Data · dataset · 2026
Survey Data on Generative AI Use and Learning Agency among Pre-service Teachers
Listed in ScienceDB
This dataset focuses on the influence mechanism of generative artificial intelligence use in education on pre-service teachers’ learning agency.
Description
It includes 1,320 pre-service teachers from six universities in Sichuan Province, Shaanxi Province, Guangxi Zhuang Autonomous Region, and Guangdong Province, China. The data were collected using a 5-point Likert scale through an online questionnaire, with all items set as mandatory; therefore, there are no missing values.
The variables include demographic and learning background information, generative AI use in education, human-AI collaboration quality, AI dependence, learning agency, AI ethics literacy, teacher guidance, and school norms. The dataset can be used to examine the dual-path mechanism through which generative AI use affects learning agency: promoting learning agency through human-AI collaboration quality and weakening learning agency through AI dependence.
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It can also support further analysis of the moderating roles of AI ethics literacy, teacher guidance, and school norms. This dataset is suitable for research in educational psychology, intelligent education, pre-service teacher development, learning agency, and AI-assisted learning.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.40974 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Psychology & Behavioral Science · Social Science
- Inferred from text
- Cognitive and computational psychology 71%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.40974 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:5204 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (71%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |