pinho_2026_genai_task_use

816 responses from 204 respondents to 4 items.

One of 11 tables from the same source; this one measures Self-reported task-related generative AI use. The others are listed under Related tables.

About this table

DescriptionSelf-reported task-related generative AI use, 4 items, 1-7, 204 students at Portuguese higher-education institutions (European Portuguese), 2025-2026; cov_gender, cov_age, cov_study_year, cov_field, cov_prior_ai_experience, cov_ai_use_frequency.
ReferencePinho, C. S. B. (2026). Generative AI Use, AI Literacy and Self-Efficacy among Portuguese Higher-Education Students [Data set]. figshare. https://doi.org/10.6084/m9.figshare.33296244.v1
LicenceCC BY 4.0
Source datahttps://figshare.com/articles/dataset/33296244

Size and shape

Responses816
Respondents204
Items4
Response categories7
Responses per respondent4
Responses per item204
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational, General/non-specific
construct typeBehavioral
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)por
construct nameSelf-reported task-related generative AI use

Codebook

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.

ColumnWhat it isSource column
IRW standard columns
idIRW 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
itemIRW standard Item identifier.not traced
respIRW standard Response value.not traced
Respondent covariates
cov_ageCovariate not traced
cov_ai_use_frequencyCovariate not traced
cov_fieldCovariate not traced
cov_genderCovariate not traced
cov_prior_ai_experienceCovariate not traced
cov_study_yearCovariate not traced

What item codes and covariate values stand for is in the codebook released with the source data.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

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("pinho_2026_genai_task_use")
# Python
pip install irw

import irw
df = irw.fetch("pinho_2026_genai_task_use")

How to cite

@misc{https://doi.org/10.6084/m9.figshare.33296244.v1,
  doi = {10.6084/M9.FIGSHARE.33296244.V1},
  url = {https://figshare.com/articles/dataset/Generative_AI_Use_AI_Literacy_and_Self-Efficacy_among_Portuguese_Higher-Education_Students_N_204_/33296244/1},
  author = {Pinho, claudia Sofia Borges},
  keywords = {Information systems education, Human-centred computing not elsewhere classified},
  title = {Generative AI Use, AI Literacy and Self-Efficacy among Portuguese Higher-Education Students (N = 204)},
  publisher = {figshare},
  year = {2026},
  copyright = {Creative Commons Attribution 4.0 International}
}

Version and provenance

IRW versionv607
Redivis datasetitem_response_warehouse_5 v10.0
Redivis dataset DOI10.57761/45xf-md22
Manifest pin for this IRW versionv9.0
Metadata sourceirw_meta v47.0