Augmented reality in developing students’ understanding of chemistry triplet: a systematic literature review

1 Introduction

Chemistry is a subject that students usually find very complex. This is due to the triple nature of chemical phenomena (Johnstone, 1991; Sjöström et al., 2020), and teaching theoretical concepts has always been difficult for educators (Wilsson & Bernie, 1999). Therefore, chemistry is a subject that requires learners to bridge different levels of chemical phenomena to explain them (Gilbert & Treagust, 2009; Sjöström et al., 2020). Chemistry should be taught with laboratory work that enables students to view only macroscopic level of chemical phenomena (Gurung & Gurung, 2023). One possible way to overcome this problem is through the use of technology (Winkelmann et al., 2017), that can strengthen students’ motivation and self-directed and collaborative learning (Bosco et al., 2019). De Jong et al. (2013) and Kusumaningdyah et al. (2023) suggest that the virtual environment and multimedia models can provide important information and support the association of unobservable processes with symbolic representations and can thus contribute to a better chemistry understanding (Mei-Hung et al., 2018).

This systematic review examines the use of augmented reality (AR) in chemistry education and its impact on student learning, knowledge acquisition, attitudes towards technology, etc. The educational value of AR has already been confirmed, but this technology may also have its limitations. It should be noted that the use of AR does not necessarily lead to better outcomes for all students in all educational settings. From a pedagogical point of view, it is not always obvious how to integrate AR technology and educational content. Therefore, in this paper we will explore the use of AR technology in chemistry education and its impact on different variables. In addition, the effectiveness of AR on students’ understanding of chemical triplet in different setting was investigated. Some similar reviews on the use of AR in chemistry education have already been done, for example by Mazzuco et al. (2022) and Romainor et al. (2022). Both studies found positive feedback on student motivation, interest and attitude when using AR in educational environments. Mazzuco et al. (2022) also found that the benefits of AR learning support were most prevalent in the articles. Both systematic reviews reported some difficulties in using AR in the chemistry classroom. Their number was low, probably due to researchers’ focus on the advantages of the technology rather than its disadvantage as noted by Mazzuco et al. (2022). Some reviews, such as that by Garzon et al. (2019), focus on the use of AR in education and the one from Chan et al. (2021) focused on technology use in education in general. The review by Garzon et al. (2019) is also a meta-analysis comprising 64 research papers. It found a medium effect of AR use on student learning gains, with AR interventions being more effective when used in informal learning settings and at undergraduate educational level. However, these reviews were based on literature that is at least four years old and do not provide up to date knowledge as the AR field is still developing. Mazzuco et al. (2022) found that the AR field is developing very rapidly and is becoming increasingly important. The review by Mazzuco et al. (2022) found a significant increase in studies on AR in the last three years, which proves that a lot of new information is being added to the field of AR research. Therefore, a systematic review of current literature is needed to add more new data to the growing body of literature on AR in chemistry education. Furthermore, there is no systematic review examining the performance of AR in learning and understanding of chemistry triplet one of the most important fields of chemistry education (Johnstone, 1991), in different learning settings. There is also need to shed more light on the difficulties of using AR technology in the chemistry classroom as previous studies have focused more on the benefits of the technology and less on the problems associated with its use.

2 Chemistry triplet

Chemical concepts such as electron, bond, structures, molecules etc. are beyond our senses. Therefore, students have little or no experience in building such concepts (Johnstone, 1991). Conceptual understanding is achieved when a person is able to represent and translate chemical problems using macroscopic, microscopic, and symbolic forms of representation (Gabel & Bunce, 1994; Sjöström et al., 2020). Claims and data in chemistry are usually based on one form of representation. However, Ramadhani et al. (2023) found that argumentation and representational ability are intertwined, which shows the need for specific representational dimensions. Due to the complex nature of chemistry, Johnstone (1991) proposed a model of thinking that addresses the microscopic, submicroscopic, and symbolic level of thinking. These multiple levels are represented in Johnstone’s triangle, with the different levels of thinking represented in the corners of the triangle (Figure 1a).

Figure 1:

The three representational levels in chemistry (Johnstone, 1991) – and model representing interdependence of three levels of science concepts – ITLS model – b (Devetak et al., 2009).

Johnstone’s model has been adapted by various scientists by adding certain elements that represent different dimensions of learning chemical concepts. Ferk Savec and Vrtačnik (2007) added visualization elements to Johnstone’s triangle. This addressed the connection of the explanation of experimental observations with particle level explanations. Devetak et al. (2009) adapted Johnstone’s triangle to the STRP model (Figure 1b). This model incorporates Johnstone’s triangle with connections between different levels that enable learning and the formation of a mental model of the chemical phenomenon. The correct connection between all three levels ensures a scientifically correct mental model of a chemical phenomenon.

3 Augmented reality

Augmented reality (AR) can be described as an emerging technology with great possibilities for its use in teaching (Akçayir & Akçayir, 2017). AR is a technology in which the view of the real-world environment is augmented by computer-generated elements (Milgram et al., 1995), it overlays the user’s view of the real world with a layer of computer-generated elements (Rabbi & Ullah, 2013), which are linked to the real-world using sensors and allow users to interact and manipulate with them (Ardiny & Khanmirza, 2018). Azuma (1997) defines AR as a variant of virtual environments and continues that AR is a technology that has three main requirements: (1) it combines real and virtual content, (2) it is interactive in real time and (3) it is registered in 3D.

Research on AR in education is developing rapidly (Santos et al., 2014), especially in the field of chemistry, AR has attracted great interest, mainly because it allows students to visualise chemical phenomena that are not visible to the human eye (Mazzuco et al., 2022). The integration of AR into chemistry teaching and learning is shown on Figure 2. Many AR applications have been developed for learning at all educational levels (Bacca et al., 2019; Nazar, et al., 2024). These applications have been shown to positively influence the affective and emotional aspects of students (Romainor et al., 2022). In the affective domain, AR technology had positive effects on motivation (Chen & Liao, 2014), interest (Zhang et al., 2020), attitude (Ewais & De Troyer, 2019), satisfaction (Cai et al., 2014), etc. In the cognitive domain, AR technology has positive effects on student performance (Badilla-Quintana et al., 2020) and knowledge retention (Abdinejad et al., 2021). However, there are still some challenges in the use of AR technology in chemistry education that need to be overcome or improved (Alzahrani, 2020; Rabbi & Ullah, 2013; Sirakaya & Sirakaya, 2020), with the main problems being marker recognition (Mazzuco et al., 2022), system crashes (Alzahrani, 2020), connectivity issues, and teachers’ resistance to AR application in the chemistry classroom (Sirakaya & Sirakaya, 2020).

Figure 2:

Teaching and learning chemistry model with integrated AR (adapted from Devetak & Glažar, 2014).

4 Research problem and research questions

The goal of this systematic review is to provide an overview of specific issues related to the use of AR technology in chemistry education, more specifically when implementing chemistry triplet in the chemistry learning process. The main aim in answering the RQ1 was to identify the main purpose of the reviewed papers. RQ2 aimed to investigate which variables were tested and which instruments were most commonly used for measuring a particular variable. RQ3 aimed to determine main problems teachers face when using AR technology in chemistry classroom. In addition, RQ4 focused on the positive impact of AR on students’ understanding of the chemistry triplet. The aim was to see if any of reviewed papers addressed the problem of the chemistry triplet, which is very important area of chemistry education (Johnstone, 1991). No previous review focused on testing the effectiveness of the AR in different learning settings. Finally, RQ5 identified the advantages and disadvantages of using this technology in the chemistry classroom.

A literature review was conducted to answer questions about the use of AR in the chemical education. For this purpose, five research questions (RQs) were established:

5 Methods

The Preferred Reporting Items for Systematic Reviews and meta-analyses (PRISMA) approach (Moher et al., 2009; Page et al., 2021) was followed in conducting the systematic review of the published literature. The PRISMA literature search focuses on transparent, replicable, and scientifically adequate systematic reviews. Systematic review of the literature in the field of education, for that matter, consists of collecting and analysing data from multiple studies to answer the research question and identify gaps in this area of research, trends for future work, and what are best conclusions of the studies to implement into the practical teaching activities in the school environment.

Therefore, research questions were defined and information sources, search strategy, exclusion and inclusion criteria, data extraction and analysis were used.

5.2 Study selection

Additional parameters were established for the inclusion of a certain paper. The parameters were used as exclusion and inclusion criteria in both databases, which are shown in Table 2 and Table 3.

Table 2:

Inclusion criteria.

Inclusion criteria
IC1: The paper about application of AR in the process of chemistry teaching and learning.
IC2: The paper includes empirical research.

Table 3:

Exclusion criteria.

Exclusion criteria
EC1: The paper was not published between the year 2018 and 2023.
EC2: The paper is not written in English language.
EC3: The paper is a conference proceeding, book chapters, project report, etc.
EC4: The study is secondary, i.e., it is a scientific review, meta-analysis, or theoretical work.
EC5: The paper has not been peer reviewed.
EC6: The study is listed in another database.
EC7: Research is qualitative.
EC8: The full text of the paper is not available.

Published full text peer reviewed papers were selected in four phases (Figure 3). In each phase, papers were excluded from the review based on exclusion criteria. First, a literature search was conducted in the WoS and Scopus databases. 495 papers from 2018 to 2023 were identified according to the research parameters in the database search (WoS = 219 papers, Scopus = 276 papers). After article identification, titles and abstracts were evaluated in the second stage of selection. Third, duplicates were removed from both database searches, and finally, full-text evaluation was performed in the fourth phase.

Figure 3:

PRISMA flowchart of the systematic literature review.

Table 4 lists the papers reviewed in this study. As can be seen from the table, AR was mostly used to facilitate laboratory work and to represent structures.

Table 4:

Summary of included studies in this review.

Author	Year	Title of the article
Seng Gan et al.	2018	Augmented reality experimentation on oxygen gas generation from hydrogen peroxide and bleach reaction.
Abd Majid & Abd Majid	2018	Augmented reality to promote guided discovery learning for STEM learning.
Thien Wan et al.	2018	Augmented reality technology for year 10 chemistry class: can the students learn better?
Franco-Mariscal	2018	Discovering the chemical elements in food.
Saidin et al.	2019	Framework for developing a mobile augmented reality for learning chemical bonds.
Ewais & De Troyer	2019	A usability and acceptance evaluation of the use of augmented reality for learning atoms and molecules reaction by primary school female students in Palestine.
Zhang et al.	2020	An augmented reality-based multimedia environment for experimental education.
Cen et al.	2020	Augmented immersive reality (AIR) for improved learning performance: a Quantitative evaluation.
Badilla-Quintana et al.	2020	Augmented reality as a sustainable technology to improve academic achievement in students with and without special educational needs.
Chun Lam et al.	2020	Interactive augmented reality with natural action for chemistry experiment learning.
Chen & Liu	2020	Using augmented reality to experiment with elements in a chemistry course.
Habig	2020	Who can benefit from augmented reality in chemistry? Sex differences in solving stereochemistry problems using augmented reality.
An et al.	2020	Usability testing and the development of an augmented reality application for laboratory learning.
Xiao et al.	2020	Multimodal interaction design and application in augmented reality for chemical experiment.
Estudante & Dietrich	2020	Using augmented reality to stimulate students and diffuse escape game activities to larger audiences.
Kailer Aw et al.	2020	Interacting with three-dimensional molecular structures using an augmented reality mobile app.
Abdinejad et al.	2021b	Students perceptions using augmented reality and 3D visualization technologies in chemistry education.
Tarng et al.	2021	A virtual experiment for learning the principle of daniell cell based on augmented reality.
Keller et al.	2021	Cognitive load implications for augmented reality supported chemistry learning.
Lu et al.	2021	Supporting flipped and gamified learning with augmented reality in higher education.
Wong et al.	2021	Using augmented reality as a powerful and innovative technology to increase enthusiasm and enhance student learning in higher education chemistry courses.
Ling et al.	2021	Which types of learners are suitable for augmented reality? A fuzzy set analysis of learning outcomes configurations from the perspective of individual differences.
Cortes Rodriguez et al.	2021	A web site for chemistry and structural biology education through interactive augmented reality out of the box in commodity devices.
Tsai et al.	2021	Design and validation of a virtual chemical laboratory – an example of natural science in elementary education.
Abdinejad et al.	2021a	Developing a simple and cost-effective markerless augmented reality tool for chemistry education.
Montalbo	2021	eS2MART teaching and learning material in chemistry: Enhancing spatial skills thru augmented reality technology.
Alrige et al.	2021	MicroWorld: An augmented-reality arabian app to learn atomic space.
Vui Ket & Osman	2021	CHEMBOND3D e-module effectiveness in enhancing students’ knowledge of chemical bonding concept and visual-spatial skills.
Elford et al.	2022	Fostering motivation toward chemistry through augmented reality educational escape activities. a self-determination theory approach.
Cheng et al.	2022	Hands-on interaction in the augmented reality (AR) chemistry laboratories enhances the learning effects of low-achieving students: a Pilot study.
Liu et al.	2022	Effects of an augmented reality-vased chemistry experiential application on student knowledge gains, learning motivation, and technology perception.
Uriarte-Portillo et al.	2022	Comparison of using an augmented reality learning tool at home and in a classroom regarding motivation and learning outcomes.
Tarng et al.	2022	Application of augmented reality for learning material structures and chemical equilibrium in high school chemistry.
Dominguez Alfaro et al.	2022	Mobile augmented reality laboratory for learning acid-base titration.
Nguk Lau et al.	2022	Prototype of a transition metal visualization app for the learning of stereochemistry in a general chemistry course: Initial findings and reflections.
Sern Low et al.	2022	Assessing the impact of augmented reality application on students’ learning motivation in chemical engineering.
Camara Olim et al.	2022	Periodic fable discovery: An augmented reality serious game to introduce and motivate young children towards chemistry.
Diaz et al.	2023	Incorporating augmented reality tools into an educational pilot plant of chemical engineering.
Peeters et al.	2023	Does augmented reality help to understand chemical phenomena during hands-on experiments? - Implications for cognitive load and learning.
Syskowski & Huwer	2023	A combination of real-world experiments and augmented reality when learning about the states of wax – an eye-tracking study.
Gao et al.	2023	Designing interactive augmented reality application for student’s directed learning of continuous distillation process.
Krug & Huwer	2023	Safety in the laboratory – an exit game lab rally in chemistry education.
Uriarte-Portillo et al.	2023	Higher immersive profiles improve learning outcomes in augmented reality learning environments.
Ahmed & Lataifeh	2023	Impact and analysis of a collaborative augmented reality educational environment.
Yamtinah et al.	2023	Augmented reality learning media based on tetrahedral chemical representation: How effective in learning process?
Coduto et al.	2023	Visualizing 3D objects in analytical chemistry.

Figure 4 lists the journals in which papers were published. As can be seen, the majority of the articles was published in Journal of chemical Education.

Figure 4:

Journals in which reviewed articles were published.

6 Results and discussion

Results and discussion are presented following the research questions.

The first research questions deal with the main objectives of the research presented in the reviewed papers. The primary studies in this review had many different objectives in which the measured variables are reflected, so some similar objectives were grouped together. For example, technology acceptance and attitudes toward technology were identified as participants’ attitudes toward AR, the effects of AR on students’ intrinsic motivation and the effects of AR on students’ motivation were identified as the effects of AR on students’ motivation. Also learning effectiveness, learning performance, learning achievement, better understanding, effect of learning process, etc. were classified as learning effectiveness. It is important to note that the majority of the analysed papers focused on more than one objective. These papers were grouped into several categories depending on which objectives were addressed.

Table 5 shows that 12 different objectives were identified with a total of 72 occurrences in 46 papers studied. Some objectives are addressed in more than one paper, such as identifying the impact of AR on learning effectiveness (21 papers) or analysing the attitudes towards AR (15 papers). In addition, most articles address more than one objective, such as the work of Elford et al. (2022); Liu et al. (2022), and Montalbo (2021).

Impact of AR on learning effectiveness and attitude toward AR and AR app testing were included in nearly one-third of the articles. These three goals were followed by impact of AR on learning motivation and its impact on cognitive load. The objectives represented more than once were effect on students’ spatial skills, effect on different types of students, and effect on students’ attitudes toward chemistry. The remaining 4 objectives (Table 5) were mentioned only once in the articles studied.

The reason so many analysed papers focus on the learning effectiveness of AR may be because this technology can provide important information about understanding of unobservable processes in chemistry, as noted by De Jong et al. (2013). AR has attracted much interest in chemistry education because it can help students visualise chemical phenomena (Mazzuco et al., 2022), and to determine how effective this technology is for visualisation, learning effectiveness must be tested. Chan et al. (2021) found that most articles focused on comparing media, the second most common purpose was an evaluative study, and the third purpose was to examine students’ performance when using virtual labs. However, looking at the reviewed papers for the purposes of this systematic review, it can be determined that 18 papers are comparative studies, such as Elford et al. (2022); Liu et al. (2022), and Nguk Lau et al. (2022), which compared student outcomes in learning with AR and traditional learning, and Peeters et al. (2023) which compared different types of technologies in laboratory work. In this paper, comparative studies would account for 25.3 % of the objectives published in analysed papers. Chan et al. (2021) found that the second most common objective was to assess student interaction with technology. These results mirror the findings of this paper. The least common objective according to Chan et al. (2021), is designing the app and its implementation. This paper cannot confirm these results because papers that don’t include empirical study were excluded. Chiu (2021) identified effect on motivation, academic achievement, learning experience, and attitudes toward chemical content as the most common objectives in his systematic review. These findings are consistent with the results of the present work as these categories were also identified in this systematic review.

The second research question aims to determine which instruments are mostly used for data collection. In the primary studies considered in this paper, many different instruments were used to collect data. Therefore, the instruments were grouped together for better presentation of the results. For example, achievement tests, tests of content understanding, knowledge assessment tests etc. were grouped into one category. The instruments used in the reviewed papers are listed in Table 6.

Table 6 shows that five different types of instruments were used to collect data in the reviewed papers. The predominant method is the questionnaire, followed by tests and surveys. Interviews and observations are rarely used, probably due to the fact that qualitative studies were excluded from this review. Questionnaires are most often used to determine student technology acceptance. However, it should be noted that the category of technology acceptance includes questionnaires on students’ attitudes toward AR, usability, usefulness intention to use, and ease of use. Because no systematic review of the instruments used in AR was conducted, it is difficult to compare these results with previous studies. However, all papers included in this systematic review present quantitative studies, and the literature on quantitative research recognizes questionnaires as the primary means of collecting quantitative data (Mohajan, 2021), as they can be inexpensive, and not time consuming (Roopa & Rani, 2012). Also due to the time factor, the questionnaires use multiple-choice questions. Among the tests, achievement tests are the most popular in the reviewed articles. One explanation for the high number of achievement tests in reviewed articles could be that the reviewed articles mostly focus on the learning effectiveness of AR. Romainor et al. (2022) also noted that learning effectiveness is the focus of AR research in education. Therefore, many papers use achievement tests to assess student knowledge. It is important to note that achievement tests are often used as pre-tests and post-tests (pre-post research design) in the reviewed articles such as Liu et al. (2022) and Tsai et al. (2021), to compare students’ knowledge before and after using AR technology and so the effectiveness of AR on students’ knowledge. Some studies in research articles such as Badilla-Quintana et al. (2020) and Chen and Liu (2020) also used delayed post-tests to test how long-lasting the knowledge is when the AR technology is used.

The third research question examined the main problems when using this technology? Following problems were highlighted in the reviewed papers: (1) problems with scanning AR cards used as markers; AR devices had problems recognising markers when the light was not right; (2) connection stability that occurred when more participants used the app; one article also reported devices overheating, causing the system to crash; (3) slow execution of the app; (4) combination of AR technology with educational theory, meaning that educators had problems with applicability; (5) some of the apps had limited functions that did not allow the AR technology to reach its full potential and (6) sometimes the app was only developed for specific operating system, and students using smartphones with other operating system could not set up the application. Problems mentioned only once in the reviewed papers were: small screen size, problems with certain presentations, difficulty setting up, lack of phones and the amount of memory needed to present the animations.

Previous reviews of AR studies by Alzahrani (2020); Mazzuco et al. (2022) and Sirakaya and Sirakaya (2020) also reported issues with marker recognition as the main problem with AR use and exposed light as the main factor contributing to this problem. Mazzuco et al. (2022) also mentioned overheating issues leading to system crashing and simplification of visualisation. Alzahrani (2020) also reported poor connectivity issues in his study. Alzahrani (2020) and Sirakaya and Sirakaya (2020) identified teacher’s resistance to the introduction of this type of technology as one of the main problems when trying to introduce this technology in the classroom. No such problems were reported in this review, but it is important to mention that in the article by Diaz et al. (2023) teachers expressed concerns about the use of this technology due to lack of training.

The fourth research question examined the use of AR in chemical education in different learning settings to improve students’ understanding of the chemistry triplet. To answer this research question, only 13 papers in which all three levels of representation were incorporated were further analysed to see if students improved their understanding of the chemistry triplet. However, none of these papers tested students’ understanding of the chemistry triplet but focused more on the understanding of the content in general. Studies such as that of Camara Olim et al. (2022) mainly focused on the usability of the app, students’ motivation to learn the content, such as that of Liu et al. (2022), students’ level of cognitive load, such as that of Peeters et al. (2023), etc. Although the understanding of chemical phenomena depends on the understanding of the chemical triplet (Gabel & Bunce, 1994) studies on learning effectiveness, such as those by Cen et al. (2020); Tarng et al. (2022) etc., tend to focus more on content understanding, rather than specifically on the understanding of the chemistry triplet that represents a specific content. Therefore, the answer to this research question is inconclusive, due to the fact that it is impossible to determine the effect of AR to students’ understanding the chemistry triplet. However, when looking at the results of these studies, in which three levels of chemical triplet were used, showed no statistically significant differences in students’ knowledge when using AR. These were the studies by Peeters et al. (2023) and Tarng et al. (2021). The main difference between these studies and the studies in which statistically significant differences were found is that Peeters et al. (2023) compared the effectiveness of AR use with filmstrips animation and animations while Tarng et al. (2021) compared a real laboratory experiment with a virtual one. Studies that compared teaching with AR to traditional teaching methods all found statistically significant differences in students’ knowledge before and after the AR intervention. These results could be explained by the fact that laboratory work has already been recognized as an effective way of teaching chemistry (Gurung & Gurung, 2023) and that these results also suggest that further research needs to be conducted in the field of AR to investigate the effects of AR on students’ understanding of the chemistry triplet.

The last research question aims to identify the main conclusions, about the use of AR in chemistry teaching and learning, presented in the reviewed papers. Results are presented in the Table 7.

Table 7 shows that students have s good attitude towards the use of AR. These results are consistent with the findings of an earlier study by Mazzuco et al. (2022), who also found a positive attitude of students towards AR technology. It is also important to note that there were no differences in attitudes towards AR technology between males and females (Habig, 2020). The conclusions of the articles in this review are consistent with those of Garzon et al. (2019), who also found that the lack of technological training of teachers could be a problem for the implementation of AR in the learning process. The results on student learning outcomes are consistent with the results of the meta-analysis by Hsin-Yi et al. (2022), in which he found that AR showed a medium to large effect on average on improving students’ knowledge and skills compared to traditional teaching methods. However, the findings of Dominguez Alfaro et al. (2022) and Elford et al. (2022) differ from these results. This could be explained by the small sample size and the inquiry-based learning method, which is suitable for students when provided with appropriate tools and additional guidance (Orosz et al., 2023). The findings on the usefulness of AR for low-achieving students were also observed in the systematic review by Garzon et al. (2019). The findings on student motivation are consistent with those of Hsin-Yi et al. (2022); Mazzuco et al. (2022), and Romainor et al. (2022). In the study by Tarng et al. (2022), the different results could be explained by the different duration of the activities (Hsin-Yi et al., 2022).

The results in the articles included in this review are inconsistent regarding the effects of AR on students’ cognitive load. In a previous review article, Mazzuco et al. (2022) also reported cognitive overload when using AR technology and stated that an efficient app is necessary to reduce cognitive load. Problems with cognitive load are likely due to the complex tasks and moving 3D objects, as found by Coduto et al. (2023). In the papers reviewed, AR technology was considered easy to use, which was also noted in the systematic review by Wang-Kin (2021) systematic review. However, some studies reported technical difficulties and teacher resistance was reported by Garzon et al. (2019).

7 Conclusions

The purpose of this study was to find out how AR technology was used in chemistry teaching and learning and how it effects students’ understanding of chemistry triplet. To this end, 495 articles from 2018 to 2023 were retrieved from Web of Science and Scopus. From this group of studies, 46 articles were selected for the final phase of the study.

First, the aim of the studies was analysed. There was a wide range of aims on which the studies focused. Most AR studies focused on learning effectiveness and attitudes toward AR. App usability and simplicity of use are important factors for both of these aspects, so testing the app is also an objective of interest in this research area. Due to the novelty of this technology, the papers also focus on students’ learning motivation, which is influenced by the duration of the activity and the app usability. Students’ cognitive load is also influenced by the usability of the app, so some papers also focus on this objective. Depending on the objectives, studies need to measure certain variables. Therefore, instruments used were also interest of research in this study. The results show that the articles examined mainly use questionnaires on technology acceptance and knowledge tests. To facilitate data collection and analysis, multiple-choice questions are mostly used in the achievement tests and Likert-type scales in the questionnaires. Interviews and surveys are rarely used, probably because they are time consuming. The main conclusions of the articles show that AR technology has a positive impact on learning effectiveness. However, the learning content and the duration of the activity influence the learning effectiveness of AR technology. In addition, AR can strengthen students’ motivation to learn. This could be due to the positive attitude of students and teachers towards AR. AR apps were rated as useful and easy to use by students and teachers. The results regarding students’ cognitive load were inconsistent with some studies reporting an increase and others a decrease in students’ cognitive load when using AR technology. However, the articles also reported some difficulties in using AR technology, such as problems with marker recognition, connection stability, system crashes, app applicability, slow app execution, inconsistency with some operating systems, etc. An important conclusion of this systematic review is that no study has yet been conducted on the impact of AR on students’ understanding of chemical triplet, which means that further research is needed to investigate students’ understanding of the different levels of representation of certain chemical phenomena and the connection and transition between them when the learning process is supported by AR technology.

Further research is needed to test AR technology with different chemical content, and in different educational settings in the chemistry classroom, from live to online courses. It is important to further develop AR apps to make them more user-friendly, useful and accessible to all. In this case, better learning effectiveness, lower cognitive load, better attitude towards AR and higher motivation to learn would be achieved. Future research should therefore focus on investigating the impact of AR on students’ understanding of the chemistry triplet, as mentioned earlier in the discussion.