Oliveira, English, Ryan, Misiejuk, dal Ponte, Lopez-Pernas & Saqr (2026) — arXiv preprint.
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.
Rasch analysis of n=158 GenAI-literacy self-assessments reveals students show an inverted skill profile (mastering creation before conceptual foundations), weak correlation with academics (r=0.188); a skill bypass gives fragile fluency where prompting self-efficacy masks low AI-mechanics literacy, arguing against one-size-fits-all curricula.
This work connects to core wiki themes: ai-literacy higher-ed over-reliance generative-ai faculty-development. It highlights how generative-AI tooling is reshaping both what learners do and how educators structure support, reinforcing the need for design that preserves authentic engagement rather than enabling shallow bypass.
Related Pages
- ai-literacy — Understanding and evaluating AI.
- higher-ed — Postsecondary education context.
- over-reliance — Risks of depending on AI.
- generative-ai — LLM-based educational tools.
- faculty-development — Teacher/professional training.