Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach

Liang, Jia-Cing; Hwang, Gwo‐Jen; Chen, Mei‐Rong Alice; Darmawansah, Darmawansah

doi:10.1080/10494820.2021.1958348

Cited by 103 publications

(83 citation statements)

References 43 publications

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“…1). These AIEd reviews focused on different educational levels, fields, and contexts, including higher education (Zawacki-Richter et al, 2019), e-learning (Tang et al, 2021), mathematics education (Hwang & Tu, 2021), language education (Liang et al, 2021), medical education (Khandelwal et al, 2019;Lee et al, 2021), programming education (Le et al, 2013), and special education (Drigas & Ioannidou, 2012). For example, Zawacki-Richter et.…”

Section: Literature Reviewmentioning

confidence: 99%

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The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Ouyang

2022

IJ STEM Ed

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Background The application of artificial intelligence (AI) in STEM education (AI-STEM), as an emerging field, is confronted with a challenge of integrating diverse AI techniques and complex educational elements to meet instructional and learning needs. To gain a comprehensive understanding of AI applications in STEM education, this study conducted a systematic review to examine 63 empirical AI-STEM research from 2011 to 2021, grounded upon a general system theory (GST) framework. Results The results examined the major elements in the AI-STEM system as well as the effects of AI in STEM education. Six categories of AI applications were summarized and the results further showed the distribution relationships of the AI categories with other elements (i.e., information, subject, medium, environment) in AI-STEM. Moreover, the review revealed the educational and technological effects of AI in STEM education. Conclusions The application of AI technology in STEM education is confronted with the challenge of integrating diverse AI techniques in the complex STEM educational system. Grounded upon a GST framework, this research reviewed the empirical AI-STEM studies from 2011 to 2021 and proposed educational, technological, and theoretical implications to apply AI techniques in STEM education. Overall, the potential of AI technology for enhancing STEM education is fertile ground to be further explored together with studies aimed at investigating the integration of technology and educational system.

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Section: Literature Reviewmentioning

confidence: 99%

“…(2019) reviewed AIEd in the higher education context and four AI technical applications were classified, namely intelligent tutoring systems, adaptive systems and personalization, profiling and prediction, and assessment and evaluation. Liang et. al.…”

Section: Literature Reviewmentioning

confidence: 99%

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Ouyang

2022

IJ STEM Ed

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“…One of the relevant research directions is the study of continuous learning systems in lifelong learning [ 25 ]. Other research has focused on the application of AI in language education domains, including reading, writing, and vocabulary acquisition [ 26 ]. Hwang and Tue also reported advancements in AI in mathematics research [ 27 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Use of artificial intelligence in activating the role of Saudi universities in joint scientific research between university teachers and students

2022

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Scientific research in Saudi Arabia’s universities has undergone significant changes in recent years with the speed of higher education expansion and the opening of new universities. Artificial intelligence (AI) can be applied to existing data analysis processes to enhance pattern recognition and to support advanced data analysis. This study aimed to investigate the obstacles to activating the role of university instructors and students in joint scientific research. The study also aimed to evaluate joint scientific research between university teachers and students in universities, as well as the mechanisms for activating joint scientific research among male and female students in health and humanities science within Saudi universities, to enhance creation and invention achievements. To determine the obstacles to activating scientific research roles between students and tutors in Saudi universities using AI, a simple random sampling technique was adopted for this study. A well-structured questionnaire was administered to 250 respondents affiliated with universities in Saudi Arabia. The data collected were statistically analyzed with the aid of the Statistical Package for Social Science (SPSS) version 20. The results of this study revealed that the objectives of joint scientific research between university teachers and students in universities have a significant positive predictor of obstacles to activating the role of teachers in joint scientific research with students in Saudi universities. The study also showed that there was a statistically significant correlation (p value = 0.00) between each of the variables.

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“…Generally, AI can facilitate learning by providing personalized learning contexts, which expedites students' independent learning (Liang et al, 2021;Yong, 2020). It enables students to study at their convenience, receive feedback, and be assisted and guided in their learning with minimal intervention from the teacher (Keerthiwansha, 2018).…”

Section: Introductionmentioning

confidence: 99%

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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Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach

Cited by 103 publications

References 43 publications

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Use of artificial intelligence in activating the role of Saudi universities in joint scientific research between university teachers and students

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

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