Development and validation of a short AI literacy test (AILIT-S) for university students
Fostering AI literacy is an important goal in higher education in many disciplines. Assessing AI literacy can inform researchers and educators on current AI literacy levels and provide insights into the effectiveness of learning and teaching in the field of AI. It can also inform decision-makers and policymakers about the successes and gaps with respect to AI literacy within certain institutions, populations, or countries, for example. However, most of the available AI literacy tests are quite long and time-consuming. A short test of AI literacy would instead enable efficient measurement and facilitate better research and understanding. In this study, we develop and validate a short version of an existing validated AI literacy test. Based on a sample of 1,465 university students across three Western countries (Germany, UK, US), we select a subset of items according to content validity, coverage of different difficulty levels, and ability to discriminate between participants. The resulting short version, AILIT-S, consists of 10 items and can be used to assess AI literacy in under 5 minutes. While the shortened test is less reliable than the long version, it maintains high construct validity and has high congruent validity. We offer recommendations for researchers and practitioners on when to use the long or short version. • Developed a 10-item AI literacy test based on a validated 28-item test with data from 1,465 university students across Germany, the UK, and the US • Demonstrates reliability and validity of the short version of the test • AILIT-S enables efficient assessment of AI literacy at the group level in under 5 minutes but is not intended for individual assessment
Authors
- Marie Hornberger
- Arne Bewersdorff (ORCID: https://orcid.org/0000-0002-9725-268X)
- Claudia Nerdel (ORCID: https://orcid.org/0000-0003-1170-8875)
- Daniel Schiff (ORCID: https://orcid.org/0000-0002-4376-7303)
Institutions
- Purdue University West Lafayette (US)
- Technical University of Munich (DE)
Publication Details
- Journal
- Computers in Human Behavior Artificial Humans
- Published
- 2025-06-16
- DOI
- https://doi.org/10.1016/j.chbah.2025.100176
- Citations
- 5
- Primary Topic
- Grit, Self-Efficacy, and Motivation
- Type
- article
- Field-Weighted Citation Impact
- 12.19
Funders
- Bundesministerium für Bildung und Forschung