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How does artificial intelligence empower EFL teaching and learning nowadays? A review on artificial intelligence in the EFL context
Open accessAbstract
The booming Artificial Intelligence (AI) provides fertile ground for AI in education. So far, few reviews have been deployed to explore how AI empowers English as Foreign Language (EFL) teaching and learning. This study attempts to give a brief yet profound overview of AI in the EFL context by summarizing and delineating six dominant forms of AI application, including Automatic Evaluation Systems, Neural Machine Translation Tools, Intelligent Tutoring Systems (ITSs), AI Chatting Robots, Intelligent Virtual Environment, and Affective Computing (AC) in ITSs. The review furthermore uncovers a current paucity of research on applying AC in the EFL context and exploring pedagogical and ethical implications of AI in the EFL context. Ultimately, challenges from technical and teachers' perspectives, as well as future research directions, are illuminated, hopefully proffering new insights for the future study.
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“The coronavirus disease 2019 pandemic accelerated the adoption of digital learning tools, highlighting the potential of AI to maintain educational continuity and support remote learning, even in complex skill domains like communication [4].”
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1 retracted
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Mikolov, T., Kombrink, S., Burget, L., Černocký, J., & Khudanpur, S. P. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5947611 · Cited by 1,622 · cited 1×
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“…vocabulary learning and retention (Lai and Chen, 2021; Tai et al., 2022), enhancing English speaking and willingness to communicate (Ebadi and Ebadijalal, 2020), building ideal 2L self (Adolphs et al., 2018), and improving English learning motivation to reduce anxiety (Chien et al., 2020).”
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“For instance, AESs, NMT tools, and ITSs can serve asmonitoring and tutoring tools in the EFL instruction to promote EFL learning mainly from cognitive and linguistic perspectives (Groves and Mundt, 2015; Abu Ghali et al., 2018; Gao, 2021; Koh, 2022).”
BackgroundLeveraging recent advances in deep learning for audio-Visual emotion recognition
Schoneveld, L., Othmani, A., & Abdelkawy, H. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.007 · Cited by 243 · cited 1×
“, audio, vision and even electroencephalo-graph, using AC techniques (Schoneveld et al., 2021), which can feed back to teachers or ITSs for further monitoring and improving the EFL learning.”
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Klimova, B., Pikhart, M., Benites, A. D., Lehr, C., & Sanchez-Stockhammer, C. (2022) · Education and Information Technologies : Official Journal of the IFIP technical committee on Education · doi:10.1007/s10639-022-11194-2 · Cited by 208 · cited 2×
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“Additionally, it’s noteworthy that the majority of published studies investigate EFL performance when applying AI in the EFL instructional settings, for instance, scaffolding argumentative writing by using AI chatbots (Guo et al., 2022), and improving speaking ability by deploying ITSs (Mohammadzadeh and Sarkhosh, 201…”
BackgroundUser experience of a mobile speaking application with automatic speech recognition for EFL learning
Ahn, T. Y., & Lee, S. (2015) · British Journal of Educational Technology · doi:10.1111/bjet.12354 · Cited by 181
Xu, Z., Wijekumar, K. K., Ramírez, G., Hu, X., & Irey, R. C. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12758 · Cited by 181 · cited 2×
“, 2018) and reading comprehension (Xu et al., 2019).”
BackgroundReview of affective computing in education/learning: Trends and challenges
Wu, C., Huang, Y., & Hwang, J.-P. (2015) · British Journal of Educational Technology · doi:10.1111/bjet.12324 · Cited by 167 · cited 1×
“On the one hand, AC should be properly used by the instructor for teaching (Wu et al., 2016a).”
BackgroundEbadi, S., & Ebadijalal, M. (2020) · Computer Assisted Language Learning · doi:10.1080/09588221.2020.1854311 · Cited by 166 · cited 1×
“, 2022), enhancing English speaking and willingness to communicate (Ebadi and Ebadijalal, 2020), building ideal 2L self (Adolphs et al.”
BackgroundImplementing flipped classroom that used an intelligent tutoring system into learning process
Mohamed, H., & Lamia, M. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.05.011 · Cited by 159 · cited 1×
“Concretely, embedded in the flipped classroom, ITSs help students solve problems (Mohamed and Lamia, 2018); assisting Self-Regulatory Learning (SRL), ITSs are proved to improve speaking skills (Mohammadzadeh and Sarkhosh, 2018); in COVID-19 context, ITSs can build students’ performance profiles to assist teachers in a…”
BackgroundEverything at Once – Multi-modal Fusion Transformer for Video Retrieval
Shvetsova, N., Chen, B., Rouditchenko, A., Thomas, S., Kingsbury, B. E. D., Feris, R., Harwath, D. F., Glass, J. R., & Kuehne, H. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01939 · Cited by 154 · cited 1×
“To this end, large-scale Transformer (Shvetsova et al., 2022) can be pre-trained based on a large amount of multi-modal signals in self-supervised learning of multi-modal embedding space.”
MethodOn the effects of 3D virtual worlds in language learning – a meta-analysis
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“It is during the last two decades that Virtual Reality (VR) tools have been widely adopted in foreign language education (Rau et al., 2018; Wang et al., 2020), exemplified by Google Earth (Chen et al.”
BackgroundEffects of using mobile-based virtual reality on Chinese L2 students’ oral proficiency
Xie, Y., Chen, Y., & Ryder, L. (2019) · Computer Assisted Language Learning · doi:10.1080/09588221.2019.1604551 · Cited by 148 · cited 2×
“It is during the last two decades that Virtual Reality (VR) tools have been widely adopted in foreign language education (Rau et al., 2018; Wang et al., 2020), exemplified by Google Earth (Chen et al., 2020), Google Tour Creator (Nobrega and Rozenfeld, 2019), and Google Expeditions (Xie et al., 2021).”
BackgroundA comparative study on the effects of a VR and PC visual novel game on vocabulary learning
Lai, K.-W. K., & Chen, H. H.-J. (2021) · Computer Assisted Language Learning · doi:10.1080/09588221.2021.1928226 · Cited by 133
York, J., Shibata, K., Tokutake, H., & Nakayama, H. (2020) · ReCALL · doi:10.1017/s0958344020000154 · Cited by 127 · cited 1×
“Similarly, some empirical evidence suggests that using avatars can decrease foreign language anxiety and encourage learners to communicatemore successfully (MelchorCouto, 2017; York et al., 2021).”
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“Notwithstanding the drawbacks of NMT tools revealed in some literature (Bahri and Mahadi, 2016), a considerable amount of literature suggest that NMT tools can benefit EFL students’ learning: from the cognitive and linguistic perspective, they promote selfdirected learning (Godwin-Jones, 2015), improve mastery of lexi…”
BackgroundForeign language anxiety levels in Second Life oral interaction
Melchor-Couto, S. (2016) · ReCALL · doi:10.1017/s0958344016000185 · Cited by 125 · cited 3×
“Similarly, some empirical evidence suggests that using avatars can decrease foreign language anxiety and encourage learners to communicate more successfully (Melchor-Couto, 2017; York et al., 2021).”
BackgroundSpeed reading on virtual reality and augmented reality
Rau, P. P., Zheng, J. H., Guo, Z. P., & Li, J. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.06.016 · Cited by 122 · cited 1×
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“It is noteworthy that from the affective perspective, they may lower language anxiety (Bahri and Mahadi, 2016).”
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Hasan, M. A., Noor, N. F. B. M., Rahman, S. S. B. A., & Rahman, M. M. (2020) · IEEE Access · doi:10.1109/access.2020.3036990 · Cited by 103 · cited 3×
“To illustrate, the Affective Tutoring System (ATS) combining emotional expressions and AC, can detect learners’ emotional expressions, determine learners’ learning state, and give interactive feedback (Lin et al., 2012; Hasan et al., 2020).”
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“Notwithstanding the drawbacks of NMT tools revealed in some literature (Bahri and Mahadi, 2016), a considerable amount of literature suggest that NMT tools can benefit EFL students’ learning: from the cognitive and linguistic perspective, they promote selfdirected learning (Godwin-Jones, 2015), improve mastery of lexi…”
BackgroundLanguage recognition with support vector machines.
Campbell, W. M., Singer, E., Torres‐Carrasquillo, P. A., & Reynolds, D. A. (2004) · Cited by 96
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An Intelligent Tutoring System for Teaching English Grammar
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“A large and growing body of literature has suggested that AI can benefit language teaching and learning (Gao, 2021; Pikhart, 2021; Klimova et al., 2022), and ameliorate the quality of online EFL learning especially during the COVID-19 pandemic (Zitouni, 2022).”
BackgroundLearners’ Feedback Regarding ASR-based Dictation Practice for Pronunciation Learning
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“…raters in EFL writing instruction (Bai and Hu, 2017; Qian et al., 2021) due to relatively low accuracy (Liu and Kunnan, 2016), insufficient high-quality comment on collocation errors and syntactic use (Gao, 2021), the frustrating levels of recognition, and the lack of convenience (McCrocklin, 2019).”
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“Additionally, the affordability and limited access to networks still stump the implementation of IVE in the EFL context (Cowie and Alizadeh, 2022).”
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“…in the EFL context tend to vary from being positive, which is in accord with Sumakul et al. (2022)’s findings, toward being conservative, as noted by Holstein et al. (2017) and Lin et al. (2017) that less-experienced instructors usually struggle to execute effective responses to analytics,…”
ResultThe use of virtual reality for language investigation and learning
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“However, some scholars still express some reservations about the effectiveness of using avatars in the EFL context, considering technical factors such as interacting with avatars outside the scripted application areas (Lotze, 2018), the acceptability of virtual avatars (Repetto, 2014).”
BackgroundVirtual Reality in the Teaching of FLE in a Brazilian Public School
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Yu, H., Peng, H., & Lowie, W. M. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.899400 · Cited by 29 · cited 1×
“Particularly, the close connection between emotions and language learning motivation is also empirically verified (Yu et al., 2022).”
BackgroundConstructing an Affective Tutoring System for Designing Course Learning and Evaluation
Wang, C.-H., & Lin, H.-C. K. (2017) · Journal of Educational Computing Research · doi:10.1177/0735633117699955 · Cited by 25
Mohammadzadeh, A., & Sarkhosh, M. (2018) · International Journal of Instruction · doi:10.12973/iji.2018.11212a · Cited by 24 · cited 4×
“…ITSs help students solve problems (Mohamed and Lamia, 2018); assisting Self-Regulatory Learning (SRL), ITSs are proved to improve speaking skills (Mohammadzadeh and Sarkhosh, 2018); in COVID-19 context, ITSs can build students’ performance profiles to assist teachers in adapting online teaching…”
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“Some underscore that AESs can’t replace human raters in EFL writing instruction (Bai and Hu, 2017; Qian et al., 2021) due to relatively low accuracy (Liu and Kunnan, 2016), insufficient high-quality comment on collocation errors and syntactic use (Gao, 2021), the frustrating levels of recognition, and the lack of conv…”
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“…and Lamia, 2018); assisting Self-Regulatory Learning (SRL), ITSs are proved to improve speaking skills (Mohammadzadeh and Sarkhosh, 2018); in COVID-19 context, ITSs can build students’ performance profiles to assist teachers in adapting online teaching modes and content wisely (Nagro, 2021).”
BackgroundAffective Mobile Language Tutoring System for Supporting Language Learning
Wu, C., Lin, H.-C. K., Wang, T.-H., Huang, T.-H., & Huang, Y. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.833327 · Cited by 19
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A Comparison of Free Online Machine Language Translators
Vanjani, M. B., & Aiken, M. W. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3961085 · Cited by 9
A Comparison of Free Online Machine Language Translators
Vanjani, M. B., & Aiken, M. W. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3960834 · Cited by 9
The role of reproducibility in affective computing
Engel, F., Keary, A., Berwind, K., Bornschlegl, M. X., & Hemmje, M. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217969 · Cited by 7 · cited 1×
“On the other hand, the learner’s autonomy should be respected (Engel et al., 2017).”
BackgroundAn Embodied Tutoring System for Literal vs. Metaphorical Concepts
Sionti, M., Schack, T., & Aloimonos, Y. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.02254 · Cited by 7 · cited 1×
“A large volume of published studies has verified that ATS can improve learning motivation and outcomes (Lin et al., 2012; Sionti et al., 2018; Wang and Lin, 2018; Hasan et al., 2020).”
BackgroundTHE INTEGRATION OF AI AND VIRTUAL LEARNING IN TEACHING EFL UNDER COVID-19
Obari, H. (2020) · ICERI proceedings · doi:10.21125/iceri.2020.1740 · Cited by 6 · cited 1×
“IVR can also serve as an effective means to engage EFL students virtually in Zoom virtual classroom teaching in covid-19 pandemic (Obari, 2020).”
BackgroundThe Development of an Affective Tutoring System for Japanese Language Learners
Chun, Y., & Lin, H.-C. K. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71084-6_41 · Cited by 4
Neural machine translation and language teaching : possible implications for the CEFR
Benites, A. D., & Lehr, C. (2021) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-24716 · Cited by 2
Research works printed but not linked: 2
- Kim, 2016, Effects of voice chat on efl learners' speaking ability according to proficiency levels, Multimedia Assist. Lang. Learn, 19, 63, 10.15702/mall.2016.19.4.63 · DOI
- Koh, 2022, A comparative error analysis of neural machine translation output: based on film corpus, LAK J, 30, 157, 10.24303/lakdoi.2022.30.1.157 · DOI
Cited by
Sorted by citations
Song, C., & Song, Y. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1260843 · Cited by 717
“This includes the use of advanced artificial intelligence (AI)-based computer and mobile programs, which offer interactive and personalized tools for honing writing abilities as well as enhanced motivation (Jiang, 2022; Meunier et al., 2022; Yan, 2023).”
Artificial intelligence technologies and applications for language learning and teaching
Son, J., Ružić, N. K., & Philpott, A. (2023) · Journal of China Computer-Assisted Language Learning · doi:10.1515/jccall-2023-0015 · Cited by 367
“There are various types of ITSs (e.g., Bibauw et al., 2019; Heift, 2010) and some use AI and machine learning algorithms to adapt to the needs of users (Jiang, 2022).”
The effect of artificial intelligence tools on EFL learners' engagement, enjoyment, and motivation
Yuan, L., & Liu, X. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108474 · Cited by 188
Yang, L., & Zhao, S. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108337 · Cited by 164
Wu, H., Wang, Y., & Wang, Y. (2024) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v25i3.7708 · Cited by 174
Metadata
- Authors
- Jiang, R.
- Affiliations
- Tianjin University of Finance and Economics Pearl River College (CN)
- Corresponding author
- Jiang, Ruihong
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2022-11-16
- Volume
- 13
- Pages
- 1049401-1049401
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC9709327
- OpenAlex
- W4309307641
- Cited by
- 181
- References
- 74
- Citations by year
- 2026: 54 · 2025: 58 · 2024: 50 · 2023: 19
- FWCI
- 4.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Educational Technology and Pedagogy
- Keywords
- artificial intelligence in education, English as a foreign language, automatic evaluation system, neural machine translation, intelligent tutoring systems, AI chatbots, intelligent virtual environments, affective computing, ethical implications of AI, teacher perceptions
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