Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model
Abstract AI literacy in education is a multi-dimensional concept involving the understanding of AI technologies, critical appraisal of AI technologies, practical application, and AI ethics. Through the Rasch Model, this duplication study validated and revised the scales used in previous studies to m...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2025-08-01
|
| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-025-05670-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849237639035617280 |
|---|---|
| author | Ying Dong Wei Xu Jiayan Huang Kerr Yann |
| author_facet | Ying Dong Wei Xu Jiayan Huang Kerr Yann |
| author_sort | Ying Dong |
| collection | DOAJ |
| description | Abstract AI literacy in education is a multi-dimensional concept involving the understanding of AI technologies, critical appraisal of AI technologies, practical application, and AI ethics. Through the Rasch Model, this duplication study validated and revised the scales used in previous studies to measure AI literacy in education. Based on the literature, we developed a scale to measure AI literacy in education, including technological understanding, critical appraisal, practical application, and AI ethics, whose validity and reliability were examined using the Rasch Model. Based on the results of validity, we removed items whose infit/outfit mean square (MNSQ) or standardized mean square (ZSTD) values fell outside the acceptable range (0.6–1.4 for MNSQ; −2 to 2 for ZSTD). This enhances the validity and provides reliable results, enabling the scale to measure AI literacy in education effectively. Future research can conduct an in-depth examination of the Rasch Model for the construction of AI literacy in education, validating its cross-disciplinary generalizability, exploring cultural and demographic factors, and enhancing the generalizability and precision of the scale. |
| format | Article |
| id | doaj-art-cee06c2ed55a4beb8f7dc3517366b95f |
| institution | Kabale University |
| issn | 2662-9992 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj-art-cee06c2ed55a4beb8f7dc3517366b95f2025-08-20T04:01:53ZengSpringer NatureHumanities & Social Sciences Communications2662-99922025-08-0112111310.1057/s41599-025-05670-6Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch ModelYing Dong0Wei Xu1Jiayan Huang2Kerr Yann3Institute of Vocational Education, Hebei Normal University of Science and TechnologyFaculty of Humanities and Social Sciences, City University of MacauFaculty of Humanities and Social Sciences, City University of MacauMacau Millennium CollegeAbstract AI literacy in education is a multi-dimensional concept involving the understanding of AI technologies, critical appraisal of AI technologies, practical application, and AI ethics. Through the Rasch Model, this duplication study validated and revised the scales used in previous studies to measure AI literacy in education. Based on the literature, we developed a scale to measure AI literacy in education, including technological understanding, critical appraisal, practical application, and AI ethics, whose validity and reliability were examined using the Rasch Model. Based on the results of validity, we removed items whose infit/outfit mean square (MNSQ) or standardized mean square (ZSTD) values fell outside the acceptable range (0.6–1.4 for MNSQ; −2 to 2 for ZSTD). This enhances the validity and provides reliable results, enabling the scale to measure AI literacy in education effectively. Future research can conduct an in-depth examination of the Rasch Model for the construction of AI literacy in education, validating its cross-disciplinary generalizability, exploring cultural and demographic factors, and enhancing the generalizability and precision of the scale.https://doi.org/10.1057/s41599-025-05670-6 |
| spellingShingle | Ying Dong Wei Xu Jiayan Huang Kerr Yann Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model Humanities & Social Sciences Communications |
| title | Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model |
| title_full | Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model |
| title_fullStr | Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model |
| title_full_unstemmed | Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model |
| title_short | Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model |
| title_sort | validating and refining a multi dimensional scale for measuring ai literacy in education using the rasch model |
| url | https://doi.org/10.1057/s41599-025-05670-6 |
| work_keys_str_mv | AT yingdong validatingandrefiningamultidimensionalscaleformeasuringailiteracyineducationusingtheraschmodel AT weixu validatingandrefiningamultidimensionalscaleformeasuringailiteracyineducationusingtheraschmodel AT jiayanhuang validatingandrefiningamultidimensionalscaleformeasuringailiteracyineducationusingtheraschmodel AT kerryann validatingandrefiningamultidimensionalscaleformeasuringailiteracyineducationusingtheraschmodel |