Computer-assisted EFL writing and evaluations based on artificial intelligence: a case from a college reading and writing course

Wang, Zhijie

doi:10.1108/lht-05-2020-0113

Cited by 47 publications

(34 citation statements)

References 34 publications

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“…Z. Wang (2022) investigated the effectiveness of online automated essay evaluation (AEE) system. Students wrote and submitted essays on the AEE system.…”

Section: Ai Applications In Eltmentioning

confidence: 99%

“…Studies also used the deep learning approach to achieve the optimization of the "automated essay evaluation system" and realize effective feedback (Z. Wang, 2022), to set up "a business English speech translation model" (Xu, 2021), to design a new tool platform to assist learners improve their ELT efficiency conforming to their comprehension of knowledge and personality (Z. Sun et al, 2021), and to devise ingredients to train learners and produce pronunciation feedback (Srikanthan et al, 2020).…”

Section: State Of Current Research On Ai In Eltmentioning

confidence: 99%

“…For example, Z. Wang (2022) identified wrongly detected proper nouns by the AI-based system such as "iPhone" as a capital letter error; the system was also unable to differentiate "a long" from "along" and failed to identify the subject fragment and predicate in a sentence like "When Robby got up early, which was a routine for twenty years." Some authors believe that the current AI-based writing systems "only provide vocabulary and grammar improvement suggestions in information feedback" (Su et al, 2019, p. 3).…”

Section: Challengesmentioning

confidence: 99%

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A Systematic Review of Research on the Use of Artificial Intelligence in English Language Teaching and Learning (2015-2021): What are the Current Effects?

Sharadgah

Sa’di

2022

JITE:Research

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Aim/Purpose: This study carried out a systematic review of the literature on artificial intelligence (AI) in English language teaching (ELT). The objective was to delineate the current research progress in the field and to further understand the challenges. Background: The study analyzed articles published between 2015 and 2021. Methodology: The qualitative research method was employed. Five steps were taken to steer the review. 200 articles were scrutinized; 64 were retained. Contribution: Prior research on AI in ELT has not investigated how the literature is progressing or what areas of AI are being covered. Without a holistic picture, some important research findings could be missed. Understanding how studies on AI in ELT are designed and implemented will contribute to a greater understanding of the existing state of research. Findings: Findings show that there is a promising future for AI in ELT. AI in ELT yielded positive results in terms of optimizing the English language skills, translation, assessment, recognition, attitude, satisfaction, etc. It was also found out that more and more articles on the topic are being published; the mixed research method is the most commonly used, higher education level is the most sampled, students as participants are the most sampled, and most studies developed novel AI-based systems. Various AI approaches have been identified in the reviewed studies, including machine learning, neural network, support vector machine, genetic algorithms, deep learning, decision tree, expert system, natural language processing, data mining, cloud computing, and edge computing. However, AI in ELT is still in its infancy, where little research has been conducted and gaps in the literature are still present, especially in terms of inherent issues related to body language, gestures, expressions, emotions, translation, lack of elaborate description of teaching material used for learning driven by AI, uncertainties and vagueness with regards to what can be considered under the realm of AI, and most authors being outside of the ELT discipline. Recommendations for Practitioners: This literature review is likely to provide practitioners with an overview of the current adopted technology, research method, instruments and/or tools, educational level, language skill, and the effects reported by the AI-based studies for designing effective systems for the use of AI in their ELT classrooms. Recommendation for Researchers: Researchers need to conduct research on AI in ELT along with a detailed in-depth description of the methodology, research design, and the proposed systems used to achieve AI in ELT. Furthermore, it is recommended that researchers explore the efficiency of AI-based systems used in previous research and ensure their relevance and functionality. They are also required to provide in-depth analysis of the challenges inherent to systems that have been highlighted in the literature, which will maximize the potentials of these AI-based technologies. Impact on Society: The findings of this paper can provide visualization of research findings that could particularly benefit researchers, educators, and AI specialists who are involved in the study of the applications of AI in ELT. Future Research: Future AI research needs to seriously include more detailed descriptions of the method in further research.

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“…Z. Wang (2022) investigated the effectiveness of online automated essay evaluation (AEE) system. Students wrote and submitted essays on the AEE system.…”

Section: Ai Applications In Eltmentioning

confidence: 99%

Section: State Of Current Research On Ai In Eltmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of Research on the Use of Artificial Intelligence in English Language Teaching and Learning (2015-2021): What are the Current Effects?

Sharadgah

Sa’di

2022

JITE:Research

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show abstract

“…Zheng and Liu (2020) also designed the topic–author–citation evaluation model based on the z index and proposed the ZAS index to evaluate scholars' academic performance. Wang (2020) integrated expectancy disconfirmation theory (EDT) and intelligent computer-assisted language learning (ICALL) theory to investigate the computer-assisted review tools users' learning behavior. Finally, the statistical analysis showed the users' initial expectations of computer-assisted learning tools and the final learning outcomes have the positive correlation.…”

Section: Fewer Research Questions Diverse Fieldsmentioning

confidence: 99%

Editorial

Chen

Liao

Lughofer

et al. 2022

LHT

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“…DWright, an online writing tutorial system Positive improvement [59] Graduate students in China EJP-Write, an online writing tutorial system No significant improvements [60] Undergraduates in China Online automated essay evaluation system Significant improvement [61] Undergraduates in Japan Online forums, blogs, and wikis. Positive [62] Undergraduates in Iran Online blogs Effective, satisfaction [63] Postgraduates in Australia Online corpora Mixed [64] English vocabulary…”

Section: Undergraduates In Chinamentioning

confidence: 99%

Effectiveness Study on Online or Blended Language Learning Based on Student Achievement: A Systematic Review of Empirical Studies

Zhou

Zhang

2022

Sustainability

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The ubiquitous impacts resulting from the COVID-19 pandemic have profoundly changed the education sector and marked research interest in online or blended learning can be witnessed. As a pervasive learning activity of paramount significance, online language learning has aroused widespread attention. Nonetheless, few systematic reviews concerning the effectiveness of online language learning have been published. With the help of CiteSpace, this study systematically investigated 103 included articles from the SSCI of empirical studies from 44 journals for the purpose of filling the research gap in this field, providing a better understanding of the research trends, exploring effective ways to implement online language courses, and testifying to the ability of CiteSpace to track research hotspots. The findings show that effectiveness studies on online language learning principally focus on assisted tools (42.72%), instructional approaches (36.89%), and specific courses (20.39%). Lack of adequate cooperation among research institutions and the dominant position of online English learning (82.52%) can be witnessed. Despite the small sample size of 103 included articles, the validation of CiteSpace in terms of tracking the research trends or hotspots is confirmed. However, the proportion of each research focus is not compatible with the results of a comprehensive full-text analysis. This literature review also probes into various methods to measure effectiveness more scientifically and effective ways to implement online language courses. Theoretical as well as practical implications and future research directions are clarified.

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Computer-assisted EFL writing and evaluations based on artificial intelligence: a case from a college reading and writing course

Cited by 47 publications

References 34 publications

A Systematic Review of Research on the Use of Artificial Intelligence in English Language Teaching and Learning (2015-2021): What are the Current Effects?

A Systematic Review of Research on the Use of Artificial Intelligence in English Language Teaching and Learning (2015-2021): What are the Current Effects?

Editorial

Effectiveness Study on Online or Blended Language Learning Based on Student Achievement: A Systematic Review of Empirical Studies

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