Systemic Barriers and Adaptive Challenges to the Employability of International Chinese Language Education Graduates in the Era of Artificial Intelligence
Bibliographic record
Abstract
As artificial intelligence (AI) reshapes the global education ecosystem, graduates of International Chinese Language Education (ICLE) programs face dual structural pressures from technological disruption and shifting labor market demands. This study aims to explore the technological dilemmas and educational challenges hindering the enhancement of employment competitiveness among graduates of ICLE in the AI era. Through semi-structured interviews with 30 participants (20 ICLE graduates, 5 university career counselors, and 5 HR professionals), data analysis was conducted using open coding, axial coding, and selective coding. Three major structural barriers were identified: curriculum disconnect from AI, fragmented acquisition of digital skills, and insufficient institutional and faculty support for AI integration. These severely constrain ICLE graduates' employment readiness in AI-mediated environments. This study enriches theoretical understanding of how information technology reshapes disciplinary employability, providing targeted guidance for ICLE curriculum reform, institutional innovation, and skill development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".