The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy
For higher education institutions designing GenAI curricula, this paper provides empirical evidence that current one-size-fits-all approaches are inadequate, advocating for diagnostic-driven modular interventions.
This study challenges the assumption of linear GenAI literacy progression by analyzing self-assessment data from 158 university students and staff using Rasch measurement theory. Results show students often master high-level creation tasks before foundational understanding, with weak correlation between student and academic skill difficulty (r = 0.188), indicating a 'skill bypass' that masks low AI literacy.
Higher education institutions are increasingly expected to ensure that both students and staff develop Generative AI (GenAI) literacies. In response, they are introducing professional development programs and embedding GenAI skills within student curricula. However, current educational frameworks typically assume a linear progression of GenAI literacy, implying that foundational technical understanding must precede creative application. This paper challenges such an assumption through a psychometric analysis of a taxonomy-based self-assessment instrument (n = 158). We applied Rasch measurement theory and Guttman ordering to map the latent perceived order of difficulty of GenAI skills across students, academics, and professional staff. Results reveal a fundamental divergence in perceived competence profiles: while academics follow a more traditional linear path, students exhibit an "inverted" profile, frequently mastering high-level creation tasks before acquiring foundational conceptual understanding. Furthermore, the correlation of skill difficulty between students and academics was weak (r = 0.188). We argue that this "skill bypass" creates a fragile sense of fluency, where high self-efficacy in prompting masks low literacy in AI mechanics. These findings challenge the "one-size-fits-all" curricula and provide the empirical basis for diagnostic-driven, modular interventions that foster genuine human-AI synergy.