Augmented Reality in Education: Current Status and Advancement of the Field

  • Matjaž KljunEmail author
  • Vladimir Geroimenko
  • Klen Čopič Pucihar
Part of the Springer Series on Cultural Computing book series (SSCC)


Despite the substantial body of work and positive reported outcomes of AR (Augmented Reality) usage for education, researchers claim that compared to other digital technologies such as multimedia and web services for teaching and learning, research in AR is still at an early stage, with the majority of studies being short-term, one-time experiments with high variability in the effect size. AR-based applications targeting the educational sector are becoming ever more available with even low-cost smartphones supporting visualization of AR content. Regardless of the fact that the number of downloads of these applications exceeds millions, and assuming they are used in educational settings, we still lack longitudinal reports on how AR affects learning outcomes (compared to other technologies). This chapter looks at the current status of AR in education from different points of view: widely available technologies, types of AR learning experiences for education, capabilities for authoring AR, suitable audiences and topics, and the status of research of AR in education. The chapter summarizes each of these points of view and looks at the possibilities to advance the field.

1.1 Introduction

Asking educators across the globe to develop an Augmented Reality Learning Experience (ARLE) might still illicit a questioning expression on their faces (Tinti-Kane and Vahey 2018). This despite the fact that these technologies have already been vastly explored and analyzed in the contexts of teaching and learning (Bacca et al. 2014; Koutromanos et al. 2015; Lee 2012; Muñoz 2017; Santos et al. 2013a; Weerasinghe et al. 2019; Wu et al. 2013) and a simple Google Scholar search sprung up more than half a million results for the term ‘education augmented reality’.

Through AR, students can develop important practices and literacies that cannot (or are hard to) be developed and enacted in other technology-enhanced learning environments (Squire and Jan 2007). The body of work in this area has revealed several advantages (Santos et al. 2013a) such as (i) availability of desirable naive physics provided by AR affordances (Hornecker 2012), (ii) positive impacts on cognitive load and motivation (Cheng 2017) as well as on spatial abilities (Martín-Gutiérrez et al. 2010), (iii) positive effects on the collaborative experience (Alhumaidan et al. 2018; Gomez 2003) (iv) ease in visualizing complex spatial relationships and abstract concepts (Arvanitis et al. 2009), (v) the possibility to experience phenomena that are hard or not possible to experience in the real world (Klopfer and Squire 2008) and (vi) positive effects on learning outcomes (Weng et al. 2019).

Regardless of the substantial body of work and reported positive outcomes, some researchers maintain that ‘compared to studies of other more mature technologies in education (e.g. multimedia and web-based platforms), research of AR applications in education is in an early stage, and evidence of the effects of AR on teaching and learning appears to be shallow’ (Wu et al. 2013). Other researchers supported this by revealing a high variability of the effect size between different studies (Santos et al. 2013a). The individual studies of AR systems in academic literature also appear to be just one-time, short experiments (Prieto et al. 2014). Muñoz argues that the research community needs to collaborate with educators in order to progress the field (Muñoz 2017, p. 32) and move from these short-term to longitudinal in-the-wild studies that would measure the effects in the long run.

We also have no official data as to how many schools, educators or institutions are using AR as an addition to their lessons or training. One way to infer this might be from the number of downloads of AR-based educational applications in digital distribution platforms such as the Apple App Store and Google Play. Google Expedition, which is one of the most widespread and affordable AR and VR educational applications, providing hundreds of AR 3D models, currently exceeds one million installs in Google Play1 alone. There are several others and even if not focused on the education they might still be used for teaching and learning (see e.g. Bertrand et al. 2018) for the use of mobile applications demonstrating war-affected places for learning empathy).

In order to move away from ‘shallow’ evidence, to study the effects of AR on education in the long run and advance the body of research in the field we need to (i) better understand the needs of the research community on one side and educators on the other, (ii) bring these two communities together and consequently (iii) support longitudinal in-the-wild studies. To better understand the current state of affairs and outline a possible future direction we are going to explore the current status and direction of the developments by looking at the available data from academic literature, advancements in technology and anecdotal evidence.

In the next section, we will first look at the available off-the-shelf technologies, followed by the section about AR Learning Experiences (ARLE). Section four discusses the authoring of AR content, section five looks at suitable audiences and topics, and section six covers research aims and the theoretical basis of ARLE. The chapter will finish with a summary of how all described concepts fit together to advance knowledge in the field.

1.2 Technologies

The AR Software Development Kits (SDK) have been evolving rapidly in the past two decades and have been available to a wider community since the ARtoolkit was released in 19992 (Kato and Billinghurst 1999). Among others, both major players in the mobile platform's market, Apple and Google, have recently released their AR SDKs (ARKit3 and ARCore4 respectively) and paved the way for a plethora of AR applications in their digital distribution platforms.

AR for education has also experienced recent investments from the corporate world. For example, in 2017 Google released the AR Expeditions5 application offering a low-cost AR classroom experience with currently over 100 different study materials. All students need is a phone that supports either ARCore or ARKit and the application installed on their phones (see Fig. 1.1). However, it is also possible to run non-native AR in a web browser, be it on a smartphone, tablet or personal computer with WebAR technologies.6 All Android-based phones from version 6 (currently 74%7 of all Android devices) and all iOS from version 11 (currently over 91%8 of iPhones) support WebAR, which covers a vast array of devices.
Fig. 1.1

An example of Google AR Expeditions AR learning content—The Roman Colosseum

With evermore phones supporting AR SDKs to run native AR applications and phones supporting WebAR in all price range the use of AR in education might become more widespread. The availability of SDKs, WebAR and the omnipresence of smartphones makes them the current AR platform of choice. This is also supported by the literature—the majority of AR applications surveyed in the last decade have been implemented on smartphones (Muñoz 2017, p. 28; Koutromanos et al. 2015; Krevelen and Van Poelman 2010; Pucihar and Kljun 2018; Weerasinghe et al. 2019).

In 2015 Google started testing their VR headsets and Expeditions application in several schools across six U.S. states (California, Illinois, New York, New Jersey, Connecticut, Texas) and later in Brazil, Australia, New Zealand and the UK as a part of the Expeditions Pioneer Program (Protalinski 2015; Pierce 2016). It was estimated that they reached more than two million pupils over a period of two years. Regardless, a 2016 survey by Samsung USA found that among 1000 K-12 educators in the USA only 2% used VR in their classrooms and a 2018 survey of 115 educators revealed that the adoption of VR and AR technologies is hindered by the lack of resources to buy appropriate hardware and software and the lack of training (Tinti-Kane and Vahey 2018). The schools might dive into AR either by providing their own devices (e.g. mobile phones or a projector) or by allowing students to use their own devices (e.g. if a ‘bring your own device’ (BYOD) policy is in place).9

However, phones are just one of the devices capable of delivering AR content. Other off-the-shelf devices that experienced considerable investments from the corporate sector include head-mounted AR displays such as MicrosoftHoloLens10 and Magic Leap.11 Education-wise, these head-mounted displays are targeting the professional market and tertiary students in various fields such as healthcare learners with immersive simulation-based training in ultrasound and anatomy (Healthcare IT Today 2017), medical students (see Fig. 1.2) with an interactive digital human anatomy curriculum (Vassallo et al. 2017), or astronomy enthusiasts with an educational application about the Milky Way (Microsoft 2018). The price range of these is currently tenfold that of the cheapest smartphones supporting AR SDKs. However, it is expected that these technological solutions will become more affordable in the future.
Fig. 1.2

The human anatomy viewed through HoloLens (CWRU 2017)

1.3 AR Learning Experience

In order to frame Augmented Reality Learning Experiences (ARLE) we can look at the related fields of e-learning and m-learning. One of the fundamental concepts in these fields is a learning object (LO) (Friesen 2005). Although definitions of learning objects vary greatly, the understanding in this work is that they are any digital resource that can be (re)used to support learning (Wiley 2000). They can have several components such as content, learning activities and elements of context (Williams 2004; Laverde et al. 2007). Willey (2000) taxonomy divides them into five different groups based on their characteristics:
  • Fundamental: an uncombined individual digital resource (content only).

  • Combined-Closed: a small number of combined but not individually accessible digital resources.

  • Combined-Open: a larger number of combined but also directly accessible digital resources.

  • Generative-Presentation: fundamental and combined digital resources coupled with logic and structure.

  • Generative-Instructional: generative-presentation digital resources coupled with evaluating interactions, created to support the instantiation of abstract instructional strategies (content, context, learning activities and evaluation).

AR content is just a digital resource that can be a part of a learning object also called ARLO (Santos et al. 2013b). In addition to the above taxonomy, we can take into account how ARLOs engage users, which spans from passive consumption of AR content to active creation of AR content represented on the user engagement continuum (Kljun et al. 2019).

An example of a fundamental ARLO is the aforementioned Colosseum example from Fig. 1.1, which is a stand-alone AR model to be explored and manipulated. The Google Expeditions application provides various AR (animated) 3D models that educators can incorporate into their lessons but does not provide any additional content, context, guidance or didactical approaches that could be used with a particular content. A more complex example is AR books that provide individual 3D AR models of the content that is available in the 2D form on pages (Billinghurst 2002; Kljun et al. 2019)—these models are interlinked with the content of the book and cannot be considered purely as fundamental ARLOs. All such AR models usually allow only passive consumption.

An example of a combined ARLO is a guided tour of a city that includes a collection of ordered or unordered (AR and non-AR) resources (Lochrie et al. 2013) one can explore when on a city tour. When we start to incorporate a variety of activities, assessments, services, game elements (such as challenges, points, badges, leader-boards, performance graphs, signs of progress, meaningful stories, avatars, teammates, role-playing, etc.) we move to the generative side of the ARLOs spectrum.

The generative ARLOs representing an activity or lesson are the ones that are usually explored in academic literature (Nilsson et al. 2012). There are also various classifications of generative ARLOs such as the one from Wu and her colleagues (Wu et al. 2013) who divided AR educational applications into role-based (Squire and Jan 2007; Dunleavy et al. 2009; Viinikkala et al. 2014), location-based (Lochrie et al. 2013; Rosenbaum et al. 2007) and task-based (Laine and Suk 2016; Rogers et al. 2015), increasing engagement, contextualization and authenticity respectively. These categories are not exclusive and they often overlap.

The level of complexity of the design and implementation of AR applications rises when we move from fundamental (stand-alone content only) to generative-instructional full-blown ARLOs as shown in Fig. 1.3. The complexity depends on what an ARLO includes such as the complexity of the structure, logic and evaluating elements, the complexity and size of the problem environment (e.g. difficulty of tasks to be solved, the amount of tasks), the amount of fundamental and combined AR resources, the amount of game elements (roles, tasks, badges, scores, etc.), the inclusion of collaboration with co-located students, the amount of location-based elements, the amount of active involvement in the creation of new content, etc.
Fig. 1.3

The continuum on which the complexity of application implementation rises from fundamental to generative AR learning objects while the freedom of incorporating AR content into existing lessons drops

There are advantages and disadvantages of fundamental versus generative AR learning objects when it comes to integrating them into a wider context of the curriculum (see Table 1.1). One of the differences between them is how much freedom is left to educators to build a lesson around an AR learning object since the fundamental content can be easily mixed with the existing teaching materials. Opposite to the level of complexity, the level of freedom drops when we move from fundamental to generative-instructional ARLOs as seen in Fig. 1.3.
Table 1.1

Advantages and disadvantages of fundamental versus generative AR learning objects




Fundamental AR object

– Freedom to incorporate it into existing lessons and mix it with existing materials

– Easy to design, create and implement (e.g. photogrammetry)

– Hard to link AR objects into the wider context of user’s surroundings

Generative AR object

– Ready to be used on its own with the whole activity or lesson covered

– Cover more content

– Less freedom to mix it with existing materials

– Location-based AR objects are not portable

– Hard to design and implement

– Hard to implement changes

The advantage of generative AR learning objects is that educators can take them and use them on their own to cover a specific learning objective. These objects take students more time to finish and cover more content but they also give less freedom to educators to implement one’s changes into the activity or lesson(s). As we will discuss later, the complexity of ARLOs requires software development skills (Bacca et al. 2014) in order to implement changes (if code is even available and open source).

Several AR games are location-based and need to be played at a particular location such as Astrid’s steps (Nilsson et al. 2012) that is played in the Astrid Lindgren’s World theme park in Sweden, which makes it impractical for much of the world. Similar are the majority of outdoor-based AR applications in Weerasinghe et al. (2019) and Muñoz (2017, p. 187), and applications in the chapter ‘Augmented Reality for outdoor environmental education’.

Another matter is how to link AR content to the user’s surroundings. Most of the fundamental AR objects can be viewed anywhere. For example, the Expeditions application allows users to display an animation of an atom over a desk and there is no connection between the atom and the surroundings; the atom is shown on its own and no other object in the surroundings relates to it (similar to the Colosseum in Fig. 1.1). As such the object loses two of the three advantages of AR—contextual visualization and real-world annotation, while it retains vision-haptic visualization (Santos et al. 2013a). If users were capable of linking various AR objects in space that would start to interact (e.g. each user has their own atom shown in AR and they are able to join them into a molecule) then this would give them a context in space (similar to how they might do it with physical models of atoms). An ARLO that takes advantage of users’ location has the potential to be linked into the surroundings such as showing certain AR content only at a certain location that is relevant to it; however, this is also more difficult to the author.

1.4 Authoring AR Content

Based on all the available off-the-shelf technologies presented above, one would assume that authoring AR content is easy. This is currently true for simple fundamental AR content.

To envision the near future of authoring AR content we can take a look at the current state of authoring VR content as AR will likely follow a similar path. After the release of Expeditions in 2015, Google started to build a community around it and invited educators to contribute their own VR (Virtual Reality) learning materials.12 They made readily available different tools such as applications to create photo spheres or 360 panoramas with smartphones13 and Google Poly platform14 that allows users to easily create, share and access panoramas with added multimedia. Educators can, for example, create virtual journeys with multiple linked photospheres and in each of the photospheres, students are able to read additional text explanations and open photos or play videos placed within. There are currently over 900 such readily available VR journeys,15 which could be described as generative-presentation learning objects. However, the Poly platform lacks the capability to incorporate additional learning activities (e.g. questionnaires) and game design elements (e.g. collecting points). It is nevertheless possible to expand Poly journeys if one has software development skills as was for example done for the Škocjan caves educational trail16 visible in Fig. 1.4 (Jesenko et al. 2019).
Fig. 1.4

An example of a gamified Google Poly VR journey. Top left is a map of the trail with all locations of the VR journey; the top centre is the current question, the answer to which is hidden in the panoramic image below; top right is the badges that users collect by answering the questions correctly. Users move from one location to another and try to explore the panorama, answer questions and collect badges

Google recently started to add AR content into their Poly platform and encourages the teaching community to build up the materials as they did with the VR. One way to create fundamental AR is stereophotogrammetry that allows reconstruction of real objects from multiple 2D photos (see Fig. 1.5). This can be achieved with several apps available for smartphones (e.g. SCANN3D17 for Android or Scandy Pro 3D Scanner18 for iOS) as well as with special sensors (e.g. Structure IO19 or MS Kinect) coupled with desktop software (e.g. Skanect).20 The research community is also exploring different ways to support users in this process (e.g. Andersen et al. 2019 or see Bot and Irschick 2019 for a more detailed description of the available software and techniques). Besides Google Poly, other online platforms support uploading, sharing and viewing of 3D models such as Sketchfab.21 These 3D models can be converted into fundamental AR experiences by using one of several WebAR platforms that allow users to experience AR right from the web browser.
Fig. 1.5

Left: a 3D model of a real thatched barn created with stereophotogrammetry. Right: the barn used to create a 3D model

It is thus relatively easy to create simple fundamental AR content. However, the development and implementation of more complex generative ARLOs are still in the domain of software developers Bacca et al. (2014), which is also confirmed by the recent study where the majority of surveyed AR educational applications are ‘exclusively engineered by low-level programmers/designers’ (Muñoz 2017, p. 32). This is not to say that there have been no attempts to formalize the authoring of ARLOs (Santos et al. 2013b) and attempts to create authoring tools for AR learning experiences (Medlock-Walton 2012; Wojciechowski and Cellary 2013; Camba and Contero 2015; Park 2011). Muñoz (2017, p. 34) surveyed over 20 AR authoring applications proposed and observed by the research community. However, the degrees of ease-of-use, accessibility, effectiveness and efficiency vary a lot (Muñoz 2017, p. 34).

Tools to create generative AR learning objects are likely to evolve into more usable products for a wider community. However, the community needs to work towards formalization and standardization of AR learning objects in order to increase discoverability, reusability and interoperability. Since we took a stance that ARLOs are learning objects containing AR content, the community can base their authoring tools on already existing standards developed by the e-learning community (Del Blanco et al. 2013). For example, thexAPI (or Experience API) standard, the successor of SCORM,22 covers novel learning experiences including mobile learning, serious games, simulations and blended learning experiences mixing digital and real objects, and is already used for authoring ARLOs (Rodrigues et al. 2017).

It is worth mentioning that with more user-friendly ARLO-authoring software the inverse relationship between freedom of mixing AR content with existing learning materials and complexity of implementation might disappear. With user-friendly authoring tools supporting xAPI, educators should be capable of adapting existing ARLOs to a variety of subjects and contexts and tailor them to their needs.

1.5 The Audience and Topics

The research of AR in education most often focuses on formal education that includes pre-school, primary, secondary and tertiary levels. Weerasinghe and her colleagues revealed that the majority of 30 studies surveyed were conducted with primary school students (56.6%), followed by high school students (20%) and undergraduate students (6.7%), while one game targeted special education (Weerasinghe et al. 2019). The review made by Muñoz (2017) also revealed that the majority of the applications support primary (26%), followed by lower secondary (13%) and upper secondary education (10%). Interestingly, 20% of surveyed applications were studied with master’s students, while only 5% with undergraduate students and 5% with doctoral students.

The results are very similar to the age groups targeted by the TES community,23 which is currently the biggest open repository of lesson plans for primary and secondary education. Only 3% of the plans that include the Expeditions application are designed for pre-school education, while 10% are targeting early primary, 24% late primary, 29% lower secondary and 22% upper secondary students. The remaining 11% are designed for 16+ students. Muñoz argues that AR’s didactic and experimental nature makes it easier to introduce in elementary education rather than higher education (Muñoz 2017, p. 31).

However, in the survey of 32 studies completed by Bacca et al. (2014) the majority of AR educational apps were targeting undergraduate students (34%) followed by primary school (19%) and lower secondary school students (18%); while none were listed in pre-school, master’s and doctoral categories. This shows how different sets of surveyed AR studies (e.g. focusing on AR games or augmented paper only) produce different results. Nevertheless, all these studies of various ARLOs show that AR is suitable for all age groups in formal education.

Some surveyed applications do not target any particular age group or the age group is not mentioned. For example, the majority of the AR applications used by museums and tourist sites to inform users about art and natural and cultural heritage do not target any specific age group (Pucihar and Kljun 2018). These applications belong to the category of informal learning.

In professional settings, the practicality of AR assistance for industrial maintenance has long been advocated (Lee 2012). In sectors such as military, manufacturing and other industries ‘AR competitively thrives and expands the scope of the technology itself’ (Lee 2012). For example, in a study (Henderson and Feiner 2009) focusing on the military sector, the military mechanical staff could conduct their routine maintenance tasks more safely and conveniently with the assistance of AR. In manufacturing, BMW is known to have been investing in AR and VR for over a decade now. A recent report revealed that they currently use Google Glass Enterprise Edition24 in their Production academy in training sessions for engine assembly units (Green car report 2019). Interestingly, the reports about evaluations of participants' learning success have shown that there are no differences in quality compared to conventional training courses. Moreover, the company developed their own authoring tool and is claiming that setting up new training programmes for other screw joint processes should be quick and easy, which additionally confirms that such authoring tools will also become available for other areas. AR for professional training has also been explored for example in Hořejší (2015), Boulanger (2004), Besbes et al. (2012). However, the majority of AR usage in industry is used as a means to support staff in their complex and technical work environments and not for training—see Fraga-Lamas et al. (2018) and Rankohi and Waugh (2013) for a review and Kothari (2017) for a general overview of Glass usage in the industry.

As with a vast variety of audiences, surveys have also identified a variety of topics that AR learning objects have covered so far. Science was reported as mostly investigated by all the reviews (Bacca et al. 2014; Koutromanos et al. 2015; Muñoz 2017, p. 31; Santos et al. 2013a; Sommerauer and Müller 2018; Weerasinghe et al. 2019) including subjects such as biology, chemistry and physics. Other topics include mathematics, history, language learning, psychology, humanities and arts, agriculture, health, engineering and so on.

1.6 The Status of ARLE Research

Studies of individual AR applications as well as reviews try to classify them into various teaching and learning paradigms, theories, models and activities. Weerasinghe et al. (2019) classified 30 educational AR games and found that they were designed based on several learning models: 43% as challenge-based learning, followed by situated learning (20%) and experiential learning (17%). In addition to these models, educational AR games are also designed upon scaffolding (7%), problem-based (7%), collaborative (3%) and contextual (3%) learning models. A chapter ‘Augmented Reality for outdoor environmental education’ in this book also categorized AR applications for outdoor learning based on whether they support situated learning, place-based learning or experiential learning. In addition, the author emphasizes the prevalence of inquiry-based approach in place-based learning. The results also coincide with a survey of 36 AR applications (Sommerauer and Müller 2018) that classified 78% of these under the constructivism learning paradigm (situated learning, game-based learning and simulations, experiential learning, etc.) followed by 19% classified under the cognitivism (cognitive theory of multimedia learning, embodied cognitive dissonance theory, etc.). Learning theories were also used to explain the effectiveness of AR, in particular multimedia, experiential learning and animate vision learning theory (Muñoz 2017, p. 32).

Fundamental AR Learning Objects (ARLOs) hardly support learning theories on their own. As mentioned in previous sections, with fundamental ARLOs educators have all the freedom to mix ARLOs with other materials and thus have the freedom to base their teaching on a variety of paradigms, theories, models and activities, while for the AR content that already takes part in the application, such as educational games (Klopfer 2008), the models are already predefined.

In Weerasinghe et al. (2019) the authors argue that the reason why challenge-based learning is mostly used in educational AR games may be related to the compensations of gamification and game-based learning. Their results also show that possible game genres for situated learning can be a simulation or role-playing games. And these game genres relate to the practice, immersion and imitation of learning activities. At the same time, it shows that the possible game genres for experiential learning can be adventure or treasure hunt games that relate to the practice and interaction learning activities. Similar results can be found in Rapeepisarn et al. (2008) on the relationship between learning techniques, learning activities and possible game genres, as well as in the already mentioned classification of AR learning software into role-, location- and task-based Wu et al. (2013). The later classification does not focus on games only, but it is clear that the AR software exhibiting role-, location- and task-based activities rely on game elements as well.

Despite the fact that surveyed AR educational applications were based on various extents of learning paradigms and models, the majority of them were exclusively developed by software engineers (Muñoz 2017, p. 32; Bacca et al. 2014). Only in 18% of 30 surveyed applications did the development teams include educators, while none of the applications were exclusively authored by educators (Muñoz 2017, p. 32). The situation is the same for AR applications that are used for informal learning. The survey of 86 AR applications on art and cultural heritage exposed that several of them are just fact giving, do not allow communication and personalization and have poor support for analytical and sensual activities (Pucihar and Kljun 2018). It is safe to conclude that educators have not been involved in the development of these AR experiences.

Notwithstanding the fact that the majority of AR applications have been studied and positive outcomes reported, current evidence appears to be ‘shallow’ (Wu et al. 2013). This claim is also supported by Prieto et al. (2014) that revealed ‘that comparatively few studies provide evidence about the learning effects of system usage, or perform evaluations in authentic setting conditions. The analysis […] also highlights the need for further longitudinal, in-the-wild studies and the existence of design tensions that make the conception, implementation and appropriation of this kind of systems still challenging’. The importance of the inclusion of educators in the design and implementation of AR technologies for learning has been stressed numerous times. For example, Prieto and colleagues argued that inclusion can foster the appropriation in in-the-wild scenarios and allow for longitudinal studies to be carried out (Prieto et al. 2014).

Appropriating ARLOs in educational practice requires an interdisciplinary approach that considers learning theory, pedagogy and instructional design (Santos et al. 2013b). Appropriation of ARLOs by educators is also important since authoring ARLOs needs time and effort, and similarly to other digital and non-digital learning objects, there is only a later advantage of becoming a permanent and re-usable learning resource (Yuen et al. 2011). An interdisciplinary approach is also needed if we would like to produce more in-depth knowledge into the effects of AR on teaching and learning. The research community, including AR researchers as well as researchers in the field of educational research, should work with educators and help in setting up and supporting ARLO environments that could be used for longitudinal studies. For example, simply helping educators to include fundamental ARLOs (e.g. using the Expeditions application or authoring AR content with photogrammetry) into their lessons, would allow the community to study the long-term effects of AR on teaching and learning.

1.7 Conclusion

In order to advance the body of knowledge of long-term AR effects on educational outcomes, the research community should consider the concepts described in this chapter. For each, we tried to look at the current trends, what is available off-the-shelf to educators and what is discussed by the research community. We will summarise each of these below.

Technologies: When planning to design an Augmented Reality Learning Object (ARLO), researchers together with educators need to take into consideration how the content will be presented to students based on what technology is available at hand or what can be procured. Mobile AR on phones has become very accessible in past years with both major mobile OS players investing in their own AR SDKs. Even more, smartphones support WebAR, which enables a plethora of devices to run AR in a browser. However, the adoption of AR technologies relies also on training educators to use these technologies. The research community needs to step in and support educators in familiarizing themselves with the technologies and revealing the possibilities besides discussing possible research plans.

AR learning experiences: The complexity of these spans from simple fundamental objects to be viewed and manipulated on their own to generative-instructional learning objects containing different types of interactions, various game elements (Weerasinghe et al. 2019), the number of tasks to be solved, collaborative interaction, location-based elements, contextually visualized AR content with the surroundings (Santos et al. 2013a), passive or active engagement (Kljun et al. 2018), etc. The complexity of development rises when we move from fundamental to more complex generative ARLEs, while the freedom of using existing learning materials and the ease of adapting the ARLE drops in the same direction. For the appropriation of ARLE by educators, researchers need to discuss their needs and requirements and the desired complexity of ARLE in relation to the existing learning materials and practices. This is especially needed if we would like to study long-term effects immediately. In the long run, we need authoring tools as described below.

Authoring AR content: Simple fundamental AR Learning Objects (ARLOs) are available in various applications for smartphones. It is also easy to create simple 3D models of real objects based on photogrammetry. Developing more complex AR learning experiences requires software development skills (Bacca et al. 2014). The easy-to-use authoring tools are still not available although there have been some attempts in this direction. In order to increase discoverability, reusability and interoperability of ARLOs, the authoring tools should take standardization into account and one possible candidate is Experience API (xAPI). With easy-to-use authoring tools educators will have complete freedom to adapt ARLEs and appropriation is more likely to materialize. Collaboration between researchers, developers and educators is also needed to create usable authoring ARLOs tools based on the previously mentioned needs, requirements and current practices as well as the available resources (programmers, time, money).

The audience and topics: The overview of several reviews have shown that AR has been studied in educational settings on all levels of formal education for all sorts of topics (Bacca et al. 2014; Muñoz 2017; Weerasinghe et al. 2019). AR has also been used in informal education, for example by museums, galleries and tourist sites (Pucihar and Kljun 2018). AR is thus suitable for all types of learning and all age groups, which makes it easy to decide on based on educators’ needs and requirements.

ARLO research: AR is well situated in and explained by learning theories and models. However, the evidence about the learning effects seems to be shallow (Wu et al. 2013). The majority of ARLEs studied by the research community did not have educators involved in the planning and designing process (Muñoz 2017). There is a clear need for researchers developing AR learning experiences to start collaborative research activities with researchers in educational sciences as well as educators in order to support their needs and requirements, which will encourage them to appropriate ARLOs in their lessons.

The importance of collaboration between communities has been stressed by several researchers. Wu and her colleagues also emphasized that ‘the educational value of AR is not solely based on the use of technologies but closely related to how AR is designed, implemented and integrated into formal and informal learning settings’ (Wu et al. 2013). Without the collaboration, the design step might not be optimal and without support, the integration and implementation into the lessons might not happen. Collaboratively discussing all the above-described concepts needs to take place in order to create meaningful ARLEs for all stakeholders involved in the process from the design, development and implementation to longitudinal research. Only if ARLEs are meaningful to educators by supporting their needs, requirements and available technologies will they use them in the long run. Consequently, this will help researchers set research goals and enable them to conduct longitudinal in-depth studies that will give concrete evidence of the effects of AR on teaching and learning.

If we draw the comparison with recent past developments with VR research, there is anecdotal evidence that the Google VR Expeditions was prototyped and tested extensively with teachers and students in schools around the world (Waismann 2016). It is also currently being studied long term in several U.S. primary schools by researchers25 (Cheng et al. 2018). It is safe to assume that AR technologies will follow if we take the right steps.


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    Google. Bring your lessons to life with Expeditions.

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    Create WebXR. Augmented reality on the web.

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    Android developers. Distribution dashboard.

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    Apple store. Support: Apple Developer.

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    Whether bringing the devices to school is acceptable or not and the discussion about possible consequences related to the (prolonged) use of smartphones (either at home or at school) by younger children and teenagers is beyond the scope of this chapter.

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  11. 11.
  12. 12.
  13. 13.

    The panoramas can be viewed with a low cost VR headset Google Cardboard viewer.

  14. 14.

    Google Poly platform to upload, manage and distribute 3D content.

  15. 15.
  16. 16.

    Virtual tour of the Škocjan caves educational trail. YouTube video:

  17. 17.
  18. 18.

    Scandy Pro 3D Scanner application for iOS.

  19. 19.

    Structure IO sensor.

  20. 20.

    Skanect 3D scanning software from Windows and macOS.

  21. 21.

    Sketchfab online library of AR and VR content.

  22. 22.

    SCORM Explained 201: A deeper dive into SCORM.

  23. 23.

    Tes lesson plans including the use of Expeditions application.

  24. 24.

    Google Glass Enterprise Edition:

  25. 25.

    CIRCL. Virtual Reality in Educational Settings.



The authors acknowledge the European Commission for funding the InnoRenew CoE project (Grant Agreement 739574) under the Horizon 2020 Widespread-Teaming program and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund). The research was also supported by the Slovenian research agency ARRS (P1–0383 and J1–9186).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Matjaž Kljun
    • 1
    • 2
    Email author
  • Vladimir Geroimenko
    • 3
  • Klen Čopič Pucihar
    • 1
    • 2
  1. 1.Faculty of Mathematics, Natural Sciences and Information TechnologiesUniversity of PrimorskaKoperSlovenia
  2. 2.Faculty of Information Studies (FIŠ)Novo MestoSlovenia
  3. 3.Faculty of Informatics and Computer ScienceThe British University in EgyptCairoEgypt

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