Bridging the Language Gap: Enhancing Academic Performance of Non-Native Students with AI-Powered Translation
DOI:
https://doi.org/10.56294/saludcyt20251426Keywords:
Neural Machine Translator, Convergent Parallel Mixed Method, Cohen’s d, Effect Size ChangeAbstract
Introduction: Disparity between language ability of non-native students and the medium of instruction used in host country education institutions can limit non-native students’ (NNES) ability to be fully engaged in learning and if not addressed can pose serious ramifications on affected students’ psychological wellbeing leading to poor academic performance and unwanted dropout.
Objective: This study uses empirical evidence to establish the effectiveness of AI neural machine translators in enhancing non-native students' academic performance through improved learner-content and learner-instructor interactions.
Methods: Convergent parallel mixed method was employed over two trials simultaneously collected quantitative and qualitative data from samples that were segregated into similar proportions of control and experiment sub-population strata according to participants’ language ability. Qualitative semi-structured interviews were conducted during the diagnostic and summative stage complemented by weekly formative quantitative assessment over a span of 10 weeks per trial.
Results: Findings from Cohen’s d’s effect size change inferred NNES with low language ability benefited the most. However, lesser effects were found on NNES with high language ability.
Conclusions: While NMT shows promise in enhancing learning interactions educators should exercise discretion to avoid deleterious effect on host country students and NNES with higher language abilities.
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