3,090 responses from 618 respondents to 5 items.
One of 4 tables from the same source; this one measures Agency maintenance when preparing lessons with AI. The others are listed under Related tables.
| Description | Agency maintenance when co-preparing lessons with AI, 5 items Q27-Q31, 1-5, Chinese; N = 618 Chinese preservice teachers; no covariates (withheld at source). |
|---|---|
| Reference | Bi, Y., Li, L., & Han, D. (2026). Anonymized Dataset for "From AI Literacy to the Creative Application of AI in Teaching: Serial Mediation through Trust Calibration and Agency Maintenance among Preservice Teachers" [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.22040756 |
| Licence | CC BY 4.0 |
| Source data | https://zenodo.org/records/22040756 |
| Responses | 3,090 |
|---|---|
| Respondents | 618 |
| Items | 5 |
| Response categories | 5 |
| Responses per respondent | 5 |
| Responses per item | 618 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
| construct name | Agency maintenance when preparing lessons with AI |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Agency maintenance when co-preparing lessons with AI |
|---|---|
| Mean words per item | 21 |
| Mean characters per item | 127.800 |
| Mean characters per response | 2 |
| Flesch-Kincaid grade level | 12.717 |
This codebook is reconstructed by the IRW from the IRW data standard. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | not traced |
item | IRW standard Item identifier. | not traced |
resp | IRW standard Response value. | not traced |
What item codes and covariate values stand for is in the source’s codebook, AI_Literacy_618_Final_README.txt.
Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("bi_2026_agency_maintenance")
# Python
pip install irw
import irw
df = irw.fetch("bi_2026_agency_maintenance")
@misc{https://doi.org/10.5281/zenodo.22040756,
doi = {10.5281/ZENODO.22040756},
url = {https://zenodo.org/doi/10.5281/zenodo.22040756},
author = {Bi, Yimu and Li, Li and HAN, DONG},
keywords = {Artificial Intelligence Literacy, Trust Calibration, Agency Maintenance, Preservice Teachers, Human–AI Interaction},
language = {en},
title = {Anonymized Dataset for "From AI Literacy to the Creative Application of AI in Teaching: Serial Mediation through Trust Calibration and Agency Maintenance among Preservice Teachers"},
publisher = {Zenodo},
year = {2026},
copyright = {Creative Commons Attribution 4.0 International}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v8.5 |
| Redivis dataset DOI | 10.57761/41xy-5k56 |
| Manifest pin for this IRW version | v8.5 |
| Metadata source | irw_meta v44.0 |