Since learning theories have been often ignored in translation education, the present study aimed to explore the impact of implementing principles of connectivism learning theory in translation training using an AI-powered translation tool, Matecat. Participants were thirty third-year students who enrolled in a course on the translation of Islamic texts from English. Before the commencement of the course, a pretest was given to the students to assess their translation skills. Then, on the basis of the results, two groups of experimental and control were formed. The homogeneity of the two groups was further checked by using independent samples t-test in SPSS. Unlike the control group, the experimental group was trained on the basis of the principles of connectivism and the tailored model designed for the present study. At the end of the program a posttest was administered, and the scores were subjected to statistical analysis using independent samples t-test. The results showed although both groups had started at more or less the same level, the quality of translations produced by the experimental group improved significantly more than that of the control group. Furthermore, the experimental group outperformed the control group in cohesion and coherence, structure, style and cultural aspects. In fact, the findings indicated that employing AI-powered translation tools per se will not lead to a significant improvement in learners' translation quality unless the training is integrated with pedagogical application of a digital age learning theory, such as connectivism.

Keywords: learning theories, connectivism, translation training, translation tools, Artificial Intelligence

     

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