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The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Developing and validating measures for AI literacy tests: From self-reported to objective measures

Chiu, Thomas K.F., Chen, Yifan, Yau, King Woon, Chai, Ching-sing, Meng, Helen, King, Irwin, Wong, Savio, Yam, Yeung·2024·Computers and Education: Artificial Intelligence
DOI 10.1016/j.caeai.2024.100282
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Abstract

The majority of AI literacy studies have designed and developed self-reported questionnaires to assess AI learning and understanding. These studies assessed students' perceived AI capability rather than AI literacy because self-perceptions are seldom an accurate account of true measures. International assessment programs that use objective measures to assess science, mathematical, digital, and computational literacy back up this argument. Furthermore, because AI education research is still in its infancy, the current definition of AI literacy in the literature may not meet the needs of young students. Therefore, this study aims to develop and validate an AI literacy test for school students within the interdisciplinary project known as AI4future. Engineering and education researchers created and selected 25 multiple-choice questions to accomplish this goal, and school teachers validated them while developing an AI curriculum for middle schools. 2390 students in grades 7 to 9 took the test. We used a Rasch model to investigate the discrimination, reliability, and validity of the items. The results showed that the model met the unidimensionality assumption and demonstrated a set of reliable and valid items. They indicate the quality of the test items. The test enables AI education researchers and practitioners to appropriately evaluate their AI-related education interventions.

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Citations

Cites

Research works printed but not linked: 19

  1. Breazeal, 2023, Day of AI: Innovating pedagogical practices to bring AI literacy to classrooms at scale, Vol. 190
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  7. Knoth, 2024, Developing a holistic AI literacy assessment matrix–Bridging generic, domain-specific, and ethical competencies, Computers and Education Open, 6, 10.1016/j.caeo.2024.100177 · DOI
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  9. Mertala, 2022, Finnish 5th and 6th grade students' pre-instructional conceptions of artificial intelligence (AI) and their implications for AI literacy education, Computers in Education: Artificial Intelligence, 3
  10. Oo, 2021, Information and communication technology literacy, knowledge and readiness for electronic medical record system adoption among health professionals in a tertiary hospital, Myanmar: A cross-sectional study, PLoS One, 16, 10.1371/journal.pone.0253691 · DOI
  11. Ow-Yeong, 2023, Learning data science in elementary school mathematics: A comparative curriculum analysis, International Journal of STEM Education, 10, 8, 10.1186/s40594-023-00397-9 · DOI
  12. Sanusi, 2023, Developing middle school students' understanding of machine learning in an African school, Computers in Education: Artificial Intelligence, 5
  13. Stemler, 2021, Rasch measurement v. Item response theory: Knowing when to cross the line, Practical Assessment, Research & Evaluation, 26, 11
  14. Touretzky, 2023, Machine learning and the five big ideas in AI, International Journal of Artificial Intelligence in Education, 33, 233, 10.1007/s40593-022-00314-1 · DOI
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  17. Weber, 2023, Toward an objective measurement of AI literacy, Vol. 60
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Metadata

Authors
Chiu, T. K. F., Chen, Y., Yau, K. W., Chai, C.-S., Meng, H., King, I., Wong, S., & Yam, Y.
Venue
Computers and Education: Artificial Intelligence
Publisher
Elsevier BV
Published
2024-12
Volume
7
Pages
100282
ISSN
2666-920X
DOI
10.1016/j.caeai.2024.100282
Cited by
41
References
9
Language
en
Licence

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