128 responses from 32 respondents to 4 items.
One of 3 tables from the same source; this one is the intentions table. The others are listed under Related tables.
| Description | Responsible AI-Use Intentions scale, 1-5 Likert; N=32, 4 items, K-12/adult ML instructors |
|---|---|
| Reference | Chowdhury, S., & Sain, N. (2026). Teacher AI Literacy for ML Learner Instruction. figshare. |
| Licence | CC BY 4.0 |
| Source data | https://figshare.com/articles/dataset/Teacher_AI_Literacy_for_ML_Learner_Instruction/31427369 |
| Responses | 128 |
|---|---|
| Respondents | 32 |
| Items | 4 |
| Response categories | 5 |
| Responses per respondent | 4 |
| Responses per item | 32 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, chowdhury2026_ai_literacy.py. 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). | made in the build script |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_ai_policy_exists | Covariate | ai_policy_exists |
cov_ml_percent | Covariate | ml_percent_cat |
cov_prior_ai_pd | Covariate | prior_ai_pd_cat |
cov_role | Covariate | role_cat |
cov_teaches_ml | Covariate | teach_ML |
cov_years_teaching | Covariate | years_teaching_bin |
What item codes and covariate values stand for is in the source’s codebook, CODEBOOK.md, codebook.csv.
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("chowdhury2026_ai_intentions")
# Python
pip install irw
import irw
df = irw.fetch("chowdhury2026_ai_intentions")
@misc{https://doi.org/10.6084/m9.figshare.31427369.v1,
doi = {10.6084/M9.FIGSHARE.31427369.V1},
url = {https://figshare.com/articles/dataset/Teacher_AI_Literacy_for_ML_Learner_Instruction/31427369/1},
author = {Chowdhury, Showrav and Sain, Nweli},
keywords = {Teacher education and professional development of educators},
title = {Teacher AI Literacy for ML Learner Instruction},
publisher = {figshare},
year = {2026},
copyright = {Creative Commons Attribution 4.0 International}
}
| IRW version | v549 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v13.0 |
| Redivis dataset DOI | 10.57761/gpg7-sg87 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v39.0 |