Exploring AI-mediated informal digital learning of English (AI-IDLE): a mixed-method investigation of Chinese EFL learners’ AI adoption and experiences
Open accessAbstract
Recent advancements in natural language processing and large language models have ushered language learning into the age of artificial intelligence (AI).Recognizing the affordances of generative AI tools, this paper aims to examine the degree to which L2 learners accepted and leveraged large language model platforms (e.g.ChatGPT, Bing Chat) for the informal digital learning of English (IDLE) purposes.Employing an explanatory sequential mixed-method design, this study draws on the technology acceptance model (TAM) and collects data via an adapted TAM questionnaire and an interview guide.A total of 867 Chinese EFL (English as a foreign language) learners answered the online survey, while 20 attended the post-survey interviews.Drawing on a validated structural model that elucidates the inter-factor relationships of perceived ease of use, perceived usefulness, intention to use, and actual use, the quantitative analysis provides statistical accounts for EFL learners' adoption of Generative Pre-trained Transformer (GPT) technologies.The qualitative findings, derived from the interview data, reveal three key themes: (1) how perceived usefulness of chatbots for IDLE emerges from hands-on experimentation with these tools; (2) how intention to use increases as learners negotiate chatbot affordances and constraints; and (3) how actual use of chatbots for IDLE involves using these tools as tutors or conversation partners.Connections between quantitative and qualitative findings enhance our understanding of how EFL learners negotiate the affordances and constraints of highly capable AI technologies to participate in creative IDLE practices.By understanding these practices, this study draws attention to the attitudes and practices that constitute AI literacies, ultimately offering implications for future classroom practices and research.
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ChatGPT's ability to deliver immediate feedback and contextualized examples contributed to learners' vocabulary gains.
“ChatGPT's ability to deliver immediate feedback and contextualized examples appears to have contributed to learners' vocabulary gains (Karataş et al., 2024;Liu et al., 2024).”
From Integrating ChatGPT for vocabulary learning and retention: A classroom-based study of Saudi EFL learners (2025) · cites this paper as Liu et al., 2024
The Introduction says ChatGPT can provide instant feedback, and “Actual use of chatbots as tutors or conversation partners” reports learners using AI to create vocabulary games for learning new words. The paper also describes contextualized answers, but these passages do not link feedback or contextualized examples to vocabulary gains; its reported feedback example concerns grammar. (LLM, conf=0.86)
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- Authors
- Liu, G. L., Darvin, R., & Ma, C.
- Affiliations
- Chinese University of Hong Kong (HK)
- University of British Columbia (CA)
- City University of Hong Kong (HK)
- Corresponding author
- Liu, Guangxiang Leon
- Venue
- Computer Assisted Language Learning
- Publisher
- Informa UK Limited
- Published
- 2024-02-02
- Volume
- 38
- Issue
- 7
- Pages
- 1632-1660
- ISSN
- 0958-8221 (ISSN-L)
- 1744-3210
- OpenAlex
- W4391473908
- Cited by
- 299
- References
- 59
- Citations by year
- 2026: 145 · 2025: 125 · 2024: 29
- FWCI
- 470.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computer Assisted Language Learning (publishedVersion, cc-by-nc-nd)
- CityU Scholars (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- generative AI in language learning, large language models, informal digital learning, English as a foreign language, Chinese learners of English, artificial intelligence adoption, technology acceptance model, perceived usefulness, perceived ease of use, intention to use, AI literacy, ChatGPT, Bing Chat
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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