1. Introduction
Through integration into educational systems in line with the principles of peace and social justice, technologies can act as vectors that drive and accelerate equitable and quality education while fostering relevant learning opportunities, presenting themselves as a solution to global educational challenges (UNESCO, 2015, 2023a, 2023b). However, the educational integration of emerging technologies such as extended reality and artificial intelligence remains a novel topic for students and teachers. Most schools still lack regulations and specific programs for their mediation (UNESCO, 2023a; Jang et al., 2022), primarily due to the lack of models and educational approaches that can help them to understand which strategies work best with these technologies to support teaching, making it necessary to explore them for a better understanding (Chen et al., 2020; Kamalov et al., 2023).
Integrating technologies into teaching practices based on justified educational approaches and models makes a fundamental difference in improving student learning. From a socio-constructivist perspective, learning is not a passive process; students are active and leading agents, capable of constructing their knowledge through interactions with their environments and others. In line with this idea, emerging technologies can be integrated into teaching, becoming facilitators and key supports for these interactions to occur in new and easier ways. The active participation of students in constructing their learning involves important challenges that must be addressed during teaching, with attitudinal factors such as motivation and interest being among the main ones (Alé-Silva & Sánchez, 2024; Maehr & Archer, 1985; Huett, 2006). Theoretically distinguishing motivation and interest in the academic context is not an easy task, as they are often used interchangeably in research. Despite the conceptual definitions of motivation and interest varying due to the inherent nature of these constructs, there are significant differences that distinguish them. According to Renninger et al. (2018), students may be motivated towards their education without being interested in it, meaning that interest and motivation are fundamentally autonomous attitudes that operate separately. On this point, Lavonen et al. (2021) argues that when students have a specific interest in a subject, it can be intrinsically motivating (Lavonen et al., 2021).
Motivation is a key factor in keeping students connected to their learning so that they can achieve class goals (Di Serio et al., 2012; Lavonen et al., 2021; Renninger et al., 2018). Hidi and Harackiewicz (2000) also explain this, conceptualizing motivation as a person’s willingness and ability to engage in and achieve their goals, influenced by their socio-cultural context and circumstances (Hidi & Harackiewicz, 2000). Educational psychologist John Keller points out that motivation is “what people want and what they choose to do” (Keller, 2009). Keller (1987) developed the ARCS motivational model, which emphasizes the importance of attention, relevance, confidence, and satisfaction in student learning (Keller, 1987). As a result, motivation is crucial for both students’ self-set goals and their attainment of the learning objectives that teachers establish for them during class (Pintrich, 2003).
While motivation does influence student behavior, it is still unclear whether students with higher motivation necessarily achieve better academic performance (Fong et al., 2017; Goksu & Bolat, 2021; Sarier, 2016). Similarly, the relationship between the use of emerging technologies like artificial intelligence and motivation and academic performance is also unclear. Recent meta-analytic studies reported in the literature have explored the academic effectiveness of generative AI (Lee & Kwon, 2024; Wu & Yu, 2023) and the academic impact of XR (Chang et al., 2022; Li et al., 2021; Shi & Kopcha, 2022; Yu & Xu, 2022), without delving into the existing relationships between both technologies and student motivation.
1.1. The Motivational ARCS Model with Emerging Technologies
In the historical search to find techniques and strategies that help us to understand the relationships between motivation components in the learning process, educational psychologist John Keller (1987) proposed a motivational framework founded on four attitudinal elements: attention, relevance, confidence, and satisfaction (ARCS). This model is sequential and aims to improve the motivational aspect of instructional materials by enriching instructional strategies during teaching (Keller, 1987).
During the attention phase, the student’s interest and curiosity are sparked (Keller, 2009). For activities to have relevance, they must link the student’s personal interests, goals, and objectives with the central themes of the class. During confidence reinforcement, the student’s perception of their ability to succeed in assigned tasks is stimulated, overcoming fears that hinder their learning. Finally, during the satisfaction stage, feedback is ensured, allowing students to evaluate their achievement of the objectives set during class Keller (1987). These aspects, as Keller (2009) explains, would help to make teaching more stimulating and also encourage students to become more autonomous and feel more responsible for their own learning.
The ARCS motivational model is frequently applied to instructional design mediated by technologies (Keller, 2016), in distance education (Huett, 2006), and in areas related to STEM education (Keller, 2016). It has also been shown to improve technology-mediated language learning and promote communication skills and school performance (Piriyasurawong, 2019). Its implementation also supports collaborative work tasks and academic research skills (Refat et al., 2019).
In research on empirical studies using the ARCS model, it is common to find more applications of quantitative methods than qualitative ones (Keller, 2016). In qualitative empirical studies, the results obtained are often inconsistent (Keller, 2016), which causes the model to remain weak in explaining the integration of technologies in learning and motivation (Woo, 2014).
When examining the current literature, an absence of systematic reviews and meta-analyses related to the ARCS motivational model was observed. The most recent literature review on the ARCS model was conducted by Li and Keller (2018), focusing exclusively on qualitative results. The latest meta-analysis, carried out by Goksu and Bolat (2021), analyzed a limited sample of studies, only including research published up to 2018. This latter study is important, as it did not take into account the rapid increase in the use of emerging technologies, such as artificial intelligence, in teaching in recent years, which indicates a need for more quantitative studies to provide an updated overview.
1.2. Research Questions
In this meta-analysis, we aim to answer the following nine questions. We consider the questions previously presented by Goksu and Bolat (2021) to guide this meta-analysis. Answering these questions would allow us to compile and integrate the quantitative empirical evidence reported in the scientific literature in recent years, identifying the main trends in the use of emerging technologies such as AI and XR, as well as exploring moderators related to active teaching strategies and methodologies.
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1.. What is the global effect of the ARCS motivational model with emerging technologies on academic performance?
1.1.. How does performance vary by the type of emerging technology and teaching modality?
1.2.. How does performance vary by subject and teaching strategy?
1.3.. How does performance vary by educational level and implementation time?
2.. What is the global effect of the ARCS motivational model with emerging technologies on student motivation?
2.1.. How does motivation vary by the type of emerging technology and teaching modality?
2.2.. How does motivation vary by subject and teaching strategy?
2.3.. How does motivation vary by educational level and implementation time?
2.4.. What is the effect of emerging technologies on the components of the motivational model?
2. Materials and Methods
2.1. Review Characteristics
Following the recommendations of Cooper et al. (2019) and Card (2012) and the PRISMA Statement and Checklist, an additive synthesis of studies was conducted through a systematic review with meta-analysis to integrate the available evidence. This was performed with the aim of estimating and separately examining the effects of the ARCS motivational model, using the emerging technologies of artificial intelligence and extended reality, on the academic performance and motivation of school and university students.
Cooper et al. (2019) and Card (2012) suggest clearly establishing procedures related to (1) literature search procedures with complementary searches; (2) defining eligibility or selection criteria for the studies (inclusion and exclusion criteria); (3) coding the main characteristics of the selected studies; (4) establishing strategies for statistical analysis; (5) establishing a strategy to assess potential publication biases.
These procedures facilitate the analysis and allow the integration of quantitative data from experimental research (Tatsioni & Ioannidis, 2017).
2.2. Literature Search Procedures
In order to conduct a thorough and high-quality systematic review with meta-analysis, primary studies were considered and reviewed from databases such as Web of Science (WoS), Scopus, ERIC, and APA PsycNet. In this study, we chose to conduct an exhaustive search, without year restrictions, to incorporate and integrate the results from as many educational experiences with artificial intelligence and extended reality technologies as possible. For this reason, during the searches, no year or language limitations were considered.
Wildcards (*) were used to broaden the range of some words, while Boolean operators (OR/AND) were used to establish relationships among technological, motivational, and educational concepts.
Variable 1: “Artificial intelligence” OR “Learning analytics” OR “Big data” OR “Machine learning” OR “Intelligent Tutoring System” OR “Technologies supported learning system” OR “ChatGPT” OR “Chat-GPT” OR “Chat GPT” OR “Gemini” OR “Copilot” OR “BingChat” OR “Bing Chat” OR “Chatbot*” OR “Chat bot*” OR “Virtual assistant” OR “Intelligent assistant” OR “adaptative learning” OR “adaptative tutoring” OR “intelligent tutoring” OR “AI-based robot” OR “AI-based Chatbots” OR “AI-based voice assistants” OR “Expert system” OR “Adaptive educational platforms” OR “Adaptive virtual learning” OR “Teaching assistant” OR “Adaptive digital learning” OR “Adaptive online learning” OR “Adaptive e-learning” OR “Adaptive electronic learning” OR “Adaptative learning” OR “Personalized learning” OR “Customized learning” OR “Adaptive educational” OR “Learning analytics-based instruction” OR “Stable Diffusion” OR “Midjourney” OR “Perplexity” OR “Teachable Machine” OR “RAISE Playground” OR “Machine Learning for Kids” OR “virtual reality” OR “VR” or “augmented reality” OR “AR” OR “mixed reality” OR “MR” OR “extended reality” OR “XR”.
Variable 2: “Learning” OR “Education*” OR “School*” OR “High* Education” OR “University” OR “College” OR “Postsecondary” OR “K-12” OR “K-12 Student*” OR “7–12” OR “13–17” OR “High School” OR “Middle School*” OR “Secondary School” OR “Elementary education” OR “Primary education” OR “Secondary education” OR “Early childhood” OR “Basic education”.
Variable 3: “ARCS*” OR “Instructional Materials Motivation Survey” OR “IMMS*” OR “Attention, Relevance, Confidence, and Satisfaction” OR “Attention, Relevance, Confidence, Satisfaction”.
We expanded the scope of the database search by incorporating as many search codes related to artificial intelligence and extended reality technologies as possible, ensuring that no relevant studies exploring how these technologies are integrated into educational environments were excluded. Some of the search codes used (e.g., “personalized learning”, “customized learning”, and “adaptive educational”) do not always constitute a technological variable in themselves but are sometimes used in research to refer to teaching systems operating in learning environments supported by emerging technologies (e.g., as with adaptive artificial intelligence technologies).
As of 6 October 2024, a total of 2656 peer-reviewed articles from academic journals and international conferences were obtained (Alé & Arancibia, 2024). These references were downloaded directly from the consulted databases in “RIS” or “BIB” format and were then uploaded to the Rayyan AI platform to compile them and streamline the review process.
2.3. Selection Criteria
First, the abstracts of the 2656 articles were reviewed. Out of the total, 638 articles that were duplicates were excluded. Additionally, following the recommendations in Cooper et al. (2019), complementary searches were conducted to access articles that were not available through the consulted databases or whose results were not fully published. This strategy involved sending emails to the corresponding authors requesting the necessary information to calculate the effect sizes. To retrieve these studies, we contacted 16 researchers via email, requesting that they send the results of their research. After waiting two to three weeks, we received responses to 4 of the 16 emails sent. However, none of the articles provided in these responses met the eligibility criteria established for inclusion in the sample, and they were not incorporated into the study.
After reading the abstracts, 1596 articles that did not meet the following eligibility criteria were excluded:
1.. Having a control and experimental group;
2.. The experimental group incorporated the ARCS model in instructional design;
3.. The experimental group incorporated artificial intelligence or extended reality in instructional design.
Thus, an initial sample of 86 articles was obtained, and the full texts were thoroughly analyzed. The 86 articles were read, and those that met the following eligibility criteria were included in the study, considering multiple rounds of reading and repeated analysis:
4.. They included measurements of students’ academic performance and/or motivation; motivation was assessed using the Instructional Materials Motivation Survey (IMMS) based on the ARCS model, while academic performance was evaluated through knowledge tests aligned with the learning objectives of each study;
5.. The studies reported statistical data for each group, including the number of participants (N), average or mean (Me), standard deviation (SD), significance (p value), and effect size (Cohen’s d);
6.. Group equivalence was provided or randomly assigned.
Finally, after multiple rounds of evaluation, 32 articles were selected for inclusion in the statistical meta-analysis. The stages leading to the identification of these 32 studies are graphically represented in the flow diagram in Figure 1.
2.4. Study Coding
The 32 articles from the final sample were thoroughly examined, and the data were coded, processed, and organized into various data tables.
First, Table 1 was constructed to summarize the overall characteristics of the studies, such as the year of publication, the country of origin of the intervention, the database, and the journal or conference.
Table 2 was constructed to summarize the characteristics of the teaching experiences, such as the type of emerging technology used (e.g., augmented reality (AR), virtual reality (VR), AI with intelligent tutors, AI with chatbots, AI with adaptive systems, etc.), along with a description of the main features of the technologies used; the teaching modality (in-person, virtual, or hybrid); the subject or discipline related to the teaching context (e.g., mathematics, computer science, history, languages, etc.); the age or educational level of the participants (primary 6–10 years, middle school 10–14 years, high school 14–18 years, university); and the duration of implementation (in weeks).
Lastly, we prepared tables (Appendix A) to summarize the main statistical results related to the academic performance and motivation of the control and experimental groups (N, M, p/t/d/Mean-Diff).
2.5. Data Analysis Strategy
In order to facilitate graph construction and effect size calculation using Cohen’s d statistic (because most of the studies had populations of n > 20), the data from the tables were analyzed using descriptive and inferential statistical techniques in the SPSS 29 environment. First, we defined the numerical variables in the software and imported the data reported in the selected studies for both the control and experimental groups (number of students, mean values of motivation and academic performance measures, and standard deviations). Then, we accessed the “Analysis” option in SPSS to perform “meta-analysis” tests for continuous outcomes and raw data and calculated the effect sizes (Cohen’s d). A random-effects model was used for calculations with primary data extracted directly from the published literature (see data in Appendix A), while fixed- or mixed-effects models were applied for moderator analyses.
Additionally, for the moderator analyses, a heterogeneity analysis between groups (QB) was included to determine whether there were significant variations according to each category type of each moderator variable.
2.6. Publication Bias Assessment
Following the recommendations of Cooper et al. (2019), an overall assessment of the publication bias was also included to evaluate the effects on academic performance and motivation.
a. Overall academic performance of students: A total of 37 effect sizes were calculated for the general academic performance of the studies, and outliers that could cause publication bias were removed, as obtained from Hung et al. (2012), Lu et al. (2018), Tsai (2020). A forest plot was created, integrating the 33 effect sizes related to academic performance. The results were evaluated using a funnel plot, which is presented in Figure 2, to show the distribution of the standard errors and weighted effect sizes. When examining the funnel plot in Figure 2, it can be observed that the effect sizes are symmetrically distributed within the pseudo-confidence intervals, with 13 studies on the right and 12 studies on the left of the funnel’s central value. This distribution is also symmetric in terms of the standard error values, with no concentrations at the base or the tip of the cone. This indicates that the potential bias is relatively low and without overestimation.
b. Overall motivation of students: A total of 32 effect sizes related to overall motivation in the studies were calculated, and outliers that could cause publication bias were removed, as obtained from Wu (2018), Tsai (2020), Cevahir et al. (2022), Kumar et al. (2023). A forest plot was created with 27 effect sizes related to overall academic performance. The results of the 27 effect sizes were evaluated using a funnel plot, which is presented in Figure 3, to show the distribution of the standard errors and weighted effect sizes.
When examining the funnel plot, it can be observed that the effect sizes are also symmetrically distributed within the pseudo-confidence intervals. This indicates that the potential bias is relatively low and without overestimation.
c. Specific motivational components: A total of 103 effect sizes related to the ARCS components of the studies were also calculated (A: 26, R: 26, C: 25, S: 26), along with the total motivation values (view Appendix B). Ten outliers that could cause publication bias were removed. A forest plot was created with 103 effect sizes related to overall academic performance. The effect size results were evaluated using a funnel plot, which is presented in Figure 4, to show the distribution of the standard errors and weighted effect sizes.
3. Results
A total of 172 effect sizes were estimated. Of these, 103 pertained to specific components of the ARCS model, 37 to overall academic performance, and 32 to overall motivation. Based on these results, we proceeded to examine the effects of the ARCS model.
Additionally, the effects were analyzed according to each of the different moderators (see Appendix C and Appendix D). The findings are presented below according to each research question.
3.1. Global Effect of the Motivational Model with Emerging Technologies on Academic Performance
The 33 results from independent primary studies on performance were integrated. Following the random-effects model, the overall effect of the ARCS model on academic performance was 0.596 (SD: 0.078, 95% CI: 0.443–0.748, p < 0.001). This finding suggests that the ARCS model using emerging technologies has a moderate and significant effect on students’ academic performance. As shown in the forest plot in Figure 5, fourteen of the calculated effect sizes did not show a significant difference between the control and experimental groups, while the remaining 19 showed a significant difference (p < 0.05) in favor of the experimental group.
The largest and most significant effect sizes were for Proske et al. (2014) with 2.34 (SD: 0.30, 95% CI: 1.46–3.23, p < 0.001) and Riedmann et al. (2022) with 1.12 (SD: 0.26, 95% CI: 0.61–1.64, p < 0.001). On the other hand, the lowest effect was for Bhagat et al. (2021) with −0.30 (SD: 0.49, 95% CI: −0.25–0.66, p = 0.54).
a. Effect sizes regarding academic performance according to moderators such as “technology type” and “teaching modality”
We first recalculated the effect sizes on performance, grouped by the type of emerging technology used and the teaching modality. The effect sizes were moderate and significant in teaching environments mediated by artificial intelligence with 0.619 (SD: 0.0547, CI: 0.512–0.727, p < 0.001) and augmented reality with 0.741 (SD: 0.0628, CI: 0.618–0.864, p < 0.001), and they were small for environments mediated by virtual reality with 0.482 (SD: 0.0964, CI: 0.231–0.671, p < 0.001). The heterogeneity value between the groups was (QB) = 6.91 and p = 0.0316, df(Q) = 2, indicating that the variance among the calculated effect sizes by technology type was significant. Therefore, there was a variation in performance depending on the type of technology used.
We also recalculated the effect sizes grouped by the teaching modality. The effect sizes were moderate and significant in both face-to-face teaching environments with 0.630 (SD: 0.0480, CI: 0.536–0.724, p < 0.001) and in virtual and hybrid environments with 0.632 (SD: 0.0700, CI: 0.495–0.769, p < 0.001). In this case, the heterogeneity between the groups was (QB) = 0.77 and p = 0.381, df(Q) = 1, suggesting that the observed variance in the effect sizes calculated for the moderators according to the modality was not significant. Therefore, there was no variation in performance according to the type of teaching modality. As no significant differences were found in the heterogeneity tests between the two teaching modalities (p = 0.381), this result suggests that the integration of emerging technologies with pedagogical models such as ARCS can facilitate academic achievements equivalent to those attainable in the face-to-face modality.
b. Effect sizes on academic performance according to educational moderators such as “subject” and “teaching strategy”
Effect sizes were calculated for performance grouped by the subject or discipline in which experiences with the groups were applied. The effect sizes were moderate and significant in natural sciences with 0.644 (SD: 0.0716, CI: 0.503–0.784, p < 0.001); in languages, mainly to learn English, with 0.548 (SD: 0.0581, CI: 0.434–0.661, p < 0.001); in computing with 0.517 (SD: 0.0716, CI: 0.180–0.853, p < 0.005); and in history with 0.630 (SD: 0.1738, CI: 0.360–0.900, p < 0.001). They were low in mathematics with 0.463 (SD: 0.1186, CI: 0.231–0.696, p < 0.001) and very high in arts and design with 1.603 (SD: 0.1757, CI: 1.259–1.948, p < 0.001). For this moderator variable, the heterogeneity between the groups was (QB) = 35.49 and p < 0.001, df(Q) = 5, suggesting that the variance observed among the calculated effect sizes for the moderators according to the subject was significant. Therefore, there was a variation in performance according to the type of subject.
Effect sizes were also calculated for performance grouped by the teaching strategies used in classes. The effect sizes were moderate and significant when implementing only ARCS strategies in the experimental group, with an effect size of 0.561 (SD: 0.0516, CI: 0.460–0.662, p < 0.001), and also in strategies that combined ARCS with game-based learning (GBL), with an effect size of 0.739 (SD: 0.1088, CI: 0.526–0.953, p < 0.001). Conversely, the results showed large effect sizes when combining ARCS with gamification, with 1.126 (SD: 0.2886, CI: 0.560–1.691, p < 0.001), and project-based learning, with 1.070 (SD: 0.1532, CI: 0.769–1.370, p < 0.001). The results also showed small and non-significant effects when combining ARCS with the flipped classroom, with an effect size of −0.37 (SD: 0.2159, CI: −0.461–0.386, p > 0.05). For this moderator variable, the heterogeneity among the groups was (QB) = 35.15 and p < 0.001, df(Q) = 4, indicating significant variance among the effect sizes calculated for the moderators according to the strategy. Therefore, there was a variation in performance according to the type of teaching strategy implemented.
c. Effect on academic performance according to “educational level” and “implementation time” for moderators
The recalculation of the effect sizes for performance grouped by “educational level” was performed. The effect sizes were small and significant in primary education levels with 0.423 (SD: 0.0757, CI: 0.273–0.574, p < 0.001) and secondary education with 0.444 (SD: 0.0986, CI: 0.833–1.219, p < 0.001); they were high in middle school education with 1.026 (SD: 0.0986, CI: 0.833–1.219, p < 0.001) and medium in higher education with 0.768 (SD: 0.0820, CI: 0.608–0.929, p < 0.001). The heterogeneity value among the groups was (QB) = 32.58 and p < 0.001, df(Q) = 3, concluding that the variance between the calculated effect sizes by the type of technology was significant.
Therefore, there was a variation in performance by educational level. The recalculation of the effect sizes for performance grouped by the implementation period was also performed. The effect sizes were large and significant for 1 to 3 weeks of implementation with 0.844 (SD: 0.0622, CI: 0.732–0.967, p < 0.001) and for 7 to 9 weeks with 0.855 (SD: 0.1437, CI: 0.573–1.137, p < 0.001), but they were medium for 3 to 6 weeks with 1.085 (SD: 0.1614, CI: 0.769–1.402, p < 0.001) and 10 or more weeks with an effect size of 0.509 (SD: 0.1040, CI: 0.305–0.712, p < 0.001). The heterogeneity value among the groups was (QB) = 28.18 and p < 0.001, df(Q) = 3; therefore, the variance among the effect sizes calculated by the type of technology was significant, and the differences between technologies were not due to chance. Therefore, there was a variation in performance according to the implementation period of the activity.
3.2. General Effect of the ARCS Model on Motivation
Twenty-seven results with independent data from primary studies on motivation were integrated. Following the random-effects model, the overall effect of the ARCS model on student motivation was 0.886 (SD: 0.1258, CI 95%: 0.640–1.133, p < 0.001). This finding suggests that the ARCS model, with the use of emerging technologies, has a strong and significant effect on the overall motivation of students engaged in activities enriched with the ARCS model. As shown in the forest plot of Figure 6, seven calculated effect sizes showed no significant difference between the experimental group and the control group, while the remaining 20 studies showed a significant difference in favor of the experimental group. The largest effect sizes were those for Kang and Kusuma (2020) with 2.17 (SD: 0.31, CI 95%: 1.56–2.77, p < 0.001) and Hsieh (2021) with 2.08 (SD: 0.35, CI 95%: 1.39–2.77, p < 0.001). On the other hand, the smallest effect sizes were those for Riedmann et al. (2022) with 0.06 (SD: 0.40, CI 95%: −0.72–0.85, p = 0.88) and Lu et al. (2018) with 0.08 (SD: 0.28, CI 95%: −0.45–0.62, p = 0.76).
a. Effect sizes for motivation according to moderators such as “technology type” and “teaching modality”
We recalculated the effect sizes for motivation grouped by the type of emerging technology used. The effect sizes were large and significant when using artificial intelligence with 0.841 (SD: 0.0591, CI: 0.725–0.957, p < 0.001); augmented reality with 0.974 (SD: 0.0668, CI: 0.839–1.109, p < 0.001); and virtual reality with 1.316 (SD: 0.1205, CI: 1.080–1.552, p < 0.001). The heterogeneity value among the groups was (QB) = 14.95 and p < 0.001, df(Q) = 2, concluding that the variance between the effect sizes calculated by the type of technology was statistically significant. Therefore, there was a variation in motivation according to the type of technology.
We also recalculated the effect sizes for motivation grouped by teaching modality. The effect sizes were large for the face-to-face modality with 1.115 (SD: 0.0553, CI: 0.997–1.213, p < 0.001) and medium for the virtual modality with 0.733 (SD: 0.0715, CI: 0.593–0.873, p < 0.001). The heterogeneity value among the groups was (QB) = 21.73 and p < 0.001, df(Q) = 1, concluding that the variance between the effect sizes calculated by modality was statistically significant. Therefore, there was a variation in motivation according to the type of teaching modality. As significant differences were found in the heterogeneity tests (p < 0.001) between the two teaching modalities, this result suggests that the integration of emerging technologies with pedagogical models such as ARCS supports student motivation in both modalities, but predominantly in the face-to-face modality.
b. Effect on motivation according to “the subject” and “teaching strategy”
We calculated the effect sizes for motivation grouped by educational subject. The effect sizes were large and significant in subjects related to natural sciences with 1.115 (SD: 0.0553, CI: 0.997–1.213, p < 0.001), computing with 1.988 (SD: 0.2324, CI: 1.532–2.443, p < 0.001), arts and design with 0.914 (SD: 0.0553, CI: 0.565–1.264, p < 0.001), and history with 1.248 (SD: 0.1744, CI: 0.906–1.590, p < 0.001). Meanwhile, the results were moderate for languages with 0.773 (SD: 0.0686, CI: 0.639–0.907, p < 0.001) and mathematics with 0.679 (SD: 0.0954, CI: 0.492–0.866, p < 0.001). For this moderating variable, the heterogeneity among the groups was (QB) = 36.87 and p < 0.001, df(Q) = 5, suggesting that the observed variance between the calculated effect sizes for the moderators by subject was statistically significant. Therefore, there was a variation in motivation according to the type of subject.
We calculated the effect sizes for motivation grouped according to the teaching strategies used. The effect sizes were large and significant when implementing ARCS strategies with 0.879 (SD: 0.0585, CI: 0.764–0.994, p < 0.001) and also for strategies combining ARCS with game-based learning (GBL), with an effect size of 1.014 (SD: 0.1002, CI: 0.818–1.211, p < 0.001), and gamification, with 3.365 (SD: 0.2002, CI: 2.972–3.757, p < 0.001). On the other hand, the effect sizes were moderate when implementing ARCS strategies for product design combined with project-based learning (PBL) with 0.735 (SD: 0.1807, CI: 0.381–1.090, p < 0.001) and flipped classrooms with 0.358 (SD: 0.2176, CI: −0.068–0.785, p = 0.100). For this moderating variable, the heterogeneity among the groups was (QB) = 161.56 and p < 0.001, df(Q) = 4, suggesting that the observed variance between the calculated effect sizes for the moderators by strategy was significant. Therefore, there was a variation in motivation according to the type of teaching strategy implemented.
c. Effect on motivation according to “educational level” and “implementation time”
The recalculation of the effect sizes for motivation grouped by “educational level” was performed. The effect sizes were large and significant for secondary education with 0.921 (SD: 0.0836, CI: 0.757–1.085, p < 0.001), and very high for higher education with 1.295 (SD: 0.0838, CI: 1.130–1.459, p < 0.001). At the same time, the effect sizes were moderate for primary education with 0.772 (SD: 0.0910, CI: 0.593–0.950, p < 0.001) and intermediate education with 0.603 (SD: 0.1083, CI: 0.390–0.815, p < 0.001). The heterogeneity value among the groups was (QB) = 32.39 and p < 0.001, df(Q) = 3, concluding that the variance between the calculated effect sizes by educational level was significant. Therefore, there was a variation in motivation according to the educational level.
We calculated the effect sizes for motivation grouped according to the implementation period of the activity. The effect sizes were large and significant across all implementation periods. For 1 to 3 weeks, the effect size was 0.917 (SD: 0.0717, CI: 0.776–1.057, p < 0.001); for 4 to 6 weeks, it was 0.852 (SD: 0.0950, CI: 0.666–1.039, p < 0.001); for 7 to 9 weeks, it was 1.002 (SD: 0.1562, CI: 0.696–1.308, p < 0.001); and for 10 or more weeks, it was 1.075 (SD: 0.0882, CI: 0.902–1.248, p < 0.001). The heterogeneity value among the groups was (QB) = 5.46 and p = 0.141, df(Q) = 3; therefore, there was no significant heterogeneity between the different implementation periods regarding their effects on student motivation. Thus, there was no significant variation in motivation according to the implementation period.
d. Effect on motivational components
We performed the final calculation of the effect sizes for motivation grouped by the components of the ARCS model. The effect sizes were large and significant, supporting all dimensions of the ARCS model. For attention, it was 1.054 (SD: 0.0528, CI: 0.951–1.158, p < 0.001); for relevance, it was 0.517 (SD: 0.0517, CI: 0.749–0.952, p < 0.001); for confidence, it was 0.831 (SD: 0.0537, CI: 0.725–0.936, p < 0.001); and for satisfaction, it was 1.076 (SD: 0.0527, CI: 0.973–1.179, p < 0.001). The heterogeneity value among the groups was (QB) = 24.63 and p < 0.001, df(Q) = 3. Therefore, there was a significant variation in motivation according to the ARCS component.
4. Discussion
The ARCS model, proposed by Keller (1987), is recognized for its ability to enhance motivation in learning environments, especially when technology is integrated. According to Keller (2016), this approach boosts motivation by developing key aspects such as students’ attention, relevance, confidence, and satisfaction. However, the major challenge remains in improving academic performance, as motivation alone is not sufficient; Keller (1999) pointed out that academic success also depends on the implementation of effective teaching strategies and student-centered pedagogical approaches.
One of the findings of this study confirms this situation, where the implementation of VR in the classroom showed a very strong effect on motivation (ES: 1.316) but a small effect on academic performance (ES: 0.482). In the case of AR, the impact on motivation was also strong (ES: 0.974), but with moderate effects on academic performance (ES: 0.741). For AI use, the results also showed that although motivation increases significantly (ES: 0.841), academic performance only improves slightly (ES: 0.619). These findings suggest that, while emerging technologies have great potential to motivate students, it is also necessary to implement teaching strategies that encourage students’ direct interaction with the technologies and foster their autonomy to achieve better learning outcomes (Criollo-C et al., 2023; Chen, 2019; Erbas & Demirer, 2019).
The second finding was that no significant differences were observed in academic performance between virtual and face-to-face teaching modalities. This suggests that integrating emerging technologies with pedagogical models such as ARCS, combined with teaching strategies such as gamification, game-based learning, and project-based learning, can support meaningful learning achievement in both teaching modalities. Recent reports support this situation: UNESCO (2020) found that, under appropriate conditions, online students achieved academic results similar to those of face-to-face students; World Bank (2019) highlighted that virtual education can be equally effective, as long as interactive technologies and quality resources are used; OECD (2021) emphasized that the flexibility of online learning improves academic outcomes when there is personalized support. In this context, the role of artificial intelligence is fundamental, especially in virtual learning environments, as it allows for the implementation of adaptive learning systems that adjust the content and activities to students’ individual needs, enhancing the personalization of the educational process (Holmes et al., 2021; Khor & K, 2023). The adaptive learning systems developed in the first decade share certain conceptual principles with the current systems, such as personalizing learning based on students’ characteristics. However, modern systems have evolved considerably due to their integration with advanced technologies like big data, data mining, learning analytics, and new self-supervised machine learning models. This evolution has greatly expanded their capabilities, enabling a more in-depth analysis of educational data and offering significantly more sophisticated adaptation. Today, these advancements allow for the more precise and real-time adjustment of content and activities, along with the greater personalization of learning experiences. This includes aspects such as emotion recognition, the interpretation of micro-social interactions, and the use of voice commands with high speed and precision, as well as the combination of multiple interactive multimedia resources accessible from mobile devices (such as smartphones, iPhones, iPads, smartwatches, etc.). In contrast, traditional technological systems are significantly more limited in terms of access, scope, storage capacity, graphical processing, and overall functionality. In summary, the studies agree on the importance of effective pedagogical design, flexibility, and access to quality technological resources to equalize learning outcomes in both virtual and face-to-face modalities.
The third finding confirms that face-to-face teaching, especially when VR technologies are used, is more effective in stimulating student motivation compared to virtual environments. This suggests that factors such as social interaction, the collaborative environment, and the inherent structure of face-to-face learning could contribute to higher levels of student motivation (Hidalgo et al., 2021; Johnson et al., 2020; Martin et al., 2018). Among the strategies used in face-to-face settings, gamification is highly effective in enhancing student learning and motivation, consistent with the findings of Rayan and Watted (2024). In the virtual context, similar to the findings of Dichev and Dicheva (2017), it was found that incorporating VR with gamification can increase engagement by incorporating game elements into teaching. Another alternative is integrating AI-based adaptive learning to provide personalized and frequent feedback to each student, which also appears to increase motivation (Holmes et al., 2021; Nicol, 2018). Lastly, course design that includes multimedia elements and an intuitive interface also facilitates student participation (Bower, 2019).
The fourth relevant finding of this study concerns the use of the ARCS model combined with teaching strategies such as gamification, project-based learning (PBL), and the flipped classroom. Primarily, strategies like gamification, PBL, and game-based learning are effective in enhancing both student motivation and academic performance, whereas the flipped classroom still presents challenges in implementation. Regarding gamification, this study shows a very strong effect on motivation (ES: 3.365) and a strong effect on academic performance (ES: 1.126). These results are consistent with the findings of Rayan and Watted (2024) and Nicol (2018), which found that gamification, by introducing mechanisms such as rewards, challenges, and immediate feedback, transforms the learning process into a more engaging and motivating experience. Another strategy that showed a favorable effect on academic performance (ES: 1.07) was PBL. This finding aligns with the trends identified in the systematic reviews in Condliffe et al. (2017) and Akcay and Benek (2024), which concluded that PBL enhances the understanding and practical application of knowledge and fosters critical thinking and problem-solving abilities (Marcinauskas et al., 2024). Game-based learning has a positive effect on motivation (ES: 1.014), also reflecting the conclusions of Garris et al. (2002), which argued that educational games capture students’ attention by providing a more dynamic environment. They also emphasized that the design of these games should be integrated with learning objectives to improve both motivation and academic outcomes. In contrast, the flipped classroom results show negative effects on academic performance (ES: −0.37). This coincides with the findings of Lo et al. (2019), which noted that the success of the flipped classroom largely depends on students’ self-discipline and prior knowledge. Additionally, the flipped classroom had a weak effect on motivation (ES: 0.358), in line with the studies in Karabulut-Ilgu et al. (2018) and Köpeczi-Bócz (2024), which suggested that this strategy could be more effective if combined with collaborative activities and other methodologies like PBL, which promote active interaction during class time. In summary, strategies that integrate the ARCS model, such as gamification and PBL, seem to be the most effective in enhancing both academic performance and motivation. However, strategies like the flipped classroom require significant autonomous work from students and still present difficulties in their implementation.
The fifth finding is that XR technologies demonstrated a strong effect on academic performance in subjects related to the arts and natural sciences, particularly biology-related subjects. This suggests that these technologies are especially useful in subjects where the visualization of abstract objects and models (such as artwork, parts of the human body, volcanoes, planets, dinosaurs, etc.) plays a key role in learning González-Zamar and Abad-Segura (2020). Although all three emerging technologies had a positive impact on motivation, VR showed the best results, probably due to its ability to create highly immersive and interactive scenarios, with engaging and uncommon experiences for students, contributing to greater attention (C. Yang et al., 2024).
The sixth finding is that the effect sizes for academic performance were large and very similar in implementations lasting from 1 to 3 weeks and from 7 to 9 weeks. Meanwhile, the effects on motivation seem to increase with longer implementation periods. In this case, the largest effect sizes were observed in implementations of 7 weeks or more. These results could assist in the development of plans, programs, and other curricular recommendations.
We also observed that integrating emerging technologies seems to yield better learning outcomes for middle school students (ages 10 to 14) and those in the early years of university.
Finally, we found that all components of the ARCS model had a strong effect, especially in supporting students’ attention (ES: 1.054) and satisfaction (ES: 1.076). In this regard, the evidence suggests that emerging technologies boost motivation at the beginning of teaching activities and also during evaluation processes.
Overall, these findings are consistent with those of Goksu & Bolat (2021), but they also consider the teaching strategies and the use of emerging technologies. This aspect underscores the importance of well-planned instructional design, which considers not only the structure of the content but also innovative strategies and resources that support and enrich teaching in different settings and contexts.
5. Conclusions
After conducting this meta-analysis, it was possible to estimate the overall effects of the motivation learning model using emerging technologies such as artificial intelligence and extended reality on students’ academic performance and motivation. It was also possible to examine whether the estimated effects varied according to different moderators, such as the type of technology, teaching modality, subject, educational level, and implementation time. Additionally, the specific effects of the motivational model components were analyzed, including whether they differed from each other. A total of 172 weighted effect sizes were calculated from the 32 studies that constituted the final sample.
The effect of the motivational learning model on academic performance was moderate, significant, and without overestimation (ES: 0.596, SD: 0.078, 95% CI: 0.443–0.748, p < 0.001), while the overall effect size on motivation was large, significant, and without overestimation (ES: 0.886, SD: 0.1258, 95% CI: 0.640–1.133, p < 0.001). Both findings indicate that this motivational instructional approach with emerging technologies positively contributes to both performance and motivation, although it has a greater effect on students’ motivation.
Specifically, regarding academic performance, it was found that there were no significant differences between face-to-face and virtual teaching. The greatest effects on performance were observed with the use of extended reality and in subjects related to art and natural science learning. At the same time, it was found that the most effective teaching strategies to support academic performance arise from combining the ARCS model with gamification and project-based learning strategies. The ages at which academic performance was the highest were between 10 and 14 years. Additionally, implementation periods of 1 to 3 weeks showed the best results.
Regarding motivation, good results were observed with all three technologies; however, the use of virtual reality showed the best results. In-person teaching was found to be more effective in stimulating motivation compared to virtual teaching. Higher motivation levels were observed in subjects related to computer science, history, and natural sciences. The combination of ARCS teaching strategies with game-based learning and gamification showed the best results in motivation. The highest motivation levels were found in the age group of 14–18 years and in the early years of higher education. Moreover, all implementation periods showed high motivation, with those of 10 or more weeks presenting the best results.
Regarding the ARCS model components, strong effects were found across all components, with the highest being attention and satisfaction.
In conclusion, the application of the ARCS model in learning environments, alongside the use of emerging technologies, represents a key opportunity to increase students’ motivation and performance. However, the results indicate that while these tools help to improve motivation, they do not always translate into direct improvements in academic performance, highlighting the need for active learning-based teaching strategies.
In the context of this study, it was emphasized that combining the ARCS model with active methodologies such as gamification, project-based learning, and game-based learning was effective in achieving this goal and improving both motivation and performance. These strategies present challenges for implementation, with the role of the teacher as a facilitator being fundamental. The teacher must adapt, continuously assess, and maintain student motivation, tasks that complicate their roles across virtual and in-person environments. In online learning, the teacher must adjust their methods, providing constant support through feedback and interactive activities to maintain student engagement. In face-to-face settings, the teacher’s challenge lies in addressing direct interaction and real-time collaborative work, motivating and responding to individual needs within a group environment.
6. Limitations
The evidence included in this meta-analysis may have been influenced by the access to the databases from which the primary studies were obtained, potentially excluding several studies. At the same time, it should be considered that the use of artificial intelligence technology is an emerging topic in research; therefore, more results are likely published in conferences and the gray literature, which were also not included in the research databases.
Furthermore, in this study, we chose to conduct an exhaustive search without year restrictions to incorporate and integrate the results from as many educational experiences as possible. This approach can help to deepen future research addressing the advancement of technologies in educational settings, but it may also limit the interpretation of certain findings in educational contexts where newer emerging technologies are used.
It is also important to consider that although the search codes used aimed for broad coverage, it is still likely that relevant studies were omitted, particularly those using different terms to refer to emerging technologies or motivation. Furthermore, the six eligibility criteria used to select the final studies might also have excluded highly relevant studies in the area of interest in this research.
Conceptualization, J.A.; methodology, J.A., formal analysis, J.A.; investigation, J.A.; data curation, J.A.; writing—original draft preparation, J.A.; writing—review and editing, J.A. and M.L.A.; supervision, J.A.; project administration, J.A. and M.L.A.; funding acquisition, J.A. and M.L.A. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The raw data supporting the conclusions of this article will be made available by the authors on request.
Chilean National Research and Development Agency (ANID): BECAS/DOCTORADO NACIONAL grant number 21240783.
The authors declare no conflicts of interest.
Footnotes
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Studies organized by author, year, database, journal, and country.
| ID | Authors and Year | Country | Database | Journal/Conference |
|---|---|---|---|---|
| 1 | United States | ERIC | Educational Technology Research and Development | |
| 2 | Taiwan | ERIC | Computers & Education | |
| 3 | Spain | WoS | Computers & Education | |
| 4 | Taiwan | WoS | Interactive Learning Environments | |
| 5 | Taiwan | ERIC | Journal of Computer Assisted Learning | |
| 6 | China | WoS | Computers & Education | |
| 7 | Taiwan | ERIC | Journal of Computer Assisted Learning | |
| 8 | Taiwan | WoS | 1st International Cognitive Cities Conference | |
| 9 | Spain | WoS | Interactive Learning Environments | |
| 10 | Taiwan | WoS | Interactive Learning Environments | |
| 11 | Taiwan | WoS | Interactive Learning Environments | |
| 12 | Taiwan | ERIC | Sustainability | |
| 13 | Indonesia | Scopus | Advances in Science, Technology and Engineering Systems | |
| 14 | Taiwan | WoS | Sustainability | |
| 15 | Taiwan | WoS | IEEE Transactions on Learning Technologies. | |
| 16 | Spain | ERIC | Computer Assisted Language Learning | |
| 17 | Taiwan | ERIC | International Journal of Instruction | |
| 18 | China | WoS | Educational Technology Research and Development | |
| 19 | Taiwan | WoS | Sustainability | |
| 20 | China | ERIC | Electronics | |
| 21 | Taiwan | WoS | Sensors | |
| 22 | Turkey | Scopus | Participatory Educational Research | |
| 23 | Germany | WoS | Conference on Intelligent Tutoring Systems | |
| 24 | China | WoS | Medicine (Baltimore) | |
| 25 | Taiwan | WoS | British Journal of Educational Technology | |
| 26 | Mexico | WoS | Information | |
| 27 | Finland | ERIC | British Journal of Educational Technology | |
| 28 | Spain | Scopus | Electronics | |
| 29 | Arabia | WoS | Electronics | |
| 30 | Taiwan | ERIC | Journal of Science Education and Technology | |
| 31 | Chile | Scopus | Education and Information Technologies | |
| 32 | China | WoS | Language Learning & Technology |
Articles organized by type of technology used, modality, subject, teaching strategy, level, and implementation time.
| ID | Technology | Modality | Subject | Strategy | Level | Time |
|---|---|---|---|---|---|---|
| 1 | AI Adaptative | Virtual | Natural Sciences (Biology) | ARCS | Secondary | 1 |
| 2 | IoT and AR | Virtual | Language (English) | ARCS + GBL | Intermediate | 8 |
| 3 | AR | In-person | Visual Arts | ARCS | Secondary | 2 |
| 4 | Robot AI | Virtual | Language (English) | ARCS | College | 3 |
| 5 | IoT and AR | In-person | Natural Sciences (Ecology) | Gamification | Intermediate | 3 |
| 6 | AR | In-person | Desing And Arts | PBL | Secondary | 1 |
| 7 | AI Adaptative | In-person | Language (English) | GBL | College | 7 |
| 8 | AR | In-person | Language (English) | GBL | Primary | NA |
| 9 | VR and AI Tutor | In-person | Social Sciences (History) | ARCS | Primary | 1 |
| 10 | AR | In-person | Language (English) | GBL | Primary | 4 |
| 11 | AR | In-person | Natural Sciences (Paleontology) | GBL | Intermediate | 1 |
| 12 | Machine Vision | In-person | Language (English) | ABE | Primary | 3 |
| 13 | IA Adaptative | Virtual | Language (Korean) | Gamification | College | 2 |
| 14 | Robot with AI | In-person | Mathematics (Geometry) | ARCS | Secondary | 18 |
| 15 | AR | In-person | Social Sciences (Patrimony) | ARCS | College | 1 |
| 16 | AI Adaptative | In-person | Language (English) | ARCS | Primary | 5 |
| 17 | AR | In-person | Language (English) | ARCS | Primary | 4 |
| 18 | AR | In-person | Mathematics (Geometry) | ARCS | Intermediate | 1 |
| 19 | AR and VR | In-person | Natural Sciences (Health) | GBL | Secondary | 1 |
| 20 | AR | Hybrid | Natural Sciences (Oceanography) | ARCS | Secondary | 8 |
| 21 | AR | In-person | Computer Sciences | ARCS | Intermediate | 1 |
| 22 | AR | In-person | Computer Sciences | ARCS | Secondary | 2 |
| 23 | Tutoring IA | Virtual | Language (German) | ARCS | Primary | 1 |
| 24 | Tutoring IA | Hybrid | Natural Sciences (Bioinformatics) | ARCS | College | 12 |
| 25 | VR | In-person | Natural Sciences (Prevention) | ARCS | College | 1 |
| 26 | AR, Tutoring AI | Virtual | Mathematics (Geometry) | ARCS | Secondary | 1 |
| 27 | VR | In-person | Natural Sciences (Evolution) | ARCS | Secondary | 5 |
| 28 | AR | In-person | Language (English) | ARCS | Secondary | 4 |
| 29 | VR | In-person | Natural Sciences (Anatomy) | Gamification | College | 12 |
| 30 | Robot with IA | In-person | Natural Sciences and Mathematics | PBL | Intermediate | 12 |
| 31 | AR and AI | Hybrid | Mathematics (Economy) | ARCS | College | 7 |
| 32 | AI Chatbot | Hybrid | Language (English) | Flipped Class | College | 12 |
Appendix A. Statistical Results for Academic Performance and Motivation in Control and Experimental Groups
| Control Groups | Motivational Components | Academic Performance | |||||||||||
| ID | N | A | A | R | R | C | C | S | S | Global | Global | Global | Global |
| 1 | 19 | 10 | 3.96 | 11 | 3.64 | 11.95 | 4.02 | 10.42 | 4.22 | 2.63 | 0.96 | 6.68 | 3.35 |
| 1 | 19 | 5.84 | 3.11 | ||||||||||
| 2 | 64 | 3.47 | 0.76 | 4.09 | 0.73 | 3.34 | 0.6 | 3.41 | 0.61 | 3.75 | 0.62 | 81.25 | 9.59 |
| 3 | 55 | 3.28 | 0.55 | 3.31 | 0.47 | 3.4 | 0.53 | 3.11 | 0.68 | 3.29 | 0.46 | ||
| 4 | 30 | 3.38 | 0.5087 | 1.164 | 0.209 | ||||||||
| 5 | 34 | 75.59 | 9.595 | ||||||||||
| 5 | 34 | 74.71 | 8.956 | ||||||||||
| 6 | 16 | 2.68 | 0.513 | 3.05 | 0.785 | 3.79 | 0.459 | 2.59 | 0.494 | 3.03 | 0.58 | 3.08 | 0.632 |
| 6 | 16 | 3.31 | 0.587 | ||||||||||
| 6 | 16 | 3.13 | 0.537 | ||||||||||
| 7 | 30 | 2.53 | 0.63 | 2.2 | 0.44 | 2.58 | 0.38 | 2.58 | 0.38 | 2.4725 | 0.4575 | 69.17 | 3.168 |
| 8 | 22 | 75.72 | 17.3 | ||||||||||
| 9 | 53 | 3.91 | 0.74 | 3.72 | 0.79 | 4.15 | 0.64 | 4.38 | 0.7 | 4.04 | 0.52 | 10.49 | 5.43 |
| 10 | 76 | 3.07 | 0.93 | 3.24 | 0.9 | 3.16 | 0.95 | 3.3 | 0.9 | 3.1925 | 0.92 | 81.4 | 21.5 |
| 11 | 28 | 4.13 | 0.67 | 3.86 | 0.66 | 3.96 | 0.62 | 4.03 | 0.71 | 3.99 | 0.56 | 88.43 | 2.31 |
| 12 | 35 | 4.51 | 0.715 | 4.518 | 0.707 | 4.535 | 0.706 | 4.640 | 0.592 | 4.55 | 0.68 | 69.143 | 11.973 |
| 13 | 33 | 3.08 | 0.56 | 68.85 | 29.93 | ||||||||
| 14 | 25 | 1.8 | 0.54 | 2.12 | 0.57 | 2.43 | 0.56 | 2.02 | 0.68 | 2.0925 | 0.5875 | ||
| 15 | 30 | 3.31 | 0.248 | 3.51 | 0.281 | 3.53 | 0.218 | 3.27 | 0.275 | 3.37 | 0.183 | 28.27 | 5.179 |
| 15 | 30 | 41.06 | 8.464 | ||||||||||
| 16 | 68 | 4 | 1 | 4 | 1.33 | 4 | 1 | 4.33 | 1.33 | 16.67 | 4.33 | 7.5 | 2.5 |
| 16 | 68 | 5 | 2.5 | ||||||||||
| 17 | 20 | 2.19 | 0.321 | 1.97 | 0.606 | 2.21 | 0.256 | 2.13 | 0.494 | 2.125 | 0.41925 | 41.8 | 11.05 |
| 18 | 9 | 3.2 | 0.37 | 3.58 | 0.32 | 3.26 | 0.39 | 3.82 | 0.36 | 3.47 | 0.36 | 13.78 | 1.48 |
| 18 | 9 | 3.2 | 0.37 | 3.58 | 0.32 | 3.26 | 0.39 | 3.82 | 0.36 | 3.47 | 0.36 | 13.78 | 1.48 |
| 19 | 25 | 3.66 | 0.528 | 3.65 | 0.569 | 3.69 | 0.621 | 3.66 | 0.582 | 3.665 | 0.5726 | ||
| 20 | 11 | 3.84 | 0.868 | 3.86 | 0.936 | 4.07 | 0.918 | 3.96 | 0.972 | 39.325 | 0.9235 | 63.18 | 12.32 |
| 21 | 20 | 3.55 | 0.99 | 3.59 | 0.62 | 3.96 | 0.74 | 4.08 | 0.56 | 3.8 | 0.65 | 85.75 | 2.487 |
| 22 | 47 | 2.43 | 0.3 | 2.43 | 0.3 | 2.31 | 0.25 | 2.31 | 0.25 | 2.37 | 0.28 | 20.98 | 11.03 |
| 23 | 12 | 4.61 | 0.51 | 4.56 | 0.48 | 4.44 | 0.59 | 4.36 | 0.78 | 4.49 | 0.33 | 29.33 | 4.64 |
| 23 | 12 | 43.67 | 3.94 | ||||||||||
| 24 | 48 | 1.8 | 0.88 | 2.5 | 0.39 | 2.3 | 0.59 | 2.1 | 0.63 | 2.175 | 0.6225 | 84.73 | 8.34 |
| 24 | 48 | 80.56 | 13.67 | ||||||||||
| 25 | 35 | 70 | 9 | ||||||||||
| 26 | 53 | 4.23 | 0.333 | 4.24 | 0.331 | 4.04 | 0.343 | 4.3 | 0.322 | 4.24 | 0.032 | 6.83 | 1.424 |
| 27 | 35 | 3.16 | 0.93 | 3.14 | 0.96 | 3.05 | 0.99 | 3.13 | 0.95 | 0.67 | 0.13 | ||
| 27 | 35 | 0.53 | 0.29 | ||||||||||
| 27 | 35 | 0.64 | 0.16 | ||||||||||
| 28 | 66 | 3.34 | 1.05 | 70.3 | 24.91 | ||||||||
| 29 | 44 | 2.9 | 0.16 | 3.2 | 0.16 | 2.9 | 0.16 | 3.25 | 0.16 | 3.1 | 0.16 | ||
| 29 | 44 | 2.9 | 0.16 | 3.2 | 0.16 | 2.9 | 0.16 | 3.25 | 0.16 | 3.1 | 0.16 | ||
| 30 | 52 | 2.97 | 0.69 | 45 | 16.66 | ||||||||
| 31 | 60 | 3.49 | 1.06 | 3.69 | 1.05 | 3.33 | 1.07 | 3.36 | 1.16 | 3.36 | 1.16 | ||
| 32 | 41 | 5.63 | 1.21 | 17.59 | 6.51 | ||||||||
| Experimental Groups | Motivational Components | Academic Performance | |||||||||||
| ID | N | A | A | R | R | C | C | S | S | Global | Global | Global | Global |
| 1 | 22 | 13.36 | 3.36 | 12.5 | 3.47 | 13.95 | 3.32 | 12.95 | 3.92 | 3.18 | 1.1 | 7.18 | 2.5 |
| 1 | 22 | 7.18 | 2.5 | ||||||||||
| 2 | 64 | 4.22 | 0.66 | 4.16 | 0.68 | 4.03 | 0.74 | 4.06 | 0.88 | 4.16 | 0.72 | 89.44 | 7.45 |
| 3 | 55 | 3.76 | 0.72 | 3.48 | 0.47 | 3.63 | 0.57 | 3.51 | 0.59 | 3.62 | 0.5 | ||
| 4 | 32 | 3.68 | 0.3736 | 1.815 | 0.203 | ||||||||
| 5 | 34 | 82.94 | 10.01 | ||||||||||
| 5 | 34 | 82.94 | 10.01 | ||||||||||
| 6 | 17 | 4.38 | 0.48 | 3.93 | 0.63 | 4.02 | 0.583 | 4.25 | 0.481 | 4.15 | 0.55 | 4.16 | 0.583 |
| 6 | 17 | 3.73 | 0.616 | ||||||||||
| 6 | 17 | 4.3 | 0.461 | ||||||||||
| 7 | 32 | 4.44 | 0.56 | 3.96 | 0.37 | 4.5 | 0.46 | 4.56 | 0.48 | 4.365 | 0.4675 | 72.22 | 4.449 |
| 8 | 24 | 85.56 | 14.41 | ||||||||||
| 9 | 45 | 4.56 | 0.45 | 4.59 | 0.63 | 3.91 | 0.63 | 4.8 | 0.36 | 4.47 | 0.31 | 13.46 | 5.14 |
| 10 | 71 | 4.51 | 0.71 | 4.52 | 0.68 | 4.54 | 0.63 | 4.46 | 0.73 | 4.5 | 0.68 | 84.78 | 21.28 |
| 11 | 25 | 4.29 | 0.7 | 3.89 | 0.74 | 3.81 | 0.59 | 4.22 | 0.71 | 4.04 | 0.62 | 96 | 1.65 |
| 12 | 35 | 4.59 | 0.65 | 4.676 | 0.58 | 4.588 | 0.53 | 4.706 | 0.629 | 4.64 | 0.603 | 76.143 | 9 |
| 13 | 33 | 4.19 | 0.46 | 93.61 | 8.56 | ||||||||
| 14 | 25 | 3.35 | 0.71 | 3.46 | 0.86 | 3.77 | 0.68 | 3.36 | 0.72 | 3.485 | 0.7425 | ||
| 15 | 32 | 3.64 | 0.16 | 3.69 | 0.26 | 3.77 | 0.166 | 3.68 | 0.215 | 3.66 | 0.146 | 31.53 | 3.75 |
| 15 | 32 | 45.25 | 3.724 | ||||||||||
| 16 | 68 | 4.17 | 1 | 4 | 1.25 | 4 | 1.33 | 4.67 | 1 | 17.17 | 3.33 | 7.5 | 2.5 |
| 16 | 68 | 6.25 | 2.5 | ||||||||||
| 17 | 22 | 3.89 | 0.22 | 4.06 | 0.44 | 3.95 | 0.373 | 3.84 | 0.382 | 3.935 | 0.356 | 76.1 | 12.05 |
| 18 | 8 | 3.1 | 0.23 | 3.78 | 0.52 | 3.29 | 0.14 | 4.48 | 0.62 | 4 | 0.377 | 11.37 | 11.76 |
| 18 | 10 | 3.3 | 0.27 | 4.3 | 0.26 | 3.66 | 0.51 | 4.58 | 0.42 | 3.96 | 0.365 | 14.7 | 1.33 |
| 19 | 27 | 4.08 | 0.47 | 4.05 | 0.47 | 4.07 | 0.51 | 4.08 | 0.499 | 4.07 | 0.4885 | ||
| 20 | 11 | 4.08 | 0.95 | 4.3 | 0.72 | 4.31 | 0.807 | 4.29 | 0.824 | 4.245 | 0.82775 | 69.55 | 12.7 |
| 21 | 22 | 4.52 | 0.85 | 4.26 | 0.79 | 4.7 | 0.62 | 4.71 | 0.59 | 4.55 | 0.72 | 91.68 | 1.545 |
| 22 | 47 | 4.13 | 0.41 | 4.13 | 0.41 | 4.25 | 0.19 | 4.25 | 0.19 | 4.19 | 0.3 | 20.98 | 7.86 |
| 23 | 13 | 4.62 | 0.58 | 4.56 | 0.76 | 4.49 | 0.54 | 4.41 | 0.66 | 4.52 | 0.58 | 31.62 | 6.38 |
| 23 | 13 | 55.69 | 6.03 | ||||||||||
| 24 | 51 | 3.38 | 0.78 | 3.73 | 0.84 | 3.79 | 0.64 | 3.33 | 0.71 | 35.575 | 0.7425 | 90.25 | 6.78 |
| 24 | 51 | 87.45 | 12.03 | ||||||||||
| 25 | 35 | 80 | 10 | ||||||||||
| 26 | 53 | 4.42 | 0.35 | 4.31 | 0.32 | 4.19 | 0.377 | 4.46 | 0.311 | 4.36 | 0.346 | 7.47 | 1.601 |
| 27 | 35 | 4.11 | 0.4 | 3.97 | 0.42 | 4.17 | 0.43 | 4.08 | 0.41 | 0.69 | 0.12 | ||
| 27 | 35 | 0.59 | 0.2 | ||||||||||
| 27 | 35 | 0.68 | 0.14 | ||||||||||
| 28 | 64 | 3.75 | 0.89 | 83.7 | 17.62 | ||||||||
| 29 | 44 | 4.6 | 0.08 | 4.5 | 0.08 | 4.4 | 0.08 | 4.4 | 0.08 | 4.45 | 0.08 | ||
| 29 | 126 | 4.6 | 0.09 | 4.6 | 0.09 | 4.8 | 0.09 | 4.58 | 0.09 | 4.645 | 0.09 | ||
| 30 | 51 | 3.29 | 0.7 | 59.04 | 20.31 | ||||||||
| 31 | 178 | 3.75 | 1.15 | 3.77 | 1.1 | 3.73 | 1.11 | 3.94 | 1.05 | 3.94 | 1.05 | ||
| 32 | 45 | 13.36 | 3.36 | 12.5 | 3.47 | 13.95 | 3.32 | 12.95 | 3.92 | 3.18 | 1.1 | 17.33 | 7.35 |
Appendix B. Forest Plot for ARCS and Motivation Components
Appendix C. Effect Sizes for Academic Performance, According to Moderators
| Moderator | n | ES | Interpretation | SD | Z | Sig. Bilateral | 95% | ||
| IC Inf. | IC Sup. | ||||||||
| Technologies | AI | 18 | 0.619 | Medium | 0.0547 | 11.328 | <0.001 | 0.512 | 0.727 |
| AR | 21 | 0.741 | Medium | 0.0628 | 11.813 | <0.001 | 0.618 | 0.864 | |
| VR | 6 | 0.482 | Small | 0.0964 | 4.999 | <0.001 | 0.293 | 0.671 | |
| Modalities | In person | 27 | 0.63 | Medium | 0.048 | 13.131 | <0.001 | 0.536 | 0.724 |
| Virtual or hybrid | 13 | 0.632 | Medium | 0.07 | 9.031 | <0.001 | 0.495 | 0.769 | |
| Subjects | Natural Sciences | 12 | 0.644 | Medium | 0.0716 | 9.988 | <0.001 | 0.503 | 0.784 |
| Language | 4 | 0.548 | Medium | 0.0581 | 9.426 | <0.001 | 0.434 | 0.661 | |
| Mathematics | 6 | 0.463 | Small | 0.1186 | 3.906 | <0.001 | 0.231 | 0.696 | |
| Computer Sciences | 3 | 0.517 | Medium | 0.1716 | 3.011 | <0.005 | 0.18 | 0.853 | |
| Arts and design | 3 | 1.603 | Very large | 0.1757 | 9.122 | <0.001 | 1.259 | 1.948 | |
| History | 3 | 0.63 | Medium | 0.1738 | 4.574 | <0.001 | 0.36 | 0.9 | |
| Teaching Strategies | ARCS | 24 | 0.561 | Medium | 0.0516 | 10.876 | <0.001 | 0.46 | 0.662 |
| ARCS + GBL | 4 | 0.739 | Medium | 0.1088 | 6.797 | <0.001 | 0.526 | 0.953 | |
| ARCS + PBL | 4 | 1.07 | Large | 0.1532 | 6.982 | <0.001 | 0.769 | 1.37 | |
| ARCS + Gamification | 3 | 1.126 | Large | 0.2886 | 3.901 | <0.001 | 0.56 | 1.691 | |
| ARCS + Flipped Class | 1 | −0.37 | Small | 0.2159 | −0.2159 | −0.461 | −0.461 | 0.386 | |
| Educational level | Primary (6–10) | 9 | 0.423 | Small | 0.0767 | 5.518 | <0.001 | 0.273 | 0.574 |
| Intermediary (10–14) | 8 | 1.026 | Large | 0.0986 | 10.405 | <0.001 | 0.833 | 1.219 | |
| Secondary (14-18) | 12 | 0.444 | Small | 0.0753 | 5.895 | <0.001 | 0.296 | 0.591 | |
| College (18+) | 9 | 0.768 | Medium | 0.082 | 9.368 | <0.001 | 0.608 | 0.929 | |
| Weeks | 1 to 3 Weeks | 22 | 0.844 | Large | 0.0628 | 13.438 | <0.001 | 0.732 | 0.967 |
| 4 to 6 Weeks | 8 | 0.354 | Medium | 0.0722 | 4.902 | <0.001 | 0.212 | 0.496 | |
| 7 to 9 Weeks | 3 | 0.855 | Large | 0.1437 | 5.95 | <0.001 | 0.573 | 1.137 | |
| 10+ | 4 | 0.509 | Medium | 0.1040 | 4.893 | <0.001 | 0.305 | 0.712 | |
Appendix D. Effect Sizes for Motivation, According to Moderators
| Moderator | n | ES | Interpretation | SD | Z | Sig. Bilateral | 95% | ||
| IC Inf. | IC Sup. | ||||||||
| Technologies | AI | 16 | 0.841 | Large | 0.0591 | 14.226 | <0.001 | 0.725 | 0.957 |
| AR | 15 | 0.974 | Large | 0.0688 | 14.15 | <0.001 | 0.839 | 1.109 | |
| VR | 6 | 1.316 | Large | 0.1205 | 10.923 | <0.001 | 1.08 | 1.552 | |
| Modalities | In person | 25 | 1.115 | Large | 0.0553 | 19.982 | <0.001 | 0.997 | 1.213 |
| Virtual or hybrid | 10 | 0.733 | Medium | 0.0715 | 10.247 | <0.001 | 0.593 | 0.873 | |
| Subjects | Natural Sciences | 9 | 1.024 | Large | 0.0851 | 12.026 | <0.001 | 0.857 | 1.191 |
| Language | 12 | 0.773 | Medium | 0.0686 | 11.272 | <0.001 | 0.639 | 0.907 | |
| Mathematics | 6 | 0.679 | Medium | 0.0954 | 7.113 | <0.001 | 0.492 | 0.866 | |
| Computer Sciences | 3 | 1.988 | Very large | 0.2324 | 8.551 | <0.001 | 1.532 | 2.443 | |
| Arts and design | 2 | 0.914 | Large | 0.1782 | 5.132 | <0.001 | 0.565 | 1.264 | |
| History | 2 | 1.248 | Large | 0.1744 | 7.156 | <0.001 | 0.906 | 1.59 | |
| Teaching Strategies | ARCS | 19 | 0.879 | Large | 0.0585 | 15.027 | <0.001 | 0.764 | 0.994 |
| ARCS + GBL | 6 | 1.014 | Large | 0.1002 | 10.121 | <0.001 | 0.818 | 1.211 | |
| ARCS + PBL | 2 | 0.735 | Medium | 0.1807 | 4.069 | <0.001 | 0.381 | 1.09 | |
| ARCS + Gamification | 4 | 3.365 | Very large | 0.2002 | 16.804 | <0.001 | 2.972 | 3.757 | |
| ARCS + Flipped Class | 1 | 0.358 | Medium | 0.2176 | 1.647 | 0.1 | −0.068 | 0.785 | |
| Educational level | Primary (6–10) | 7 | 0.772 | Medium | 0.091 | 8.482 | <0.001 | 0.593 | 0.95 |
| Intermediary (10–14) | 6 | 0.603 | Medium | 0.1083 | 5.562 | <0.001 | 0.39 | 0.815 | |
| Secondary (14-18) | 10 | 0.921 | Large | 0.0836 | 11.015 | <0.001 | 0.757 | 1.085 | |
| College (18+) | 6 | 1.295 | Very large | 0.0838 | 15.455 | <0.001 | 1.13 | 1.459 | |
| Weeks | 1 to 3 Weeks | 16 | 0.917 | Large | 0.0717 | 12.781 | <0.001 | 0.776 | 1.057 |
| 4 to 6 Weeks | 5 | 0.852 | Large | 0.095 | 8.967 | <0.001 | 0.666 | 1.039 | |
| 7 to 9 Weeks | 3 | 1.002 | Large | 0.1562 | 6.417 | <0.001 | 0.696 | 1.308 | |
| 10+ | 8 | 1.075 | Large | 0.0882 | 12.185 | <0.001 | 0.902 | 1.248 | |
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Abstract
To contribute to the design of better teaching strategies mediated by emerging technologies, the aim of this systematic review and meta-analysis was to estimate the effect sizes of the attention, relevance, confidence, and satisfaction (ARCS) motivational model with technologies such as AI and XR regarding academic performance and student motivation. From a sample of 2656 studies obtained from WoS, Scopus, ERIC, and APAPsycNet, 32 primary studies with quasi-experimental designs were selected, where the ARCS model and some types of emerging technology were used. To estimate the possible risks of bias and overestimation, preliminary tests with funnel plots were used. The effect sizes were calculated with Cohen’s d using random-effects models. Moderations were also examined using fixed-effects models and heterogeneity tests. The results showed a moderate effect on academic performance (ES: 0.596, 95% CI: 0.443–0.748) and a strong effect on motivation (ES: 0.886, 95% CI: 0.640–1.133), both with low bias. According to the moderator analysis on academic performance, no significant differences were found between face-to-face and virtual teaching. Furthermore, the greatest effects on academic performance were observed when using AI and XR in subjects like the natural sciences and arts and when combining the motivational model with strategies such as gamification and project-based learning. Finally, gamification and game-based learning proved to be an effective strategy to increase motivation.
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; Arancibia, María Luisa 2 1 Doctor of Education Program, Faculty of Social Sciences, University of Chile, Santiago 7800284, Chile
2 Center for Advanced Research in Education, Institute of Education, University of Chile, Santiago 8330014, Chile