3,136 responses from 392 respondents to 8 items.
One of 5 tables from the same source; this one measures Use of AI in teaching. The others are listed under Related tables.
| Description | Frequency of using AI for eight teaching tasks (exams, homework, lesson design, reports, writing; 1 = never to 5 = always), 392 Turkish teachers. |
|---|---|
| Reference | Kayır, G. (2026). Validation of the Turkish AI Specific Teacher Attitude Questionnaire with measurement invariance across gender and teaching level [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/KRWI6E |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/KRWI6E |
| Responses | 3,136 |
|---|---|
| Respondents | 392 |
| Items | 8 |
| Response categories | 5 |
| Responses per respondent | 8 |
| Responses per item | 392 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Workplace |
| construct type | Behavioral |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | tur |
| construct name | Use of AI in teaching |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, kayir_2026_ai_teacher.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_age | Covariate | YAŞ |
cov_ai_daily_use | Covariate 1 = gündelik yaşamda YZ kullanıyorum · 2 = KULLANMIYORUM | gündelikyaşamYZkullanma |
cov_ai_in_curriculum | Covariate 1 = OLMALI · 2 = NO NEED | YZmufredattaolmalı |
cov_ai_training | Covariate 1 = YZ ile ilgili eğitim aldım · 2 = eğitim almadım | YZeğitim |
cov_degree | Covariate 1 = LİSANS · 2 = LİSANSÜSTÜ | EĞİTİMDÜZEYİ |
cov_gender | Covariate 1 = KADIN · 2 = ERKEK | CİNSİYET |
cov_perceived_ses | Covariate | algılananSED |
cov_school_level | Covariate 1 = İLKOKUL · 2 = ORTAOKUL · 3 = LİSE | KURUMGÖREVYAPILAN |
cov_seniority | Covariate | KIDEM |
cov_teacher_type | Covariate 1 = SINIF · 2 = BRANŞ | ALTBRANŞ |
Covariate value labels are the source’s own, as the IRW recorded them. What item codes and any unlabelled values stand for is in the codebook released with the source data.
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("kayir_2026_ai_use")
# Python
pip install irw
import irw
df = irw.fetch("kayir_2026_ai_use")
@misc{https://doi.org/10.7910/dvn/krwi6e,
doi = {10.7910/DVN/KRWI6E},
url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/KRWI6E},
author = {KAYIR, GÖKHAN},
keywords = {Social Sciences},
title = {Validation of the Turkish AI Specific Teacher Attitude Questionnaire with Measurement Invariance across Gender and Teaching Level},
publisher = {Harvard Dataverse},
year = {2026}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_6 v5.11 |
| Redivis dataset DOI | 10.57761/qahq-j855 |
| Manifest pin for this IRW version | v5.11 |
| Metadata source | irw_meta v44.0 |