Navigating ai in China: Access, censorship, and trust among preservice teachers
Bibliographic record
Abstract
This study aims to explore how preservice teachers at a Chinese Normal University perceive and engage with artificial intelligence (AI) tools, with particular attention to issues of access, censorship, and trust. Drawing on questionnaire responses from 50 sophomores and follow-up unstructured interviews with five participants, the research investigates students’ awareness and usage of both international platforms (e.g., ChatGPT) and Chinese-developed systems (e.g., Wenxin, Deepseek). The study further examines students’ perceptions of content differences across platforms, especially in politically sensitive contexts, and considers how these perceptions influence levels of trust in AI-generated information. Findings reveal that participants demonstrate a nuanced awareness of censorship and its implications, noting divergences in information quality and availability depending on platform origin. While many students acknowledge the educational potential of AI tools, they also express skepticism toward politically restricted outputs, underscoring how sociopolitical conditions shape digital trust. The results highlight that AI literacy plays a critical role not only in shaping preservice teachers’ trust in emerging technologies but also in guiding their future pedagogical choices. This research contributes to broader discussions of AI ethics and cross-cultural digital engagement, offering insights into how higher education in China can better prepare future educators to critically evaluate and responsibly integrate AI into their teaching practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 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".