Generative AI Literacy of Incoming American College Students: Assessing the Need for Formal AI Literacy Training at Two Institutions

Sara J. Finney, Juste Mehou, Stuart A. Miller, Rachel Whitman Rotch, & Jaime Miller   |    Volume 21 Issue 3  |   

As artificial intelligence (AI) becomes embedded in education and the workforce, higher education faces growing pressure to ensure students are AI-literate, which necessitates high-quality measures of AI literacy and baseline assessment of AI literacy. We investigated the generative AI literacy of incoming college students from James Madison University (JMU) (n = 3037) and Auburn University (n = 3709) to (1) extend the evaluation of the Generative AI Literacy Assessment Test (GLAT), (2) assess baseline generative AI literacy prior to formal university training, and (3) explore links between generative AI literacy and student characteristics. The GLAT had adequate psychometric properties supporting its use. AI literacy was low for both institutions (63% versus 54% correct for Auburn and JMU, respectively) with negligible differences across student groups (e.g., major), underscoring the need for broad educational programming. Results added to literature on students’ AI literacy and offered a method to assess curriculum needs for 21st-century competencies.

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