019) observed that most teachers did not use AR applications in teaching and never created any AR application. Similarly, Palamar et al. (2021) discovered that most future teachers (students) had a medium and low level of readiness of using AR in the educational process. Finally, Belda-Medina and Calvo-Ferrer (2022) found that pre-service language teachers lacked knowledge for creating AR applications and integrating them in real classes. Successful implementation of AR in educational practice largely depends on teachers’ AR skills and competences. While there is a variety of existing frameworks that address main dimensions of digital competences (e.g., information and data literacy), latest developments have increased the digital skills requirements for teachers. New and emerging technologies introduced new challenges and opportunities. One of the frameworks that have been recently updated to incorporate new and emerging technologies such as systems driven by Artificial Intelligence, is Version 2.2 of the Digital Competence Framework for Citizens (Vuorikari et al., 2022). However, this framework is applicable to a heterogeneous group of people without focusing on education and it defines general (not AR-specific) digital skills. Augmented Reality introduces new interactions with the physical and virtual worlds (Akçayır & Akçayır, 2017) and enables individuals to accomplish increasingly complex tasks (Vrontis et al., 2021) allowing thus learning spaces to be explored in different ways. Therefore, there is an increasing need to address the required knowledge, skills and attitudes needed by teachers to integrate AR in their teaching. AR competences constitute a specialization of advanced digital competences and digital competences should now encompass AR competences. Despite numerous previous studies on AR in education, there is only one study (Nikou et al., 2022), to the best of our knowledge, that examines and explores the AR competences of teachers. Based on that teachers’ AR competences (TARC) framework, the current study aims to develop a scale for teachers to self-assess their competences with respect to exploiting AR in education. It is important to investigate the AR specific competences that teachers should have in order to be able to offer AR-supported learning experiences to their students. The current study is the first one to develop and validate a scale for the augmented reality competences of teachers.

Towards this goal, the present study has the following research objectives:

  1. (i)

    to develop and validate an instrument (scale) on teachers’ AR competences,

  2. (ii)

    to explore potential differences across the proposed instrument (scale) dimensions among different groups of teachers.

Therefore, the study is aiming to increase the understanding of the teachers’ continuing professional development needs related to AR so that they can effectively integrate AR into their professional practice.

Methods

Instrument

The initial instrument of the study is based on the Teachers’ Augmented Reality Competencies (TARC) framework proposed by Nikou et al. (2022). The framework introduced a questionnaire of fourteen items defining the following three AR competence areas namely Create, Use, and Manage AR learning experiences. The aforementioned competence areas originate from the Educational Technology definition by the Association for Educational Communications and Technology as “the study and ethical practice of facilitating learning and improving performance by creating, using, and managing appropriate technological processes and resources” (Januszewski & Molenda, 2008). Nikou et al. (2022) referred to creation as the capacity to design, develop and modify augmented reality learning experiences; use as the capacity to teach, assess/provide feedback and communicate and collaborate using augmented reality; and management as the capacity to find, classify and evaluate augmented reality learning resources as well as considering ethical, safety, and security issues related to the integration of augmented reality into teaching. After an evaluation of the original questionnaire and consultation with experts in the field of Augmented Reality, the initial three dimensions were adjusted to four and the initial number of fourteen items was reduced to eleven, as Fig. 1 shows. The four dimensions of the modified instrument along with their items are: (i) DDM: Design, Develop and Modify, (ii) PTAF: Pedagogy, Teaching, Assessment and Feedback, (iii) SEO: Search, Evaluate and Organise, and (iv) ES: Ethics and Safety. DDM corresponds to the Creation, PTAF corresponds to Use. Finally, SEO and ES correspond to the Manage competence areas named before. The questionnaire about the AR teachers’ competences can be found in the Appendix. All items were assessed using a 5-point Likert scale ranging from “Strongly disagree (1) to “Strongly agree” (5).

Fig. 1
figure 1

The proposed framework

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Participants and data collection

Once ethical approval was granted by the University [details are omitted for review] ethical committee, researchers distributed the questionnaire internationally through emails, discussion lists, and social media channels during the period from May to June 2022. Participation in the study was voluntary and anonymous. A total of 176 responses were collected via Qualtrics. Several responses have been eliminated (as not being complete) and the final sample was 150 which is considered sufficient for Confirmatory Factor Analysis (Tabachnick & Fidell, 2013). Participants were 49.9% female, 46.6% male, 1.2% preferred to self-describe while 2.4% preferred not to say. Their distribution in terms of their age was, in descending order, 41–50 years old (37.1%), 51–60 years old (26.3%), 31–40 years old (17.2%), 21–30 years old (11.3%), 61–70 years old (5.4%), and other/prefer not to say (2.7%).

Participants were from UK (17.5%), Greece (16.2%), Malta (12.3%), USA (7.2%), India (5.5%), Portugal (3.8%), Canada (2.5%), Australia (2.5%), Malaysia (2.1%), and a number of 37 other countries with lower participation as well (30.4%). In terms of their general digital skills level, 9.7% of the participants had basic digital skills (use of a basic range of software such as office; and devices such as computer, tablet), 43% had intermediate digital skills (use of a big variety of software such as Screencastify, Audacity; and devices such as smart interactive whiteboards), and 47.3% had advanced digital skills (use of highly innovative and complex digital and communication technologies such as programming, software development, network management). Most of the participants were teaching in tertiary education (45.7%) with the secondary education (28.5%) and primary education (10.7%) to follow, while 15% of the participants described the education level they were teaching as other. Their teaching experience in years was varied with 23.1% to have 16–20 years teaching experience, 15.6% with 21–25 years, 15.6% with 6–10 years, 12.4% with 1–5 years, and with the ranges of 11–15, 31–35, and 26–30 to be similar namely 10.1%, 10.2%, and 9.1% respectively. The distribution of the participants in terms of the subject they were teaching was Informatics/Engineering/Technology (50%), Science (Math, Physics, Chemistry, Biology) (19.3%), Languages/Literature (6.7%), Economics/Social Sciences (4.8%), Arts (3.2%) and other (12.9%). Finally, regarding the previous use of AR in class, 36% replied that they had used AR in class before while 64% said they had not. The respondents’ socio-demographic and teaching related characteristics are presented in Tables 1 and 2 respectively.

Table 1 Participants socio-demographic characteristics (N = 150)
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Table 2 Participants teaching related characteristics (N = 150)
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Data analysis

Data were analysed using IBM SPSS v.28 and IBM Amos v.5. We used a Confirmatory Factor Analysis (CFA) to estimate the structure of teachers’ AR competences (MacKenzie et al., 2011). To ensure the suitability of the data for the factor analysis we conducted a principal components analysis estimating the Bartlett’s Test of Sphericity to be statistically significant (Byrne, 2010). For the normality of the data, we used the skewness and kurtosis values to be not higher than 3 and not higher than 8 respectively (Hair et al., 2010). To ensure the quality of the model, we have verified all criteria for convergent and discriminant validity. For convergent validity, all factor loadings on their relative construct to exceed 0.70, composite reliability of each construct to exceed 0.70 and all average variance extracted (AVE) values to be greater than 0.50 exceeding the variance due to measurement error for that construct. Discriminant validity should also be supported with the square root of the average variance extracted (AVE) of a construct is higher than any correlation with another construct (Hair et al., 2010).

For the CFA we followed the guidelines and recommendations provided in Brown (2015) for the acceptable fit values for the standard fit criteria, namely: the incremental fit index (IFI), the Tucker-Lewis index (TLI), the comparative fit index (CFI) and the goodness-of-fit index (GFI) should be greater than or equal to 0.90, the fraction chi-squared (χ2)/degrees of freedom (df) should be less than 3.00 and the root mean square error of approximation (RMSEA) should be less than or equal to 0.08.

Finally, to identify any significant differences among the teachers’ groups (according to their (i) gender, (ii) age, (iii) teaching level, (iv) teaching subject/discipline, (v) teaching experience, (vi) general digital skills, and (vii) previous use of AR in the class) across the various components of the proposed instrument we conducted Kruskal–Wallis non-parametric tests that do not require any distributional assumption.

Results

Assumptions of CFA

Principal components analysis conducted on all items of the instrument indicated that the Kaiser–Meyer–Olkin value was 0.934 and that Bartlett’s Test of Sphericity was statistically significant (p < 0.5). Most of the item coefficients in the correlation matrix were 0.3 and above indicating that the data was suitable for factor analysis (Byrne, 2010; Hair et al., 2010). Skewness values for all items were not higher than 3 and kurtosis values for all items were not higher than 8 indicating that data is considered to be normally distributed (Byrne, 2010; Hair et al., 2010; Kline, 2005), as Table 3 depicts.

Table 3 Descriptive statistics of the measurement items
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Construct validity and reliability

To ensure the quality of the model, we have verified the internal consistency, convergent and discriminant validity of the proposed research model, as depicted in Table 4. The criteria of Cronbach’s alpha (α), composite reliability (CR) and average variance extracted (AVE) have been considered to ensure the construct validity and reliability of the model (Bagozzi & Yi, 1988; Fornell et al., 1981; Gefen et al., 2000; Hair et al., 2010). All Cronbach’s α values are greater than 0.7 demonstrating internal consistency (Hair et al., 2010). All calculated Composite Reliability (CR) values are greater than 0.70 indicating satisfactory construct reliability (Gefen et al., 2000). All factor loadings from the four factors are greater than 0.70 indicating that all items are loaded sufficiently on the corresponding factors ensuring high convergent validity as suggested by Hair et al. (2010). Average variance extracted (AVE) values range between 0.619 (ES) to 0.820 (PTAF) which are all above 0.5 indicating high convergent reliability as suggested by Hair et al. (2010).

Table 4 Results for convergent validity for the measurement model (acceptable threshold values in parentheses)
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Discriminant validity is also supported since the square root of the average variance extracted (AVE) of a construct is higher than any correlation with another construct (Table 5). Thus, both convergent and discriminant validity for the proposed research model are verified (Hair et al., 2010).

Table 5 Discriminant validity for the measurement model (values in bold: the square root of the average variance extracted for each construct)
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After the reliability and validity analysis, we performed a confirmatory factor analysis (CFA) to test the hypothesized factor structure as derived from the theoretical model proposed.

Confirmatory factor analysis

Our model has been evaluated against the aforementioned fit indices and the values were as follows. The χ2 was 69.757, with degrees of freedom (df) = 36 and p-value < 0.001, which were significant. The ratio χ2/df = 1.93 is below three, indicating a good fit (Kline, 2016). Moreover, the following fit indices have been identified: CFI = 0.983, TLI = 0.973, IFI = 0.983, GFI = 0.922, and RMSEA = 0.079). The CFI, TLI and IFI values are considered excellent fit for the model as being ≥ 0.95 (West et al., 2012). The Goodness of Fit Index (GFI) is very close to 1 which is considered a perfect fit (Tanaka & Huba, 1985). The RMSEA value found is considered to be acceptable as being between 0.05 and 0.08 (MacCallum et al., 1996). In summary, all aforementioned values of CFI, TLI, IFI, GFI and RMSEA indicate an acceptable fit for the model (Brown, 2015; Hu & Bentler, 1999). Therefore, the CFA results demonstrated a relatively adequate model fit.

Teacher differences across the TARC components

With respect to the second research objective, the study examined the differences in teachers; self-reported AR competences in different groups according to (i) gender, (ii) age, (iii) teaching level, (iv) teaching subject/discipline, (v) teaching experience, (vi) general digital skills, and (vii) previous use of AR in the class. Table 6 shows the results on all teachers’ groups across the four components of TARC instrument.

Table 6 Kruskal Wallis tests on teachers’ groups across the four components of TARC instrument competence scale
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The results did not reveal any gender and age differences in the teachers’ self-reported level of AR competences. Teaching level showed significant differences only for the DDM dimension of the instrument (p < 0.05) with teachers in higher education to self-report better skills in creating AR resources. Teaching experience revealed significant correlations (p < 0.05) with the factors of SEO and ES. There is a tendency for positive correlation between teaching experience in years and competence to manage AR resources including ethical and safety aspects. Teaching subject showed significant correlations in all components of the instruments, especially for the DDM, PTAF, and SEO factors (p < 0.001) following by ES (p < 0.05) in favour of the Informatics/Engineering/Technology followed by Science (Maths, Physics, Chemistry, Biology), and Languages/Literature teaching disciplines. General digital skills and previous use of AR in class also showed significant differences (p < 0.001) in all components of the instrument as well, as it would normally be expected.

Discussions and conclusion

The current study developed and evaluated a self-reporting instrument (scale) for teachers’ AR competences and investigated various factors contributing to the various areas of the instrument. Teachers’ competences to integrate AR in their teaching is an area that has not received much attention in the literature so far. Therefore, the current study aims to shed some light on the set of competences that teachers need to master in order to effectively integrate AR into their teaching enhancing the learning experience offered to students. The first research objective was to develop and validate an instrument on teachers’ AR competences and the second was to explore, among different groups of teachers, potential differences across the proposed instrument dimensions.

Regarding the first research objective, the main components of the proposed instrument are: (i) Design, Develop and Modify (DDM), (ii) Pedagogy, Teaching, Assessment and Feedback (PTAF), (iii) Search, Evaluate and Organise (SEO), and (iv) Ethics and Safety (ES). Confirmatory analysis successfully confirmed the proposed instrument on a sample of 150 teachers from 46 countries with different demographic characteristics, teaching disciplines and experience. The instrument’s reliability and validity were also confirmed since all reliability related measurements exceeded the minimum value of 0.7 and all items have been sufficiently loaded on the corresponding factors. The proposed modelling of teachers’ competences in AR, elaborating mainly in the Creation (DDM), Use (PTAF), and Management (SEO and ES) dimensions of AR specific digital skills, reflects existing validated digital competence scales. The proposed AR competences suggest an AR-oriented approach for the “digital resources” and “teaching and learning” areas of the European Framework for the Digital Competence of Educators (European Commission, 2017). Similarly, our DDM dimension is in line with the “designing, developing and delivering digital content” dimension of the Digital Competency Framework for Digital Teaching and Learning proposed by Lameras and Moumoutzis (2021). The same holds for the “digital content creation” and “safety and security” dimensions of the instrument developed by Rubach and Lazarides (2021) to measure teachers’ basic ICT competence beliefs. Similarly, our proposed instrument further elaborates on the dimensions of the Students’ Digital Competence Scale (SDiCoS) by Tzafilkou et al. (2022) focusing on AR.

The descriptive statistics presented in Table 3 indicate that teachers’ AR competences are at low to medium levels across most components. The lowest mean scores were for the “creation” component” and the medium mean scores were for the “use” component. The mean of the total AR competences score was at a medium level (2.99/5.00). This is in line with previous research reporting intermediate levels of teachers’ digital competences (Cabero-Almenara et al., 2021) or even teachers’ inefficiencies in digital skills (e.g., Fernandez-Batanero et al., 2020; Ottenbreit-Leftwich et al., 2018).

Regarding the second research objective, the results did not reveal any gender differences in the teachers’ self-reported level of AR competences. Results on gender differences in teachers’ perceived digital skills reported from previous studies are rather contradictory (Esteve-Mon et al., 2020). While studies exist providing evidence for significant gender differences in perception (European Institute for Gender Equality, 2021; Grande-de-Prado et al., 2020; Tzafilkou et al., 2016), other studies reported no significant differences in digital skills in terms of gender (e.g., Badiozaman et al., 2021; Ertl et al., 2020; European Commission, 2018; Tzafilkou et al., 2022). Our study agrees with the latter, showing that digital skills between the genders in the teaching profession are roughly equal. Teacher training institutions foster both men’s and women’s digital upskilling and therefore both genders are capable of integrating technology in their classes (OECD, 2018).

The results did not reveal age differences in the teachers’ self-reported level of AR competences. Related literature again reports contradictory evidence regarding age differences in digital skills. Age is usually negatively corelated to teachers’ digital skills (Garzon et al., 2020; Saikkonen, & Kaarakainen, 2021) with the older teachers to seem less likely to use ICT in their classrooms (Scherer et al., 2015) while the intermediate age groups to self-report higher competence levels in the pedagogical use of technologies (Rodríguez-García et al., 2019). Our findings, aligning with other studies (Benali et al., 2018; Tondeur et al., 2021) revealed no significant age difference. While younger teachers would normally be expected to be more competent in using digital technologies (Tzafilkou et al., 2023), our findings suggest that digital skills among age groups do not seem to differ significantly. This is encouraging from the perspective of eliminating the gap in the use of digital technologies for teaching among age groups. One reason for this might be that teachers’ engagement in delivering online teaching during the pandemic accelerated their digital competencies and their dispositions towards digital education post-pandemic (González et al., 2023; Myyry et al., 2022). Again, the results suggest that teacher training programs should be focused to genders and age groups as well (Sánchez-Cruzado et al., 2021).

Teaching level did not show any significant differences for all dimensions of the instrument but the DDM level (p < 0.05). Educators employed in higher education institutions self-reported higher competence level for designing, developing and modifying AR resources, while for the areas of using and managing AR no significant differences were reported. This result can further support the evidence provided by other studies reporting that better digital skills are often associated to higher education (Kaarakainen et al., 2018). The specific result would normally be expected considering the fact that Informatics/Engineering/Technology following by Science were the main teaching disciplines among participants. Higher education science and engineering staff members are more likely to design immersive resources which when coupled with appropriate instructional principles can be used for teaching specific types of learning content (Merchant et al., 2014).

Teaching experience revealed significant correlations (p < 0.05) with the factors of SEO and ES. While no significant difference found for the factors of DDM and PTAF, teachers with longer teaching experience can better manage AR resources being more confident to search, evaluate, organise and most importantly to handle ethical and safety related issues for their students. Similarly, studies have found that teachers with more years of experience in using technologies in teaching report higher scores of digital competences (Ghomi & Redecker, 2019). In contrast, other studies have reported that teachers with longer teaching experience demonstrate lower levels of digital skills (Lucas et al., 2021), but this may be due to the fact that more teaching experience is usually related to older teachers and therefore more reluctance in using digital technologies.

Teaching subject showed significant correlations in all components of the instrument, especially for the DDM, PTAF and SEO factors (p < 0.001) following by the ES factor (p < 0.05) in favour of the Informatics/Engineering/Technology teachers followed by Science (Maths, Physics, Chemistry, Biology), and Languages/Literature teachers. Teaching subject is an important contextual factor that affects teachers’ perceptions and use of digital technologies (Hennessy et al., 2005). Not many studies exist exploring digital skills across subjects (Schmid et al., 2021). Our study is in line with previous findings suggesting that teaching subject indicates significant differences across various professional and pedagogical components (Tzafilkou et al., 2023). Existing evidence suggests that Science, Technology, Engineering, and Mathematics (STEM) teachers value more the importance of integrating ICT in education and they usually benefit more from it; therefore, they are more positive towards using it (Siddiq et al., 2016) and more competent in their use (Altun & Akyıldız, 2017). This is in agreement with our findings revealing that STEM teachers self-reported more competence in creating, using, and managing AR resources.

General digital skills and previous use of AR in class showed significant differences (p < 0.001) in all components of the instrument as well. Teachers who have higher general digital skills and teachers who have already used AR in their classrooms self-reported higher level of AR competences. This result agrees with previous findings indicating that previous use of digital technology in teaching is a predictor of teachers’ digital competence (Lucas et al., 2021).

Implications

The proposed TARC instrument is specifically focused on the AR competences that teachers need to have in order to appropriately integrate AR in educational practice. It could be used by teachers, educational organizations, educational policy makers, software companies, and other researchers.

The proposed instrument addresses a methodological and theoretical gap in the current literature (Nikou et al., 2022): the development and validation of an instrument that can evaluate teachers’ competences in AR. This work is a step forward in establishing a basic understanding of the AR competencies that are important for teaching and learning providing a theoretical basis for other studies exploring teachers’ digital competencies in AR. Moreover, researchers would replicate this study with different samples. They would also be inspired by the TARC scale and develop new scales. Furthermore, the study has implications on instructional practices and professional development of teachers.

Recent studies (e.g., Fernandez-Batanero et al., 2020; Ottenbreit-Leftwich et al., 2018) highlighted teachers’ inefficiencies in digital skills and a lack of teachers’ training (e.g., Perez-Navio et al., 2021; Yoon, 2022). Using our proposed instrument for self-assessing their AR competences, teachers could become aware of their strengths and weaknesses and reflect with regard to AR in teaching. After identifying their competences’ levels, they may also take appropriate training in order to develop their AR competence and integrate AR in their teaching practice. For example, teachers may need knowledge and skills with regard to developing AR-based educational applications suitable for their specific teaching subjects. It seems that STEM teachers may be more competent in creating, using, and managing AR resources. Moreover, STEM academic staff are more confident and capable in designing and developing AR resources. Also, teachers with high digital skills may also have high AR competences. These teachers would help their colleagues in selecting, using and even creating appropriate AR applications for their specific teaching subjects.

Educational organizations could use the scale for hiring new teachers with AR expertise as well as for training in-service teachers. They can also design, develop, and implement staff professional development programs on the topics of the TARC scale; provide incentives to their teaching staff in order to improve their AR competences; measure the progress of the AR competences of their teaching staff and the integration of AR in the teaching; and more. Based on the TARC scale, universities could design appropriate programs and courses for teaching pre-service (students) teachers on the integration of AR in education.

Educational policy makers could decide on the areas and levels of introducing and adopting AR in education as well as implementing teachers’ professional development programs on integrating AR in education.

Software companies could develop educational AR software at the various dimensions of the TARC scale that will empower teachers. Researchers would replicate this study with different sample. They would also be inspired by the TARC scale and develop new scales.

Limitations and future directions

The present study has some limitations that need to be addressed in future research. First, the sample was not representative of teachers in all countries, subjects/disciplines, education level etc. Future research could replicate this study by considering larger and differentiated sample with respect to teachers’ countries, general digital skills, teaching level, teaching subject, previous AR use in class, and more.

Second, respondents were only teachers who were reached via our online survey dissemination efforts and voluntarily answered the questions. Probably, these teachers were interested in AR. Future research could recruit teachers via other methods and persuade them to answer the questionnaire.

Third, the survey instrument collected only the teachers’ perceptions with regard to AR in education. Future research could measure the actual teachers’ performance in completing specific tasks, creating e-portfolios, solving problems, etc. across the TARC dimensions. Future research also can extent the Ethics and Safety subscale to be tested with three items at least.

Fourth, the current study investigated the teachers AR competences at a specific time. Future longitudinal studies could investigate the evolution of teachers’ AR competences over time.

Fifth, this study examined any differences in teachers’ AR competences with respect to gender, age, teaching level, teaching experience, teaching subject, general digital skills and previous use of AR. Future researcher could investigate other factors (e.g., personality) that may also affect teachers’ AR competence.

Appendix

See Table 7.

Table 7 Teachers’ Augmented Reality Competences (TARC) scale
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