The Auxiliary Role of Artificial Intelligence Applications in Mitigating the Linguistic, Psychological, and Educational Challenges of Teaching and Learning Chinese Language by non-Chinese Students
Bibliographic record
Abstract
Learners might have several challenges while attempting to learn a second/foreign language. Learners of Chinese face linguistic, psychological, and educational challenges. The integration of technology, especially artificial intelligence (AI), into teaching foreign languages is a blessing for teachers and learners. This study delved into the auxiliary role of AI-powered applications in mitigating the linguistic, psychological, and educational challenges which non-Chinese learners face while learning Chinese/Mandarin language. Qualitative research was employed, and 20 teachers of Chinese language were selected through theoretical sampling. In-depth interviews were used for collecting data, and MAXQDA was used for thematic analysis. Findings revealed that AI-powered educational applications are useful for helping language learners overcome the commonly reported linguistic, psychological, and educational challenges which non-Chinese learners and teachers of Mandarin might encounter. Findings verify the effectiveness of AI-powered applications, such as ChatGPT, Poe, Brainly, and so forth, in helping teachers and learners of Chinese language learn grammar, structure, idioms, and cultural issues of Chinese language. Findings have implications for foreign language (Chinese) learners and teachers, educational technologists, as well as syllabus designers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".