This paper examined the influence of an anthropomorphic VRT on students’ computational intelligence and English language skills in a technology-based learning environment. Pre-post study involving 68 subjects revealing significant changes in the scores: The mean pre-test being 47.53 (SD = 2.60) and post-test score of 86.82 (SD = 3.58); instrument reliability was noted at Cronbach's α = 0.839. ANOVA showed a between-group effect (F(13,54) = 8.513, p <. 001) with a high amount of between-group variance (η² ≈ 0.67), whereas t-tests revealed accurate mean estimates at both measurement occasions. The interpretation of the findings provided evidence that VRT led to robust and consistent learning gains in the computational domain due to three core mechanisms: 1) adaptive scaffolding which diminished cognitive load as the range of solving strategies increased; 2) anthropomorphic social support, leading to greater motivation and propensity to engage with characters during learning activities; and 3) deliberative practice cycles which included immediate feedback with an adaptive task generator. Furthermore, by incorporating language-aware scaffolding, such as cohesion correction, rubric creation or reflective prompts in English into the framework we can ensure that computational competencies transfer to the learning of languages with learners' development both disciplinarily and linguistically being mutually supportive. In this study, anthropomorphic VRT shown some promise as a pedagogical practice for scaling up high quality teaching practices in the era of AIIoT, especially towards fostering computational intelligence and second language proficiency.
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