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With the growth of chatbots, concerns about implementing artificial intelligence (AI) chatbots in educational settings have consistently arisen, especially for the purpose of language learning. This study introduced a task-based voice chatbot called “Ellie”, newly developed by the researchers, and examined the appropriateness of its task design and performance as an English conversation partner and students’ perceptions on using it in EFL class. Korean EFL learners (N = 314) aged 10–15 years performed three speaking tasks with Ellie in their school classroom. The participants took 9.63 turns per session on average using the first 1,000-word band, indicating that the chatbot highly encouraged students to engage in conversation, which rarely occurs in general EFL classes in Korea. The high task success rates (88.3%) showed the design appropriateness of both L2 tasks and operational intents in terms of users’ successful understanding and completeness of the given chatbot tasks. The participants’ responses to the survey not only supported the positive potential of the chatbot in EFL settings but also revealed limitations to be resolved. Future suggestions for advancing and implementing AI chatbots in EFL classrooms are discussed.

Language learners today have access to a much wider choice of resources and language-support tools that aid their writing processes. While research on the convenience and flexibility of the writing resources that learners use in real-life writing activities have argued for a re-conceptualization of writing ability, little is known about the effect of using them in a writing assessment setting. Accordingly, the present study aimed to examine whether L2 learners’ use of writing resources in a writing test might have an effect on their writing performance. Furthermore, we also investigated how the effects of the writing resources might vary by test takers’ writing proficiency and scoring domains. Results showed that the group who had access to writing resources outperformed the group who were not given the online resource, but both groups’ scores were within the same scoring descriptor. The significant score improvement was more evident in the low- and intermediate-level learners. In a questionnaire, test takers reported that they normally use the writing resources in real-life writing situations and also found the online resource features helpful in improving their writing performance and agreed upon introducing such features in future writing tests.

In this series, we explore technology-related themes and topics. This series aims to discuss and demystify what may be new areas for some readers and to consider their relevance for English language teachers.

Artificial intelligence has recently seen tremendous growth, and been applied to several fields, including the second-language domain. In this article, we share an innovative practice based on one of potential artificial intelligence technologies for second-language teaching and learning – artificial intelligence-based content generator, which generates texts based on user’s keywords. In total, 121 young English-as-a-foreign-language learners participated in the study, with half of them having engaged in the artificial intelligence-based content generator-based activity, and the other half having received traditional English-as-a-foreign-language reading instruction. We examined the extent to which the artificial intelligence-based content generator-based activity could influence the participants’ foreign language enjoyment and interests in reading English books, and the participants were given the survey addressing these variables, prior to and after the innovative practice. It was found that the condition based on the artificial intelligence-based content generator-based activity was more effective in terms of enhancing the target variables, and that the group which engaged in the artificial intelligence-based content generator-based activity was largely in favor of artificial intelligence-based content generator technology. Pedagogical implications for employing this technology in second-language contexts are provided.

Multiword units receive attention as being an essential part of vocabulary knowledge that will expedite the learning of an L2. However, there is lack of a graded multiword units (MWU) list that can offer direct applications for pedagogy, syllabus design or materials development. The current article aims to report on the development and evaluation of COCA (Corpus of Contemporary American English) Multiword Unit 20 (COCA_MWU20) where the notion of MWU family is adopted. The compilation involved selecting and grading the MWU by grammatical well-formedness, range, and frequency. The 10,000 MWU families which made up the list were utilized in the development of ColloGram, a list-based MWU family analysis program. The COCA_MWU20 can be expected to help L2 learners increase their knowledge of lexical items beyond single words and ColloGram is the first of the kind that can analyze multiword items based on COCA. For researchers, the ColloGram can also be a tool for identifying MWU that may appear in a target text.

This study presents a list of the highest frequency collocations of spoken English based on carefully applied criteria. In the literature, more than forty terms have been used for designating multi-word units, which are generally not well defined. To avoid this confusion, six criteria are strictly applied. The ten million word BNC spoken section was used as the data source, and the 1,000 most frequent spoken word types from that corpus were all investigated as pivot words. The most striking finding was that there is a large number of collocations meeting the six criteria and a large number of these would qualify for inclusion in the most frequent 2,000 words of English, if no distinction was made between single words and collocations. Many of these collocations could be usefully taught in an elementary speaking course. © The Author 2007. Published by Oxford University Press; all rights reserved.

The present study aimed to compare English non-native speaker corpora vs. English native speaker corpora for the analysis of spoken and written...

Recent developments in machine translation, such as in Google Translate, may help second language (L2) writers produce texts in the target language according to their intended meaning. The aim of the present study was to examine the role of machine translation (MT) in L2 writing. For this purpose, 66 Korean English as a foreign language (EFL) university learners produced compositions in which writing tasks were counterbalanced in three writing modes (i.e., Direct Writing, Self-Translated Writing, and Machine-Translated Writing). The learners’ writing products were first graded by independent markers and later submitted for computerized text analyses using BNC-COCA 25000, Coh-Metrix, and SynLex to assess linguistic complexity. The texts were also analyzed for types of errors. The results indicate that MT narrowed the difference of writing ability between the skilled and less skilled learners, facilitated learner use of lower frequency words, and produced syntactically more complex sentences. Error analysis showed a reduction in the quantity of grammatical errors when MT was used to aid L2 writing. However, MT-translated compositions contained more mistranslations and a greater number of poor word choices. The results offer pedagogical implications for using MT for L2 writing.

Educational assessment in the Republic of Korea reflects social and cultural perspectives, as most assessments are considered as a ‘gatekeeper’ of one’s success in life, in terms of entering an eli...

As already well-acknowledged in the field, formulaic sequences often make up an important part of lexical knowledge. Within the L2 learning contexts, there is increasing demand for the need to understand and retrieve lexical items as in prefabricated chunks. In tertiary contexts, knowledge of academic collocations is even more required. For individual word forms, there is the General Service List (West, 1953), and the Academic Word List (Coxhead, 2000). However, what the field currently lacks is a list of academic collocations. Identification of collocations was conducted with the British Academic Spoken English and the Academic Corpus for comparison of academic collocations in spoken and written discourse. There was use of 20 node words in each corpus, which produced 934 written and 460 spoken collocations. With more number of written collocations being produced from the corpus, the study provides a comparative view of the top 50 collocations which suggests a concentration of common collocations in the field of economics. Within the guided approach for data-driven learning, there is proposal for the need to facilitate psychological conditions for learning collocations.

Lexical knowledge is an important predictor of second language learning. The present study focuses on aspects of lexical knowledge of Korean elementary (6th grade) and middle school learners (9th grade) in connection to learning strategies, that is, with regard to vocabulary learning and by investigating the learners' preferred English medium of interest. The learners were asked to complete questionnaires for information on vocabulary learning strategies, and were tested with receptive and productive vocabulary levels test to assess their vocabulary knowledge. One-way ANOVA was conducted to note differences between the different vocabulary learning strategy groups, and multiple regression was employed to select predictors of learners' receptive and vocabulary knowledge. While the learners did not favor public education (school lessons), private lessons indicated to be the dominant means of learning English among the 6th graders with larger vocabulary sizes; the use of the vocabulary workbook indicated to be a useful resource for the 9th graders. Although movies were popularly viewed by most of the learners, it was the learners who had been reading books in English that seemed to have obtained larger vocabulary sizes. The findings provide guidelines for vocabulary instruction in Korean elementary and middle school contexts.

This corpus-based study introduces an effective means of identifying legal terms in digitized documents. In the present study the first 300 entries resulted from the web-based term-extraction tool TermoStat was re-examined by using Chung's (2003) rating scale. When using the combination model, about 70 to 74 % of the candidate terms were found technical words. This is a much lower percentage than the 86% produced by the corpus comparison approach. However, the web-based term-extraction tool TermoStat provides four advantages: 1) users can save time and effort identifying terms, 2) users do not have to prepare any comparison corpora, 3) each item is automatically hyper-linked to all examples used in the technical corpus, and 4) technical collocations, as well as technical single words, are identified. Thus, if both mechanical and manual processes are used to extract legal terms, combining Chung's (2003) rating scale and the web-based comparison tool TermoStat could be an effective way to extract legal terms. Finally, this study concludes by offering some suggestions for further research.

This study separately examined a 10,000,000 token spoken corpus and a 10,000,000 token written corpus to find the most frequent,...

In spite of the domineering influence of the Korean College Scholastic Ability Test (CSAT) on learners who aim to enter universities, as yet there is lack of interest in separately examining the lexical aspects of university learners’ English proficiency. The gap led us to assess Korean EFL university learners’ vocabulary size at 10 word bands, and conduct multiple regression to analyze the learners’ background variables in relation to vocabulary sizes. Participants of the study were 403 freshmen enrolled in a general education English program. Learners’ vocabulary profiles, which could be explained through the responses that were obtained through the vocabulary levels test, indicated that frequently appearing words in the curriculum-based high school textbooks were noticeably well-known. The learners’ receptive vocabulary size indicated the learners to be competent in using English for academic purposes, but not necessarily for productive vocabulary. Of the background variables, the CSAT level was a significant predictor for both receptive and productive vocabulary. However, abroad experience was a significant predictor only for productive vocabulary. The results of the study provide implications for how different aspects of word knowledge may be involved in respectively expanding receptive and productive vocabulary knowledge.