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

Jul 16, 2026

Upload by Ezra

Generative AI literacy in higher education is increasingly required for both students and staff, yet prevailing models often assume linear development where foundational technical understanding comes before creative and ethical application. This preprint uses psychometric methods—Rasch measurement theory and Guttman ordering—to analyze a taxonomy-based self-assessment (n=158) and map perceived difficulty ordering across students, academics, and professional staff. Results show divergent competence profiles and a weak skill-difficulty correlation (r=0.188).

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

Introduction

Background and policy context

Research gap and motivation

Framing learning sequences

arXiv :2607 .05411v1 [ cs .CY] 10 Jun 2026 The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy Eduardo Oliveira 1 *, Narelle English 1, Tracii Ryan 1, Kamila Misiejuk2, Cory dal Ponte 1, Sonsoles López-Pernas3, and Mohammed Saqr3 1 University of Melbourne, Australia 2 FernUniversität, Germany 3 University of Eastern Finland, Finland *Corresponding author: Eduardo Oliveira ([eduardo.oliveira@unimelb.edu.au](eduardo.oliveira@unimelb.edu.au)) This is a Preprint. This manuscript is currently under review and has not yet been peer reviewed or accepted for publication. 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. Implications for practice or policy • Curriculum designers should not assume students learn GenAI skills foundation-first, since this study reveals that many master AI-assisted creation before conceptual, ethical, and evaluative understanding. • Educators could consider treating early student fluency as fragile, embedding evaluative and ethical understanding into AI workflows to counter over-reliance. • Institutional leaders could implement training based on modular pathways differentiated for students, academics, and professional staff, rather than deploying one-size-fits-all approaches. • Researchers should continue modelling AI literacy as a role-dependent, non-linear construct rather than a single uniform developmental ladder. Keywords: AI literacy, generative AI, higher education, psychometrics, Rasch measurement, Guttman ordering Introduction The integration of Generative AI (GenAI) into higher education has shifted from a phase of rapid experimentation to a pressing demand for sustainable, evidence-based and responsible implementation. As GenAI tools become ubiquitous in professional and academic workflows, the primary challenge for institutions is no longer merely providing staff and students with access, but bridging the critical “competency gap” between basic exposure and true fluency. For example, Australia’s higher education regulator, the Tertiary Education Quality and Standards Agency (TEQSA), recently encouraged institutions to take a whole-of-institution approach to developing AI literacy, to ensure ethical and appropriate usage of these tools and enable students to meet the learning outcomes of their program. However, as recent frameworks emphasise, navigating this landscape requires a comprehensive, multidimensional literacy that integrates theoretical knowledge, practical application, and ethical reflection (Bozkurt, 2024) . Such fluency is a prerequisite for

FAQs

What assumption about GenAI literacy does the paper challenge?

The paper challenges the assumption that GenAI literacy develops linearly, with foundational technical understanding always preceding creative application and ethical evaluation.

How did the authors measure and compare AI literacy pathways?

They used a taxonomy-based self-assessment instrument (n=158) and applied Rasch measurement theory and Guttman ordering to map the latent perceived order of difficulty.

What are the main findings about students versus academics?

Academics show a more traditional linear pathway, while students show an “inverted” profile, often mastering high-level creation tasks before foundational conceptual understanding. The correlation in perceived skill difficulty between students and academics is weak (r=0.188).

What practical implications does the study suggest?

Curricula should not be built on a foundation-first model for all learners; early fluency may be fragile. The paper supports diagnostic-driven, modular interventions tailored to different roles rather than one-size-fits-all approaches.

Jul 16, 2026

Upload by Ezra