Leveraging Artificial Intelligence (AI) in English as a Foreign Language (EFL) Classes: Challenges and Opportunities in the Spotlight
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The prominence of language-related studies reflects a growing interest in the application of AI for personalized and adaptive instruction, particularly through technologies such as intelligent tutoring systems, generative AI, automated feedback tools, and speech recognition.
“The prominence of language-related studies reflects a growing interest in the application of AI for personalized and adaptive instruction, particularly through technologies such as intelligent tutoring systems, generative AI, automated feedback tools, and speech recognition [53,54].”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [54]
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References 71 · Cited by 109
- Authors
- Dai, K., & Liu, Q.
- Affiliations
- Shaanxi Normal University (CN)
- Corresponding author
- Dai, Kun
- ORCID
- Dai, Kun
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 159
- Pages
- 108354
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W4399864398
- Semantic Scholar
- 2f7b9fe2daa54d4bfa68f8fe7fe85cf049cbedbd
- Cited by
- 109
- References
- 71
- Citations by year
- 2026: 54 · 2025: 50 · 2024: 5
- FWCI
- 40.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers in Human Behavior (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
- Keywords
- artificial intelligence in language teaching, English as a foreign language, opportunities and challenges of AI, personalized learning, immediate feedback, teaching materials, interactive learning, Chinese learners of English
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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