# The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy

arXiv: https://arxiv.org/abs/2607.05411 · HTML: https://mlsanity.com/paper/2607.05411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024)
- [Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Study](https://mlsanity.com/paper/2404.13414/) (2024)
- [Generative AI Literacy: Twelve Defining Competencies](https://mlsanity.com/paper/2412.12107/) (2024)
- [GLAT: The Generative AI Literacy Assessment Test](https://mlsanity.com/paper/2411.00283/) (2024)
- [Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming](https://mlsanity.com/paper/2604.27134/) (2026)

## Abstract (arXiv)

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.