The effect of virtual reality applications on the development of productive language skills: a meta-analysis

Abstract

Introduction:

The current literature on Virtual Reality (VR) reports promising findings in teaching productive language skills; however, important gaps remain. Evaluating these differences and drawing general conclusions across different conditions will inform future studies examining the impact of VR on the improvement of productive language skills. Therefore, this research aims to comprehensively examine the effects of VR-supported interventions on productive language skills through a meta-analysis.

Methods:

This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Experimental and quasi-experimental studies published between 2015 and 2025 were included in the scope.

Results:

A summary of 21 studies involving 2,503 participants showed that VR applications have a moderately positive and significant effect on productive language skills (g = 0.538). This result means that VR interventions significantly support language learners’ productive language skills. The moderator analysis showed that the moderators’ language type, target language, target language skills, learner educational level, intervention duration, and intervention setting have no significant effect. However, the “control treatment” moderator was statistically significant.

Discussion:

As a conclusion, the research suggests that VR significantly affects the development of productive language skills through interactive, context-sensitive environments.

1 Introduction

For Technology is developing rapidly, and new technologies are being adopted worldwide with increasing ease (Pradana et al., 2022; United Nations Conference on Trade and Development, 2020). They constantly affect society, the economy, health, and education (Barreto, 2018; Rintaningrum, 2019). Technology is widely regarded as an effective tool to support the learning process (Ahmadi, 2017; Ahmani, 2019) and is increasingly prevalent, becoming a global trend (Chen M. P. et al., 2020; Chen Y. et al., 2020; Zhang and Yu, 2021; Zou et al., 2019). In this age, when our lives are increasingly mediated by technology, the use of educational technology tools has become a critical part of the learning process both inside and outside the classroom (Khodabandeh, 2022). Technological resources that expand opportunities for collaboration, create virtual environments, and enhance student autonomy benefit teachers and researchers (Bolgün and McCaw, 2019; Su and Zou, 2020).

Virtual reality (VR) uses a 3d environment combining real and virtual components (Peterson, 2006), where individuals feel immersed in an environment without time and space limitations (Chang et al., 2020; Dickey, 2003; Lee and Wong, 2014; Lorenzo et al., 2016). People use machines and other devices to interact with others in virtual environments (Hay, 1997; Rene, 2018). VR, which can be easily integrated into education settings with its enormous potential (Howard, 2017; Jensen and Konradsen, 2018; Lloyd et al., 2017; Loup et al., 2016; Matsangidou et al., 2017; Parmaxi, 2020; Yang et al., 2010), offers the chance to improve students’ learning by providing a realistic learning experience and addressing the problem of a lack of authentic learning context and social availability (Chang et al., 2018). As a new technology, VR has been used as a teaching tool for language learners over the last decade (Ahn et al., 2022; Chen, 2016). Several benefits of using VR in language learning have been demonstrated, including providing visual support, increasing interest in learning, and offering authentic learning opportunities (Shadiev and Yang, 2020) by providing a sense of “being here” within the real-life interactions and settings (Yeh et al., 2021). At this point, the pedagogical uniqueness of VR lies in its capacity to directly shape cognitive processes through multisensory inputs, real-time interaction, and contextualized task designs (Petersen et al., 2022; Makransky and Mayer, 2022; Radianti et al., 2020; Wei et al., 2025). In particular, the richness of contextual clues and the level of user-centered interaction increase the active participation of the learner by supporting meaning-making processes (Bak et al., 2025; Dalgarno and Lee, 2010; Mizuho et al., 2024; Parmaxi, 2020; Petersen and Makransky, 2024; Uygun and Girgin, 2022). In this respect, VR environments offer a holistic learning environment that encourages experience and action-based learning rather than passive knowledge transfer.

One of the primary structures driving VR-based learning studies is the sense of presence, which is the “illusion of being there even when you know for certain that you are not” (Lin and Lan, 2015; Slater, 2018), and users experiencing a sense of presence can improve their learning performance (Gruber and Kaplan-Rakowski, 2020; Witmer and Singer, 1998). While not necessarily causally related to learning (Klippel et al., 2019), this sense of presence has the potential to improve task performance (Lee et al., 2010), support the interpretation of information (Li et al., 2002), positively influence student attitudes (Katz and Halpern, 2015), and reinforce real learning experiences (Cheng and Tsai, 2019). In language learning contexts, modern technologies have been reported to increase student motivation and foster positive attitudes toward language learning (Li et al., 2019). VR, for example, overcomes the limitations of traditional media by providing language learners with realistic, simulated language-learning environments (Chen B. et al., 2022; Chen Y. et al., 2022) and increases students’ intrinsic motivation through a greater sense of belonging (Huang et al., 2021; Makransky and Lilleholt, 2018a,b). Furthermore, data showing that VR use reduces language learners’ anxiety is noteworthy (Lin and Wang, 2021). VR-supported activities have the potential to develop students’ language skills both holistically and individually by enlivening teaching-learning contexts (Morélot et al., 2021). On the other hand, the relationship between sense of presence and cognitive load is also noteworthy. Appropriately designed VR experiences have been reported to help learners direct their limited cognitive resources toward learning goals by reducing external cognitive load (Albus et al., 2021; Tang et al., 2022; Wen et al., 2024; Zhang et al., 2017). This has been shown in various studies that VR environments are associated with increased performance, especially in tasks that require interaction and real-time language production (Kaplan-Rakowski and Gruber, 2023a,b; Gu, 2025; Sudiana and Santosa, 2024; Makransky and Lilleholt, 2018a,b). In addition, the instant feedback and multi-sensory stimuli offered by VR in immersive environments support the development of self-regulation skills by increasing learners’ error awareness (Chimbo et al., 2026; Li et al., 2024; Pedrosa et al., 2023; Radianti et al., 2020; Wang et al., 2025). In this context, the sense of presence can be considered as a multidimensional construct that is not limited to a motivational element and produces direct and indirect effects on learning outcomes (Slater and Sanchez-Vives, 2016). For this reason, it is thought that addressing the mediating and regulatory role of sense of presence in VR-based language teaching research within the framework of holistic models will provide more explanatory and theoretically grounded contributions to the literature.

The language learning process is the acquisition of language skills in a productive way. To do this, learners must develop both receptive (listening and reading) and productive (speaking and writing) language skills. Speaking and writing fundamentally involve the effective communication of factual ideas and needs (Marcu, 2020). Writing and speaking are important components of language proficiency and are critical for students’ academic success, social interaction, and career preparation (Luo, 2014; Tse and Hui, 2016; Wang, 2017). Students experience higher levels of speaking anxiety in the absence of authentic language environments; this creates a cycle of avoidance and hesitation, leading to stagnation in speaking skills (Dizon, 2020). Similarly, students perceive the writing process as a complex and daunting task (Li and Chu, 2018). It is emphasized that most students experience anxiety about participating in writing activities, and that learning to write remains a low-interest, low-participation area for many students (Abdel Latif, 2019; Clark, 2005; Mahfoodh, 2017). Asif (2017) stated that anxiety and reluctance in language learners affect the language learning process and language performance. At this point, VR offers promising opportunities for language learning and the development of productive language skills by providing new opportunities for more dynamic and authentic learning experiences in language classrooms (Chen, 2016; Lan, 2014, 2015; Lan et al., 2015; Schwienhorst, 2002). Indeed, research shows that the use of VR in the development of students’ productive language skills reduces cognitive load and anxiety (Barrett et al., 2020; Ebadi and Ebadijalal, 2022; Xie et al., 2019a,b), motivates students (Xie et al., 2019a,b), and increases their willingness to communicate and fluency in speech (Ebadi and Ebadijalal, 2022; Lin et al., 2023). These findings suggest that pedagogical mechanisms such as providing safe testing environments, providing instant feedback, and creating realistic interaction contexts can play a decisive role in VR’s support of productive language skills (Akay and Kessler, 2024; Chen B. et al., 2022; Chen Y. et al., 2022; Ozgun and Sadik, 2023; Sun and Song, 2025; Radianti et al., 2020).

Although there are promising findings in the literature on the use of VR in language learning environments, significant gaps remain (Dooly et al., 2023). Indeed, when the results of experimental VR research are examined, notable differences among these studies are evident. Some studies have shown that VR use supports language learning environments (Alfadil, 2020; Chien et al., 2020; Huang et al., 2020; Lan et al., 2016; Liaw, 2019; Madini and Alshaikhi, 2017; Nóbrega and Rozenfeld, 2019; Tai et al., 2022; Papin and Kaplan-Rakowski, 2022; Tai and Chen, 2021; Xie et al., 2019a,b; Xu et al., 2011; Wu et al., 2020; Yang et al., 2021), some studies have shown that VR-assisted interventions have no effect (Cheng et al., 2017; Dolgunsöz et al., 2018), and some studies have shown that individuals learning with traditional methods perform relatively better compared to those who use VR interventions (Derakhshan et al., 2024; Shi et al., 2024). These differing results in the literature raise questions about the impact of VR use. On the other hand, the fact that the research was conducted at different educational levels prevents obtaining clear conclusions about the generalizability of the results (Acar and Cavas, 2020; Alemi and Khatoony, 2020; Chen et al., 2023; Lan et al., 2019; Chen and Hwang, 2022; Nóbrega and Rozenfeld, 2019; Tai et al., 2022; Uygun and Girgin, 2022; Chen M. P. et al., 2020; Chen Y. et al., 2020). However, the fact that meta-analysis studies on VR use in language learning processes in the literature focus on general language learning processes (Chen B. et al., 2022; Chen Y. et al., 2022; Qiu et al., 2024) continues to require detailed information about the development of productive language skills, which are an important part of the language learning process. Indeed, Parmaxi (2020) identified a clear need for more quantitative studies to understand the impact of VR use. The current research examines the effects of VR use on productive language skills to inform their development. In this context, the research aims to examine the effect of virtual reality applications on the development of learners’ productive language skills through meta-analysis.

2 Literature review

2.1 Potential moderators

VR-assisted speech and writing skills development activities have been examined from various perspectives, and numerous studies have been conducted in this regard. However, the findings from these studies are not homogeneous and point to different trends. Indeed, while some studies emphasize that VR-assisted speech and writing skills development significantly contributes to these skills (Lee, 2025; Cao and Luo, 2025; Chen et al., 2023; Feng and Ng, 2023; Mubarok et al., 2024; Zhao et al., 2024), other studies report that individuals learning through other methods perform relatively better than those learning with VR interventions (Derakhshan et al., 2024; Shi et al., 2024). Furthermore, the varying effect sizes reported in each study highlight the diversity of the findings. Research shows that the target audience ranges from elementary school students to university students, and this diversity leads to variations in effect sizes and in the basic research results (Lee, 2025; Chen B. et al., 2022; Chen Y. et al., 2022; Huang et al., 2020; Huang et al., 2021; Lan et al., 2019; Shen et al., 2025; Yang et al., 2021; Zhao et al., 2024). However, variables such as target language, language type (L1/L2), targeted language skill, education level, control intervention (VR-Multimedia, VR-Traditional, etc.), intervention duration (short, medium, and long), and intervention environment (classroom/out-of-classroom) are addressed differently in research, increasing the heterogeneity of the results and making different effect sizes visible (Cao and Luo, 2025; Chen B. et al., 2022; Chen Y. et al., 2022; Chen et al., 2023; Davis et al., 2020; Ebadi and Ebadijalal, 2022; Ebadijalal and Yousofi, 2024; Feng and Ng, 2023; Huang et al., 2020; Huang et al., 2021; Hung et al., 2024; Khodabandeh, 2022; Lan et al., 2019; Mubarok et al., 2024; Shen et al., 2025; Shi et al., 2024; Ou Yang et al., 2020; Zhao et al., 2024). This diversity in research findings on VR-assisted speech and writing activities reveals that the effect sizes are not homogeneous; therefore, it is necessary to consider variable s such as “language type, target language, target language skills, learner educational level, control treatment, intervention duration, intervention setting” as moderators.

2.2 Current study

3 Method

This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The identification, screening, eligibility, and inclusion stages were conducted in line with the PRISMA framework to ensure transparency, replicability, and methodological rigor. This meta-analysis research used Comprehensive Meta-Analysis Software (version 2.2.064) for the analysis. The research process for collecting, evaluating, and reporting the empirical evidence included in the meta-analysis. This process was carried out in 5 steps. (1) Identifying studies to be included in the meta-analysis, (2) reviewing the identified studies and eliminating those that did not meet the defined criteria, (3) developing a codebook based on the conditions and characteristics of the remaining studies, (4) calculating the overall effect size for each condition, and (5) testing the effects of potential moderators under these conditions. Detailed explanations of these steps are provided below.

3.1 Search and retrieval of studies

In this meta-analysis, we searched the Web of Science Core Collection (WOS), Scopus, and ERIC databases. We selected these three databases because they contain large datasets (Chang et al., 2022) and relatively high-quality academic literature (Zhu et al., 2024). Another important factor is that they have been examined in numerous meta-analyses (Mohsen et al., 2024; Tsai and Tsai, 2018). Focusing on these databases was intended to enhance methodological rigor by including only peer-reviewed and internationally indexed studies. However, the use of indexed databases alone may have led to the exclusion of gray literature; therefore, this issue has been considered a potential limitation. Considering the rapid development of VR technologies and the increase in content quality in recent years, we limited the search to the last 10 years (2015–2025). After determining the year range, we selected keywords for the search. The keywords we identified were: (VR OR virtual reality) AND (“productive language skills” OR “speaking” OR “speaking ability” OR “speaking performance” OR “speaking proficiency” OR “speaking achievement” OR “speaking learning” OR “speaking skill” OR “oral ability” OR “oral skill” OR “oral communication ability” OR “talk” OR “interaction” OR “communicative ability” OR “writing” OR “writing ability” OR “writing proficiency” OR “writing performance” OR “writing achievement” OR “writing learning” OR “writing skill”). In addition to this search, we also examined the reference lists of the studies we identified. The meta-analysis was conducted independently by two researchers. At the end of this process, the data obtained were compared and a single pool was created. Subsequently, the inclusion and exclusion criteria were defined.

3.2 Study eligibility: inclusion and exclusion criteria

We searched electronic databases using specified keywords and identified 172 studies. We combined the results from each database search into a single list. We identified duplicate studies in this list. Furthermore, we observed that some studies employed a qualitative approach to data interpretation. We removed these studies, leaving 119. We then reviewed the titles and abstracts of these 119 studies and determined that the conditions in some studies were not suitable for our research. We also found that some studies did not involve VR-supported interventions, and others did not focus on productive language skills. We removed these studies from the list and excluded them from the analysis. This left 52 studies. We examined the full texts of the remaining studies in the context of our selection criteria. Thirty-three of these studies were removed from the list for reasons such as the absence of experimental and control groups, VR intervention in both groups, and insufficient data to calculate effect size. In addition, we reviewed the bibliographies of the included studies and included two studies in the analysis. This process was carried out jointly by two researchers. Each researcher conducted the searches separately, compiled lists, and then discussed the final lists to reach a consensus on the studies included in the analysis. Thus, we conducted a meta-analysis of 21 studies. The steps of the search procedure are shown in the flowchart in Figure 1.

Figure 1

3.3 General coding procedure

The studies were coded independently by two researchers, who met regularly to compare coding results. They discussed and agreed on any differences that emerged during this process. If necessary, the studies were reviewed again to ensure the reliability of the coding. The researchers reviewed all codes obtained from the coding, and 100% agreement was achieved among them in the final coding. In this meta-analysis, the moderators “language type, target language, target language skills, learner educational level, control treatment, intervention duration, intervention setting” were considered. The moderators in question were checked for suitability with the literature and determined by the researchers. The language type moderator was grouped into native language (L1) and second language (L2). Because the studies included in the meta-analysis focused only on English and Chinese, only these two languages were considered in the target-language moderator. Although many VR studies focusing on other languages (Li and Yang, 2020; Thrasher, 2022) were identified in the search, they did not meet the criteria for the current study. Since the study focused on productive language skills, only speaking and writing were included as target language skills in the moderator. We grouped the students’ education levels as kindergarten, primary school, lower secondary school, upper secondary school, and university. However, because no studies were conducted at the kindergarten level, this category was removed. We divided the control treatment moderator into two categories: studies that used any media tool and studies that employed traditional methods. We grouped the intervention duration moderator into three categories. When determining the categories, we adopted a balanced distribution across short-, medium-, and long-term interventions. Finally, we grouped the intervention setting moderator into in-class and out-of-class categories. A significant portion of the work was carried out in a classroom setting. Out-of-class environments, such as online environments and laboratories, were grouped under a single heading.

3.4 Conducting the meta-analysis

In the present meta-analysis, Hedges’ g was used to determine the effect of VR interventions on the development of productive language skills. Hedges’ g can be interpreted as the difference between two group means, which are adjusted for the overall standard deviation, and is more suitable for small samples (Cooper, 2010, pp. 163–168). Hedges’ g was preferred because Cohen’s d value can yield biased results in studies with small samples. In the meta-analysis, we calculated the overall effect size to assess the magnitude of the difference between groups. We also aimed to compare the mean effects of study groups with different conditions using moderator analysis. In addition, we determined the presence and magnitude of heterogeneity among the studies included in the meta-analysis using a heterogeneity test. This test examines whether the observed variance in effect sizes differs from the expected variance due to sampling error (Cooper, 2010, p. 185). Homogeneity of effect sizes is violated when Q is significant, and I2 exceeds 75. Therefore, a random effects model and moderator analyses were preferred (Borenstein et al., 2009). We used multiple outcome measures for some studies included in the meta-analysis. In total, we obtained 30 effect sizes for 21 articles. In some studies, multiple effect sizes were reported (e.g., speaking and writing outcomes). In such cases, effect sizes were treated as separate comparisons to preserve the multidimensional nature of productive language skills. To minimize potential dependency bias, only effect sizes derived from different outcome measures and skill domains were included. Additionally, sensitivity analyses were conducted to determine whether any single study exerted a disproportionate influence on the overall results. This approach is consistent with prior meta-analyses in the field of educational technology (Chang et al., 2022; Wu et al., 2020). Finally, we conducted moderator analyses to determine whether the combined effect size of VR interventions on productive language skills differed significantly among the moderators: “language type, target language, target language skills, learner educational level, control treatment, intervention duration, intervention setting”.

4 Results

The overall effect size and heterogeneity test results of virtual reality applications on productive language skills are shown in Table 1. The combined effect size estimate for the use of virtual reality applications in the development of productive language skills is 0.538 (95% CI [355, 0.721], p < 0.05). Therefore, it can be said that virtual reality-supported instruction has a positive and significant effect on the development of speaking and writing skills, which are productive language skills. The I2 value was estimated at 79.465% based on the analysis. This result indicates substantial heterogeneity among the studies. Therefore, the effect of virtual reality applications may vary depending on the characteristics of the relevant research. The results of the moderator analysis conducted in the study are presented in Table 2, while the forest plot illustrating the effect sizes is shown in Figure 2.

Figure 2

4.1 Moderator analysis

Given the high degree of heterogeneity observed in the initial analysis, moderator analyses were conducted to determine whether moderator variables could account for the observed variance in the estimates. The results are presented in Table 1.

Seven moderator variables were used to examine the potential effects of VR-assisted instruction on the development of productive language skills. The findings for these moderators are as follows:

4.1.1 Language type

The language type variable includes two subgroups: L1 (k = 11) and L2 (k = 19). According to the analysis results, the effect of VR-assisted instruction is higher in the L2 group (g = 0.664, 95% CI [0.390, 0.938]). In contrast, the effect size is relatively smaller in the L1 group (g = 0.357, 95% CI [0.208, 0.505]). No significant difference was found between language types, Q(1) = 3.739, p = 0.053. This result indicates that VR-assisted instructional applications are practical across language types. It can also be said that it yields relatively greater gains in the L2 context.

4.1.2 Target language

The target language variable consists of two subgroups: English (k = 18) and Chinese (k = 12). According to the analysis results, the effect size is larger in studies targeting English teaching (g = 0.653, 95% CI [0.369, 0.937]). In studies focusing on Chinese, this value is relatively lower (g = 0.392, 95% CI [0.234, 0.550]). In both languages, the effect sizes were positive and statistically significant. However, there is no significant difference in the target language variable, Q(1) = 2.470, p = 0.116. This result shows that the effect of VR-supported teaching applications on productive language skills does not differ between English and Chinese. Target language skills.

The target language skills variable comprises two subgroups: speaking (k = 12) and writing (k = 18), both productive. The effect size (g = 0.557, 95% CI [0.330, 0.784]) is relatively higher in studies on writing skills. In speaking skills, the effect size was calculated as 0.512 (95% CI [0.193, 0.831]). However, the difference between subgroups is not statistically significant [Q(1) = 0.051, p = 0.822]. This indicates that VR applications have a positive and significant effect on both productive language skills. Therefore, VR supports productive language skills holistically.

4.1.3 Learner educational level

The learner educational level variable was divided into four subgroups: primary school (k = 7), lower secondary school (k = 4), upper secondary school (k = 4), and university (k = 15). The highest effect size was obtained at the lower secondary school level (g = 0.682, 95% CI [0.339, 1.025]). This was followed by university (g = 0.666, 95% CI [0.340, 0.992]) and primary school (g = 0.407, 95% CI [0.236, 0.578]). In the upper secondary school group, the effect size was relatively small (g = 0.263, 95% CI [−0.092, 0.619]). The difference between learner educational levels is not statistically significant, Q(3) = 4.666, p = 0.198. These results indicate that VR applications have a substantial effect, particularly at the lower secondary and university levels. At the upper secondary school level, the effect size is not significant. This may be due to the limited number of studies at this educational level or related to contextual variables.

4.1.4 Control treatment

The control treatment moderator was divided into two groups: multimedia (k = 19) and traditional (k = 11). VR-supported instructional applications showed a much stronger effect than traditional instruction (g = 0.979, 95% CI [0.626, 1.331]). However, the effect size was smaller than that for multimedia-supported instruction (g = 0.298, 95% CI [0.136, 0.460]). This difference between the subgroups is statistically significant, Q(1) = 11.821, p = 0.001. Therefore, VR-supported instruction offers a significant advantage over traditional teaching in developing productive language skills.

4.1.5 Intervention duration

The intervention duration variable was examined in three subgroups: up to 2 weeks (k = 11), 3–6 weeks (k = 9), and more than 6 weeks (k = 10). According to effect size, shorter interventions showed a more negligible effect (g = 0.387, 95% CI [0.162, 0.613]). In contrast, medium- (g = 0.761, 95% CI [0.369, 1.152]) and long-term (g = 0.546, 95% CI [0.192, 0.901]) interventions yielded better outcomes than short-term interventions. The difference between the three groups was not statistically significant, Q(2) = 2.783, p = 0.249.

4.1.6 Intervention setting

The intervention setting variable was divided into two subgroups: in-class (k = 26) and out-of-class (k = 4). Considering the effect size, it is evident that out-of-class environments can also be evaluated for VR interventions (g = 0.846, 95% CI [0.237, 1.456]). Although the effectiveness of applications in the classroom environment is relatively low, it has a statistically significant and positive effect (g = 0.485, 95% CI [0.298, 0.673]). Comparisons between different environments are not statistically significant, Q(1) = 1.232, p = 0.267. Although VR-supported teaching applications outside the classroom show larger effect sizes, the number of studies remains limited.

4.2 Publication bias

If the majority of studies included in a meta-analysis are statistically significant, this may indicate publication bias (Borenstein et al., 2009). Publication bias refers to the possibility that published studies on a topic may not represent all studies (Rothstein et al., 2006). In the present meta-analysis, a funnel plot (Figure 3) and the Classic fail-safe N were used to assess publication bias and its impact on the results.

Figure 3

When the funnel plot was examined, the studies included in the meta-analysis were essentially symmetrically distributed around the cumulative effect size. Therefore, there is no significant publication bias in the current meta-analysis. Furthermore, additional calculations to assess publication bias were performed using CMA. The results of the Classic fail-safe N test indicated that 1,030 studies would be required for the cumulative effect to become insignificant. The trim-and-fill analysis indicated that no studies were imputed, suggesting a low likelihood of publication bias and supporting the robustness of the findings. To further examine publication bias, Egger’s regression intercept test was performed. The results were not statistically significant, indicating that the likelihood of publication bias was low and supporting the robustness of the findings.

5 Discussion

The results of the current meta-analysis study indicate that Virtual Reality (VR) can be used as an effective tool for developing productive language skills (PLSs), namely speaking and writing. The overall effect size, calculated from the effect sizes of all independent studies included in the meta-analysis, is (g = 0.538) and shows that VR has a moderately positive effect on the development of PLSs. VR supports learners’ cognitive and affective processes through multi-sensory, interactive, and context-based environments. Therefore, it is an important tool in improving individuals’ learning performance. This finding is consistent with previous research, which points out that VR supports language skill development (Alyaz and Demiryay, 2023; Hua and Wang, 2023; Şimşek, 2023; Wang and Matsumura, 2019; Žnideršič et al., 2025). Interactive learning significantly contributes to the development of writing skills (Feng and Ng, 2023; Liu, 2023; Zhai, 2023) by supporting idea generation and text structuring, helping students produce more detailed, coherent, and creative outputs, and to the development of speaking skills (Alemi and Khatoony, 2020; Ebadi and Ebadijalal, 2022; Frisby et al., 2020; Sülter et al., 2022; Palmas et al., 2019; Lear, 2020; Xie et al., 2019a,b; Yan et al., 2024) by improving fluency, pronunciation, and communicative competence through interactive environments. Furthermore, VR’s immersive and interactive nature can enhance the acquisition and retention of vocabulary within context, while strengthening motivation and participation in the lesson, thereby improving PLSs (Chen et al., 2014; Liu et al., 2023; Luan et al., 2024; Madini and Alshaikhi, 2017; Wang et al., 2021). A study by Hsiao (2021) demonstrated that VR increased student engagement and self-efficacy by supporting personalized learning experiences. However, VR supports autonomous learning and contributes to more effective language learning processes by reducing cognitive load over time by increasing students’ self-confidence, reducing anxiety, lowering cognitive load, and providing personalized learning opportunities (Akyıldız, 2025; Bailenson, 2018; Berns et al., 2018; Hoang et al., 2023; Gonzalez-Franco and Lanier, 2017; Ebadi and Ebadijalal, 2022; Monteiro and Ribeiro, 2020; Kaplan-Rakowski, 2019; Wehner et al., 2011). These possibilities demonstrate that VR offers significant advantages in developing individuals’ PLSs. These possibilities show that VR offers significant advantages in developing individuals’ productive language skills. From a pedagogical perspective, these findings suggest that VR is a functional tool for designing highly interactive and communication-oriented learning environments. VR applications integrated with task-based language teaching are reported to enhance language production by providing authentic communication contexts (Chen B. et al., 2022; Chen Y. et al., 2022; Takase, 2025; Yudintseva, 2023). Accordingly, it can be suggested that teachers should structure VR activities with role-playing, problem-solving and scenario-based communication tasks. For curriculum developers, it is important to support VR integration with explicit outcomes, process-oriented assessment tools, and performance-based assessment approaches (Kourtesis et al., 2025; Neher et al., 2026; Wang et al., 2024). Such an approach can contribute to the systematic and sustainable use of VR in curricula.

The meta-analysis revealed substantial heterogeneity (I² = 79.46), indicating substantial variation among the studies included. Therefore, teaching processes aimed at developing VR-supported PLSs may have different effects depending on the conditions under which they are implemented. Indeed, in their systematic review of the effects of immersive technologies on learning performance, cognitive load, and intrinsic motivation, Poupard et al. (2025) concluded that VR hinders learning performance by imposing unnecessary cognitive load. In contrast, its effect on learning motivation was uncertain. However, VR-based interventions do not always produce superior learning outcomes, that high levels of interactive participation can increase cognitive load in participants (De Witte et al., 2025; Grecu, 2025), that anxiety can increase in some contexts due to the strengthening of reality perception (Pertaub et al., 2002; Slater et al., 2006), that it can lead to problems such as dizziness and visual fatigue (Alkhammash et al., 2025; Biswas et al., 2024), and that potential inequalities in access may occur due to reasons such as high hardware and software costs (Fakahani et al., 2022). The disparate results in the literature and the high heterogeneity observed in this study necessitate a systematic examination of moderator variables to more comprehensively evaluate the effects of VR on the development of PLSs.

The results of the meta-analysis show that the type of target language does not have a significant effect on the results in interventions for productive language skills supported by VR technology. This finding suggests that the determining factor in VR-based language teaching designs can be developed in line with pedagogical principles rather than language type. In particular, basic principles such as communicative competence, interaction and context-based learning are known to function similarly in different target languages (Canale and Swain, 1980; Nation and Macalister, 2010). This suggests that the learning effect in VR environments may be related to the pedagogical construct rather than the structural features of the language. Similarly, Chen B. et al. (2022) and Chen Y. et al. (2022), in a meta-analysis of 21 quantitative studies (N = 1,144) published between 2010 and 2021, found no significant moderating effect of the target language variable on the effectiveness of VR-supported language instruction. Also, VR-supported instruction is effective regardless of language type. The effect size of VR-supported instruction on L2 (g = 0.664) was greater than on L1 (g = 0.357). Therefore, VR yields relatively greater gains in L2 contexts and is most used in L2 learning (Alfadil, 2024; Chen B. et al., 2022; Chen Y. et al., 2022). The main reason may be that virtual learning environments can provide immersive, realistic experiences that enable L2 learners to engage in social interaction while using the target language and interacting with native speakers.

This study determined that the target language variable did not have a significant effect on the outcomes in VR interventions aimed at improving PLSs. The studies included in the meta-analysis focused only on English and Chinese. Therefore, differences among these languages did not significantly affect VR effectiveness. In addition, the effect size was larger in studies targeting English (g = 0.653) than in those targeting Chinese (g = 0.392). This difference can be explained by the fact that VR content for English is both more numerous and more mature in its pedagogical structure. Furthermore, this result supports that the vast majority of studies on VR-assisted language teaching, as seen in meta-analyses and systematic reviews, address English as the target language (Chen B. et al., 2022; Chen Y. et al., 2022; Dhimolea et al., 2021; Wang et al., 2020; Qiu et al., 2024; Parmaxi, 2020; Pinto et al., 2021; Makransky and Petersen, 2019). The large number of studies focusing on English in current research may have contributed to increased pedagogical knowledge and design quality in VR-based teaching applications. On the other hand, while there are VR studies in the literature focusing on speaking and writing skills in different languages, these studies were not included in the analysis because they did not meet the inclusion criteria of the meta-analysis (Alhajya et al., 2018; Keller et al., 2024; He and Suñer Munoz, 2025; Humayrah et al., 2025; Taguchi and Hanks, 2024; Thrasher, 2022). In this context, future studies with larger sample sizes across different target languages could more comprehensively evaluate the role of the target-language variable in the effectiveness of VR interventions.

The results of this meta-analysis showed that target language skills did not have a significant effect on outcomes in VR interventions aimed at improving PLSs. This indicates that VR applications have a positive and significant effect on both PLSs. Looking at the effect size difference between language skills, the effect sizes for speaking (g = 0.512) and writing (g = 0.557) skills are close. Therefore, VR supports PLSs holistically. Similarly, existing meta-analyses and systematic reviews indicate that VR has positive, statistically significant effects on language learning outcomes, with these effects particularly pronounced in interaction- and context-based learning situations (Chen et al., 2022; Peixoto et al., 2021; Radianti et al., 2020). At this point, the interactive communication contexts, multisensory stimulation, and embodied learning experiences offered by VR environments stand out as key elements that explain the development of PLSs (speaking and writing). Indeed, studies have shown that VR-assisted applications reduce anxiety and significantly improve productive language performance (Dhimolea et al., 2021; Dolgunsöz et al., 2018; Feng and Ng, 2023; Thrasher, 2022; Ding, 2024; Park et al., 2025).

The effect of VR interventions did not differ significantly by education level. When examining effect sizes across subgroups, the lower secondary school (g = 0.682) and university (g = 0.666) levels had larger effect sizes than the other groups. This was followed by primary school (g = 0.407). Furthermore, it was determined that VR interventions had no significant effect in the upper secondary school group (g = 0.263). This may be related to the limited number of studies on this education level or to contextual variables. Overall, the results indicate that VR interventions have a substantial effect, particularly at the lower secondary and university levels. Indeed, VR applications are prevalent among older age groups and particularly at the higher education level, both in the number of studies and in the intensity of use. In this context, the use of VR at the higher education level has significant and positive effects on learning outcomes and student experience (Campos et al., 2022; Çoban et al., 2024; Fan and Zhang, 2024; Radianti et al., 2020; Makransky and Petersen, 2019; Llanos-Ruiz et al., 2025; Liu et al., 2025; Jensen and Konradsen, 2018; Suhag, 2024). On the other hand, the use of this technology is limited among younger children due to physiological and psychological risks associated with VR applications and the possibility of exposure to harmful content (Milovidov, 2023; Scovell, 2023; Galoustian, 2024). These studies also emphasize that, due to the risks and privacy concerns inherent in VR (Smith, 2024), the process should be limited to parental supervision and pedagogical guidance (AI-Heeti, 2018). Therefore, these risks and requirements help explain why VR is more widely used among groups with higher cognitive maturity, abstract thinking, and technical device use skills.

This study found that the control treatment variable significantly affected outcomes in VR interventions aimed at improving PLSs. VR-assisted instruction showed a much stronger effect compared to traditional instruction (g = 0.979). However, the effect size was smaller than that for multimedia-assisted instruction (g = 0.298). Therefore, VR-assisted instruction offers a significant advantage over traditional instruction in developing PLSs. Indeed, in VR-assisted language learning studies, substantial evidence indicates that VR applications yield higher learning outcomes than traditional methods, particularly for PLSs such as speaking and writing (Huang et al., 2022; Radianti et al., 2020). Furthermore, numerous studies have reported that the interactive, contextual, and immersive features offered by VR-based learning environments encourage learners to produce language actively, thereby increasing their motivation and significantly improving their language performance (Akyıldız, 2025; Legault et al., 2019; Makransky and Petersen, 2019; Ozgun and Sadık, 2023; Yan et al., 2024; Žnideršič et al., 2025). In this context, the findings of this study are consistent with the literature showing that VR-supported instruction is more effective than traditional teaching processes in developing PLSs and support the evidence in the literature.

The finding that VR outperformed both traditional instruction and multimedia-supported instruction indicates that VR interventions hold strong potential for enhancing productive language skills. However, the larger effect observed in comparison with traditional instruction may be explained by the fact that multimedia-supported instruction already incorporates evidence-based instructional elements such as audiovisual supports, structured content, and guided practice (Noetel et al., 2022). Because baseline learning effectiveness is typically higher in multimedia environments, the incremental contribution of VR may appear more limited in such comparisons. Moreover, VR interventions often embed multimedia components themselves, including narration, visual cues, and structured task sequences. This overlap may increase shared pedagogical elements between VR and multimedia control conditions, thereby reducing the observable difference between them (Meyer et al., 2019). In addition, variations in instructional guidance and differences in alignment between VR-based learning experiences and the outcome measures used may further attenuate the observed effect size in VR–multimedia comparisons (Meyer et al., 2019; Radianti et al., 2020). Nevertheless, this pattern does not negate the overall effectiveness of VR as a learning environment. In this context, it would be more appropriate for teachers to structure VR applications with task-based and production-based activities to enrich learning environments with low interaction levels. For curriculum developers, planning VR integration in a framework that is compatible with interaction and outcome-oriented learning goals and embedded in instructional design can strengthen the impact of VR on learning outcomes.

The results of the current meta-analysis showed that the intervention duration variable did not have a significant effect on outcomes in VR interventions aimed at improving PLSs. Furthermore, shorter intervention durations had a smaller effect size (g = 0.387). In contrast, medium-term (g = 0.761) interventions had the largest effect size, while long-term (g = 0.546) interventions yielded results relatively close to it. Short-term interventions, particularly in technology-supported applications, can rapidly increase students’ motivation, primarily through the “novelty effect” (Cai et al., 2022). However, larger effect sizes are observed with structured, medium-term (3–6 weeks) VR exposures, and there is increasing evidence that lasting performance gains are strengthened. Indeed, VR environments designed for repeated exposure can contribute to language learning (Dhimolea et al., 2021). However, the study emphasizes that the effectiveness of interactive virtual reality environments in terms of the permanence and generalizability of language skill gains should be carefully evaluated, depending on the instructional design and the time available for implementation. Additionally, the study found no significant effect of the intervention setting variable among studies employing VR interventions. Given the effect size, out-of-classroom environments may also warrant consideration for VR interventions (g = 0.846). However, the number of studies on this matter is quite limited. Therefore, caution should be exercised when evaluating the results. Although the effectiveness of classroom applications is relatively low, it has a statistically significant positive effect (g = 0.485). When the overall effect size and moderator analysis results of the current meta-analysis are evaluated, it can be said that VR is a powerful and effective pedagogical tool in developing PLSs. This effect has been similarly observed in numerous studies conducted in different contexts in the literature, both in terms of speaking skills (Boetje and Ginkel, 2021; Bachmann et al., 2023; Takac et al., 2019; Valls-Ratés et al., 2024) and writing skills (Azir, 2022; Feng and Ng, 2023; Huang et al., 2020; Mohamed et al., 2022; Muthmainnah et al., 2025; Sitorus et al., 2025).

The substantial heterogeneity observed in this meta-analysis (I² ≈ 79%) suggests that variability may extend beyond the examined moderators. Although variables such as language type, target language, target language skills, educational level, control treatment, intervention duration, and intervention setting were analyzed, additional factors may have contributed to the differences across studies. In particular, the manner in which VR was pedagogically integrated, levels of immersion and interactivity, instructional guidance, and implementation fidelity may have influenced learning outcomes. Contextual factors such as technological infrastructure, teacher mediation, and learners’ familiarity with immersive environments may also have contributed to variability in results. Furthermore, differences in assessment instruments and the operationalization of learning outcomes across studies may have amplified heterogeneity. These findings highlight the context-sensitive nature of VR-supported language learning and help explain the remaining heterogeneity (Radianti et al., 2020; Wu et al., 2020).

6 Conclusion

The results of the current meta-analysis indicate that VR is a powerful and effective pedagogical tool for developing productive language skills. The overall effect size (g = 0.538, 95% CI [0.355, 0.721], p < 0.05) indicates that VR use has a positive, moderate effect across the contexts examined in the studies. The observed heterogeneity (I² = 79.465%) indicates that the effect of VR may vary across studies. To understand the source of this difference, we determined the variables “language type, target language, target language skills, learner educational level, control treatment, intervention duration, intervention setting” as moderators and performed a moderator analysis. The results were significant only in the control treatment moderator. This indicates that VR interventions can have similar effects across contexts. According to the results of the moderator analysis, VR is more effective in L2 teaching. This result demonstrates that VR can offer significant advantages in learning a new language, particularly in developing productive language skills. Furthermore, the analysis of the target language moderator showed that the effect size was relatively larger in studies focused on English. This result may be related to the widespread use of technology in English teaching and the greater availability of high-quality content. According to the analysis results, there is no significant difference between speaking and writing skills within the target language skill moderator. Overall, the results show that VR interventions have a positive and similar effect on both productive language skills. When the results were examined by educational level, the educational level variable was not significant. However, the results were relatively more positive at the lower secondary school and university levels. One important finding of the study is that the VR intervention has a significantly larger effect size than traditional methods. However, this effect is smaller than that of a media-supported intervention. Therefore, the use of VR in studies of productive language skills could be more widely adopted. Furthermore, although the duration variable did not have a significant effect on VR-supported interventions to improve productive language skills, medium-term interventions (3–6 weeks) may be more beneficial. These interventions may have a greater impact when conducted in a classroom setting than when conducted outside the classroom. Overall, the results suggest that VR has a significant impact on the development of productive language skills.

7 Limitations and recommendations

One limitation of this study is that the meta-analysis was not preregistered in a public repository. Although the review followed established methodological standards, preregistration could further enhance transparency and reduce potential bias. Future studies may benefit from preregistered protocols. Another limitation of this study is the exclusion of gray literature (e.g., dissertations, conference proceedings, and unpublished studies). Although focusing on peer-reviewed sources increases methodological quality, this decision may introduce potential selection bias. Future meta-analyses may benefit from including gray literature to provide a more comprehensive evidence base. The study has some limitations. Firstly, this meta-analysis is limited to journal articles indexed in the specified electronic databases between 2015 and 2025. Therefore, it should be noted that the results may differ in a review that includes different years and databases. The studies included in the review focused more on the L2 level and were conducted in English and Chinese. Therefore, it is recommended that similar studies be conducted in different languages and that the number of studies focusing on the native language be increased. In addition, we found that a significant portion of VR interventions were conducted at the university level, with fewer applications among younger age groups. Therefore, future research could be structured to focus on younger age groups. This would yield more precise results on the impact of VR at different learning levels. In this study, interventions in the control group were examined under two headings: studies using media and studies using traditional methods. The results showed that the effect of VR use was greater than that of teaching the same subject using traditional methods. Therefore, it can be suggested that instructors consider VR as an alternative to certain activities traditionally conducted using purely traditional methods for developing individuals’ productive language skills, based on these data. Furthermore, the media heading in the control intervention encompasses interventions using different technologies. To better understand potential differences, more detailed studies could examine how VR affects various technological tools. Finally, given that the impact of medium- and long-term VR interventions is relatively greater, it is recommended that instructors develop long-term plans when integrating this technology into their lesson content.

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