Students' AI Literacy Study Identifies Correlates and Educational Opportunities

A peer-reviewed article titled "Students' Artificial Intelligence (AI) literacy: An exploratory study" by Mor Deshen and Noa Aharony appears in the Journal of Librarianship and Information Science, as listed on the publisher site and summarized by infoDOCKET. According to the article abstract shared on infoDOCKET, the study used a quantitative online questionnaire completed by 190 higher-education students. The measures reported in the abstract include an AI literacy scale, the personality trait openness to experience, cognitive appraisals of threat and challenge, and four AIDUA subscales: social-influence, hedonic-motivation, willingness to accept AI usage, and positive emotions toward AI. The abstract reports positive correlations between AI literacy and openness, challenge appraisal, social-influence, hedonic-motivation, willingness to use, positive emotions toward AI, and Generative AI (GenAI) usage.

The article abstract, as reproduced on infoDOCKET and listed on the journal site, identifies the study design as quantitative and cross-sectional with self-reported measures via an online questionnaire. The abstract names established validated questionnaires for the constructs but does not include full psychometric tables in the summary; readers should consult the full SAGE article for instrument reliability, exact item counts, and statistical models used.

Industry observers note that survey-based studies linking personality traits, affective attitudes, and technology acceptance to domain literacy are common in digital-literacy research. In that pattern, finding positive associations between openness to experience, affective motivators (hedonic-motivation, positive emotions), social influence, and actual tool usage is consistent with prior work on technology adoption and digital skill acquisition. For practitioners designing curricula, such patterns imply that addressing attitudes and exposure can be relevant levers when aiming to raise measurable AI literacy across cohorts.

Editorial analysis: The study sits in a growing corpus examining how exposure to GenAI tools and individual differences relate to measurable AI knowledge and literacy. For higher-education stakeholders and learning-science researchers, the reported sample-size and variable set make this a useful exploratory data point, but it remains one study with a modest sample and a cross-sectional design. Replication with larger, diverse samples and transparent psychometrics will be necessary to assess generalizability.

Observers should watch for the full article's methods and results sections on the SAGE site for:

• •detailed reliability and validity statistics for the AI literacy scale
• •the statistical controls used when reporting correlations
• •any subgroup analyses by gender and age that the abstract flags. Replication studies that move beyond self-report and cross-sectional designs would better establish causal links between GenAI exposure, attitudes, and literacy