Considering noticeable improvements in the accuracy of Google Translate recently, the aim of this study was to examine second language (L2) learners' ability to use post-editing (PE) strategies when applying AI tools such as the neural machine translator (MT) to solve their lexical and grammatical problems during L2 writing. This study examined 57…

While previous investigations on online machine translation (MT) in language learning have analyzed how second language (L2) learners use and post-edit MT output, no study as of yet has investigated how the learners process MT errors and what factors affect this process using response and reading times. The present study thus investigates L2…

This study illustrates how a synergy of two technologies--Intelligent Computer-Assisted Language Learning (ICALL) and corpus linguistic analysis--can produce a lasting improvement in L2 learners' ability to edit persistent grammatical errors from their writing. A large written English corpus produced by Korean undergraduate and graduate students…

This study focuses on the computer-aided analysis of interlanguage errors made by the participants in the telematic simulation IDEELS (Intercultural Dynamics in European Education through on-Line Simulation). The synchronous and asynchronous communication analysed was part of the MiLC Corpus, a multilingual learner corpus of texts written by…