Exploring AI-mediated informal digital learning of English (AI-IDLE): a mixed-method investigation of Chinese EFL learners’ AI adoption and experiences
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".