This study examines the impact of rewards on motivation and academic performance in digital game-based learning (DGBL) for teaching English as a foreign language to primary school students in Kazakhstan. The main objective is to evaluate how incentive systems influence student engagement and achievement, addressing a gap in the existing literature on the effectiveness of rewards in educational environments. Using a quasi-experimental design, 46 fourth-grade students were divided into a control group that received traditional instruction and an experimental group that engaged in DGBL with rewards. Data were collected through questionnaires measuring motivation and academic performance before and after the intervention. The results showed that students in the experimental group demonstrated significantly higher motivation and better educational outcomes than the control group. This study concludes that rewards can effectively enhance motivation and performance in DGBL, providing practical recommendations for educators to incorporate rewards in a balanced manner to sustain long-term engagement and learning success. Future research should explore the long-term effects of such interventions in different educational contexts.

The current research investigates the influence of gamification and artificial intelligence (AI) on the motivation and efficacy of primary school students in acquiring English language skills. With the increasing prevalence of digital technologies in education, there is a growing need to explore innovative approaches that engage students and foster a positive learning environment. The study hypothesizes that the combination of gamification and AI will positively influence motivation and efficacy in English language learning. The study utilized a mixed-methods research approach, where an experimental group received English language teaching through gamified learning platforms enhanced by AI, while a control group received conventional tuition. The quantitative data obtained from pre- and post-assessments clearly showed that the experimental group shown greater levels of engagement, enjoyment, and intrinsic motivation, resulting in enhanced language proficiency outcomes in comparison to the control group. The qualitative analysis demonstrated that the experimental group experienced heightened self-assurance, a feeling of accomplishment, and a favorable disposition towards learning English. The utilization of gamification, along with the assistance of artificial intelligence, enabled customized learning experiences that were engaging and immersive, promoting motivation and active involvement. The results emphasize the possibility of combining gamification and AI in teaching English to primary school students to improve motivation and effectiveness, underscoring the significance of innovative methods in educational environments.

Artificial intelligence (AI) and gamification are increasingly combined in language learning, and learners' acceptance of such tools is typically measured with multidimensional questionnaires whose dimensional structure is rarely tested. This study asked whether the commonly assumed subdimensions of acceptance of AI-gamified adaptive learning are empirically separable in English-as-a-foreign-language (EFL) learners, or whether acceptance is better understood as a general evaluative orientation. Using a 13-item instrument, survey data from 401 EFL students at a Kazakhstani university were analyzed with reliability analysis, exploratory factor analysis with parallel analysis, competing confirmatory models (one-factor, four-factor, second-order, and bifactor), the heterotrait–monotrait (HTMT) ratio, bifactor dimensionality indices, ordinal (WLSMV) sensitivity analysis, and independent confirmation on a held-out subsample. Parallel analysis (exploratory half) retained a single factor. Although a strict one-factor model fitted poorly, the four-factor, second-order, and bifactor models fitted better; however, the subdimensions lacked discriminant validity (HTMT up to .94; latent correlations up to .95 under WLSMV). A bifactor analysis showed a dominant general factor (ECV = .82; ωh = .93; PUC = .81), and the bifactor model was not identifiable under WLSMV, consistent with very weak specific factors (although non-identification can also reflect model instability). Formal comparisons (χ ² difference tests and log-likelihood-based information criteria) favored the bifactor representation, and an S-1 bifactor diagnostic implicated the effectiveness-specific factor as a major contributor to the WLSMV non-identification. A Harman test (first factor = 66.5%), together with the uniformly positive, single-occasion, self-report design, suggests that common-method variance may also contribute to this generality. The evidence supports scoring this 13-item measure as a single, possibly method-influenced general factor and warns against interpreting fine-grained subscales, with perceived gamification the only partly distinguishable facet. These findings, obtained from a predominantly female sample of prospective English teachers at one Kazakhstani university, provide structural validity evidence only; nomological and external-criterion validity were not examined and remain the necessary next step.